Skip to content

GitLab

Unverified Commit 7c9fbcf8 authored by PabloAgustin's avatar PabloAgustin Committed by GitHub
Browse files

New healthcare benchmark: careqa (#2714) 



* New healthcare benchmark: careqa

* LAUNCH_MN5_ACC <python main.py --config config/mn5.yml --models Llama-3.2-1B-Instruct --tasks careqa_open --num_fewshot 0>

* Add fixes, READMES, and remove task_list.txt

* pre-commit passed, add formatting updates; add nanmean agg_metric

* Fix import error.

* Wrapped imports in try excepts

* Wrapped imports in try excepts; also metrics to catch bert_score import error

* Try except to catch ImportErrors as well

* use np.nan

* pre-commit

---------
Co-authored-by: default avatarPabloAgustin <pablo.martin@bsc.es>
Co-authored-by: default avatarBaber <baber@hey.com>
parent 2c8ffb80
Hide whitespace changes
Inline Side-by-side
Showing with 3094 additions and 136 deletions
lm_eval/api/metrics.py
View file @ 7c9fbcf8
......@@ -21,6 +21,13 @@ def bypass_agg(arr):
return 999
@register_aggregation("nanmean")
def nanmean(arr):
if len(arr) == 0 or all(np.isnan(arr)):
return np.nan
return np.nanmean(arr)
@register_aggregation("mean")
def mean(arr):
return sum(arr) / len(arr)
......@@ -498,6 +505,7 @@ def stderr_for_metric(metric, bootstrap_iters: int):
bleu,
chrf,
ter,
nanmean,
]
if metric in bootstrappable:
......
lm_eval/tasks/README.md
View file @ 7c9fbcf8
......@@ -5,141 +5,149 @@
For more information, including a full list of task names and their precise meanings or sources, follow the links provided to the individual README.md files for each subfolder.
| Task Family | Description | Language(s) |
|--------------------------------------------------------------------------|-------------|-------------------------------------------------------------------------------------------------------------------------------|
| [aclue](aclue/README.md) | Tasks focusing on ancient Chinese language understanding and cultural aspects. | Ancient Chinese |
| Task Family | Description | Language(s) |
|--------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
| [aclue](aclue/README.md) | Tasks focusing on ancient Chinese language understanding and cultural aspects. | Ancient Chinese |
| [aexams](aexams/README.md) | Tasks in Arabic related to various academic exams covering a range of subjects. | Arabic |
| [agieval](agieval/README.md) | Tasks involving historical data or questions related to history and historical texts. | English, Chinese |
| [anli](anli/README.md) | Adversarial natural language inference tasks designed to test model robustness. | English |
| [arabic_leaderboard_complete](arabic_leaderboard_complete/README.md) | A full version of the tasks in the Open Arabic LLM Leaderboard, focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabic_leaderboard_light](arabic_leaderboard_light/README.md) | A light version of the tasks in the Open Arabic LLM Leaderboard (i.e., 10% samples of the test set in the original benchmarks), focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabicmmlu](arabicmmlu/README.md) | Localized Arabic version of MMLU with multiple-choice questions from 40 subjects. | Arabic |
| [AraDICE](aradice/README.md) | A collection of multiple tasks carefully designed to evaluate dialectal and cultural capabilities in large language models (LLMs). | Arabic |
| [arc](arc/README.md) | Tasks involving complex reasoning over a diverse set of questions. | English |
| [arithmetic](arithmetic/README.md) | Tasks involving numerical computations and arithmetic reasoning. | English |
| [asdiv](asdiv/README.md) | Tasks involving arithmetic and mathematical reasoning challenges. | English |
| [babi](babi/README.md) | Tasks designed as question and answering challenges based on simulated stories. | English |
| [basque_bench](basque_bench/README.md) | Collection of tasks in Basque encompassing various evaluation areas. | Basque |
| [basqueglue](basqueglue/README.md) | Tasks designed to evaluate language understanding in Basque language. | Basque |
| [bbh](bbh/README.md) | Tasks focused on deep semantic understanding through hypothesization and reasoning. | English, German |
| [belebele](belebele/README.md) | Language understanding tasks in a variety of languages and scripts. | Multiple (122 languages) |
| benchmarks | General benchmarking tasks that test a wide range of language understanding capabilities. | |
| [bertaqa](bertaqa/README.md) | Local Basque cultural trivia QA tests in English and Basque languages. | English, Basque, Basque (MT) |
| [bigbench](bigbench/README.md) | Broad tasks from the BIG-bench benchmark designed to push the boundaries of large models. | Multiple |
| [blimp](blimp/README.md) | Tasks testing grammatical phenomena to evaluate language model's linguistic capabilities. | English |
| [catalan_bench](catalan_bench/README.md) | Collection of tasks in Catalan encompassing various evaluation areas. | Catalan |
| [ceval](ceval/README.md) | Tasks that evaluate language understanding and reasoning in an educational context. | Chinese |
| [cmmlu](cmmlu/README.md) | Multi-subject multiple choice question tasks for comprehensive academic assessment. | Chinese |
| code_x_glue | Tasks that involve understanding and generating code across multiple programming languages. | Go, Java, JS, PHP, Python, Ruby |
| [commonsense_qa](commonsense_qa/README.md) | CommonsenseQA, a multiple-choice QA dataset for measuring commonsense knowledge. | English |
| [copal_id](copal_id/README.md) | Indonesian causal commonsense reasoning dataset that captures local nuances. | Indonesian |
| [coqa](coqa/README.md) | Conversational question answering tasks to test dialog understanding. | English |
| [crows_pairs](crows_pairs/README.md) | Tasks designed to test model biases in various sociodemographic groups. | English, French |
| csatqa | Tasks related to SAT and other standardized testing questions for academic assessment. | Korean |
| [drop](drop/README.md) | Tasks requiring numerical reasoning, reading comprehension, and question answering. | English |
| [eq_bench](eq_bench/README.md) | Tasks focused on equality and ethics in question answering and decision-making. | English |
| [eus_exams](eus_exams/README.md) | Tasks based on various professional and academic exams in the Basque language. | Basque |
| [eus_proficiency](eus_proficiency/README.md) | Tasks designed to test proficiency in the Basque language across various topics. | Basque |
| [eus_reading](eus_reading/README.md) | Reading comprehension tasks specifically designed for the Basque language. | Basque |
| [eus_trivia](eus_trivia/README.md) | Trivia and knowledge testing tasks in the Basque language. | Basque |
| [evalita-LLM](evalita-LLM/README.md) | A native Italian benchmark with diverse tasks formats and multiple prompts. | Italian |
| [fda](fda/README.md) | Tasks for extracting key-value pairs from FDA documents to test information extraction. | English |
| [fld](fld/README.md) | Tasks involving free-form and directed dialogue understanding. | English |
| [french_bench](french_bench/README.md) | Set of tasks designed to assess language model performance in French. | French |
| [galician_bench](galician_bench/README.md) | Collection of tasks in Galician encompassing various evaluation areas. | Galician |
| [global_mmlu](global_mmlu/README.md) | Collection of culturally sensitive and culturally agnostic MMLU tasks in 15 languages with human translations or post-edits. | Multiple (15 languages) |
| [glue](glue/README.md) | General Language Understanding Evaluation benchmark to test broad language abilities. | English |
| [gpqa](gpqa/README.md) | Tasks designed for general public question answering and knowledge verification. | English |
| [gsm8k](gsm8k/README.md) | A benchmark of grade school math problems aimed at evaluating reasoning capabilities. | English |
| [agieval](agieval/README.md) | Tasks involving historical data or questions related to history and historical texts. | English, Chinese |
| [anli](anli/README.md) | Adversarial natural language inference tasks designed to test model robustness. | English |
| [arabic_leaderboard_complete](arabic_leaderboard_complete/README.md) | A full version of the tasks in the Open Arabic LLM Leaderboard, focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabic_leaderboard_light](arabic_leaderboard_light/README.md) | A light version of the tasks in the Open Arabic LLM Leaderboard (i.e., 10% samples of the test set in the original benchmarks), focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabicmmlu](arabicmmlu/README.md) | Localized Arabic version of MMLU with multiple-choice questions from 40 subjects. | Arabic |
| [AraDICE](aradice/README.md) | A collection of multiple tasks carefully designed to evaluate dialectal and cultural capabilities in large language models (LLMs). | Arabic |
| [arc](arc/README.md) | Tasks involving complex reasoning over a diverse set of questions. | English |
| [arithmetic](arithmetic/README.md) | Tasks involving numerical computations and arithmetic reasoning. | English |
| [asdiv](asdiv/README.md) | Tasks involving arithmetic and mathematical reasoning challenges. | English |
| [babi](babi/README.md) | Tasks designed as question and answering challenges based on simulated stories. | English |
| [basque_bench](basque_bench/README.md) | Collection of tasks in Basque encompassing various evaluation areas. | Basque |
| [basqueglue](basqueglue/README.md) | Tasks designed to evaluate language understanding in Basque language. | Basque |
| [bbh](bbh/README.md) | Tasks focused on deep semantic understanding through hypothesization and reasoning. | English, German |
| [belebele](belebele/README.md) | Language understanding tasks in a variety of languages and scripts. | Multiple (122 languages) |
| benchmarks | General benchmarking tasks that test a wide range of language understanding capabilities. | |
| [bertaqa](bertaqa/README.md) | Local Basque cultural trivia QA tests in English and Basque languages. | English, Basque, Basque (MT) |
| [bigbench](bigbench/README.md) | Broad tasks from the BIG-bench benchmark designed to push the boundaries of large models. | Multiple |
| [blimp](blimp/README.md) | Tasks testing grammatical phenomena to evaluate language model's linguistic capabilities. | English |
| [careqa](careqa/README.md) | Multiple choice and open-ended medical question answering based on the Spanish Specialised Healthcare Training (MIR) exams. | English, Spanish |
| [catalan_bench](catalan_bench/README.md) | Collection of tasks in Catalan encompassing various evaluation areas. | Catalan |
| [ceval](ceval/README.md) | Tasks that evaluate language understanding and reasoning in an educational context. | Chinese |
| [cmmlu](cmmlu/README.md) | Multi-subject multiple choice question tasks for comprehensive academic assessment. | Chinese |
| code_x_glue | Tasks that involve understanding and generating code across multiple programming languages. | Go, Java, JS, PHP, Python, Ruby |
| [commonsense_qa](commonsense_qa/README.md) | CommonsenseQA, a multiple-choice QA dataset for measuring commonsense knowledge. | English |
| [copal_id](copal_id/README.md) United States | Indonesian causal commonsense reasoning dataset that captures local nuances. | Indonesian |
| [coqa](coqa/README.md) | Conversational question answering tasks to test dialog understanding. | English |
| [crows_pairs](crows_pairs/README.md) | Tasks designed to test model biases in various sociodemographic groups. | English, French |
| csatqa | Tasks related to SAT and other standardized testing questions for academic assessment. | Korean |
| [drop](drop/README.md) | Tasks requiring numerical reasoning, reading comprehension, and question answering. | English |
| [eq_bench](eq_bench/README.md) | Tasks focused on equality and ethics in question answering and decision-making. | English |
| [eus_exams](eus_exams/README.md) | Tasks based on various professional and academic exams in the Basque language. | Basque |
| [eus_proficiency](eus_proficiency/README.md) | Tasks designed to test proficiency in the Basque language across various topics. | Basque |
| [eus_reading](eus_reading/README.md) | Reading comprehension tasks specifically designed for the Basque language. | Basque |
| [eus_trivia](eus_trivia/README.md) | Trivia and knowledge testing tasks in the Basque language. | Basque |
| [evalita-LLM](evalita-LLM/README.md) | A native Italian benchmark with diverse tasks formats and multiple prompts. | Italian |
| [fda](fda/README.md) | Tasks for extracting key-value pairs from FDA documents to test information extraction. | English |
| [fld](fld/README.md) | Tasks involving free-form and directed dialogue understanding. | English |
| [french_bench](french_bench/README.md) | Set of tasks designed to assess language model performance in French. | French |
| [galician_bench](galician_bench/README.md) | Collection of tasks in Galician encompassing various evaluation areas. | Galician |
| [global_mmlu](global_mmlu/README.md) | Collection of culturally sensitive and culturally agnostic MMLU tasks in 15 languages with human translations or post-edits. | Multiple (15 languages) |
| [glue](glue/README.md) | General Language Understanding Evaluation benchmark to test broad language abilities. | English |
| [gpqa](gpqa/README.md) | Tasks designed for general public question answering and knowledge verification. | English |
| [gsm8k](gsm8k/README.md) | A benchmark of grade school math problems aimed at evaluating reasoning capabilities. | English |
| [groundcocoa](groundcocoa/README.md) | A benchmark evaluating the conditional and compositional reasoning of language models using a grounding task. | English |
| [haerae](haerae/README.md) | Tasks focused on assessing detailed factual and historical knowledge. | Korean |
| [headqa](headqa/README.md) | A high-level education-based question answering dataset to test specialized knowledge. | Spanish, English |
| [hellaswag](hellaswag/README.md) | Tasks to predict the ending of stories or scenarios, testing comprehension and creativity. | English |
| [hendrycks_ethics](hendrycks_ethics/README.md) | Tasks designed to evaluate the ethical reasoning capabilities of models. | English |
| [hendrycks_math](hendrycks_math/README.md) | Mathematical problem-solving tasks to test numerical reasoning and problem-solving. | English |
| [histoires_morales](histoires_morales/README.md) | A dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | French (Some MT) |
| [hrm8k](hrm8k/README.md) | A challenging bilingual math reasoning benchmark for Korean and English. | Korean (Some MT), English (Some MT) |
| [humaneval](humaneval/README.md) | Code generation task that measure functional correctness for synthesizing programs from docstrings. | Python |
| [ifeval](ifeval/README.md) | Interactive fiction evaluation tasks for narrative understanding and reasoning. | English |
| [inverse_scaling](inverse_scaling/README.md) | Multiple-choice tasks from the Inverse Scaling Prize, designed to find settings where larger language models perform worse. | English |
| [japanese_leaderboard](japanese_leaderboard/README.md) | Japanese language understanding tasks to benchmark model performance on various linguistic aspects. | Japanese |
| [kbl](kbl/README.md) | Korean Benchmark for Legal Language Understanding. | Korean |
| [kmmlu](kmmlu/README.md) | Knowledge-based multi-subject multiple choice questions for academic evaluation. | Korean |
| [kobest](kobest/README.md) | A collection of tasks designed to evaluate understanding in Korean language. | Korean |
| [kormedmcqa](kormedmcqa/README.md) | Medical question answering tasks in Korean to test specialized domain knowledge. | Korean |
| [lambada](lambada/README.md) | Tasks designed to predict the endings of text passages, testing language prediction skills. | English |
| [lambada_cloze](lambada_cloze/README.md) | Cloze-style LAMBADA dataset. | English |
| [lambada_multilingual](lambada_multilingual/README.md) | Multilingual LAMBADA dataset. This is a legacy version of the multilingual dataset, and users should instead use `lambada_multilingual_stablelm`. | German, English, Spanish, French, Italian |
| [lambada_multilingual_stablelm](lambada_multilingual_stablelm/README.md) | Multilingual LAMBADA dataset. Users should prefer evaluating on this version of the multilingual dataset instead of on `lambada_multilingual`. | German, English, Spanish, French, Italian, Dutch, Portuguese |
| [leaderboard](leaderboard/README.md) | Task group used by Hugging Face's [Open LLM Leaderboard v2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard). Those tasks are static and will not change through time | English |
| [lingoly](lingoly/README.md) | Challenging logical reasoning benchmark in low-resource languages with controls for memorization | English, Multilingual |
| [logiqa](logiqa/README.md) | Logical reasoning tasks requiring advanced inference and deduction. | English, Chinese |
| [logiqa2](logiqa2/README.md) | Large-scale logical reasoning dataset adapted from the Chinese Civil Service Examination. | English, Chinese |
| [mathqa](mathqa/README.md) | Question answering tasks involving mathematical reasoning and problem-solving. | English |
| [mbpp](mbpp/README.md) | A benchmark designed to measure the ability to synthesize short Python programs from natural language descriptions. | Python |
| [mc_taco](mc_taco/README.md) | Question-answer pairs that require temporal commonsense comprehension. | English |
| [med_concepts_qa](med_concepts_qa/README.md) | Benchmark for evaluating LLMs on their abilities to interpret medical codes and distinguish between medical concept. | English |
| [metabench](metabench/README.md) | Distilled versions of six popular benchmarks which are highly predictive of overall benchmark performance and of a single general ability latent trait. | English |
| medmcqa | Medical multiple choice questions assessing detailed medical knowledge. | English |
| medqa | Multiple choice question answering based on the United States Medical License Exams. | |
| [mgsm](mgsm/README.md) | Benchmark of multilingual grade-school math problems. | Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, Telugu |
| [minerva_math](minerva_math/README.md) | Mathematics-focused tasks requiring numerical reasoning and problem-solving skills. | English |
| [mlqa](mlqa/README.md) | MultiLingual Question Answering benchmark dataset for evaluating cross-lingual question answering performance. | English, Arabic, German, Spanish, Hindi, Vietnamese, Simplified Chinese |
| [mmlu](mmlu/README.md) | Massive Multitask Language Understanding benchmark for broad domain language evaluation. Several variants are supported. | English |
| [mmlu_pro](mmlu_pro/README.md) | A refined set of MMLU, integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. | English |
| [mmlu-pro-plus](mmlu-pro-plus/README.md) | A new test set for evaluating shortcut learning and higher-order reasoning of LLMs. | English |
| [mmlusr](mmlusr/README.md) | Variation of MMLU designed to be more rigorous. | English |
| model_written_evals | Evaluation tasks auto-generated for evaluating a collection of AI Safety concerns. | |
| [moral_stories](moral_stories/README.md) | A crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | English
| [mutual](mutual/README.md) | A retrieval-based dataset for multi-turn dialogue reasoning. | English |
| [nq_open](nq_open/README.md) | Open domain question answering tasks based on the Natural Questions dataset. | English |
| [okapi/arc_multilingual](okapi/arc_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [okapi/hellaswag_multilingual](okapi/hellaswag_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (30 languages) **Machine Translated.** |
| okapi/mmlu_multilingual | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (34 languages) **Machine Translated.** |
| [okapi/truthfulqa_multilingual](okapi/truthfulqa_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [openbookqa](openbookqa/README.md) | Open-book question answering tasks that require external knowledge and reasoning. | English |
| [paloma](paloma/README.md) | Paloma is a comprehensive benchmark designed to evaluate open language models across a wide range of domains, ranging from niche artist communities to mental health forums on Reddit. | English |
| [paws-x](paws-x/README.md) | Paraphrase Adversaries from Word Scrambling, focusing on cross-lingual capabilities. | English, French, Spanish, German, Chinese, Japanese, Korean |
| [pile](pile/README.md) | Open source language modelling data set that consists of 22 smaller, high-quality datasets. | English |
| [pile_10k](pile_10k/README.md) | The first 10K elements of The Pile, useful for debugging models trained on it. | English |
| [piqa](piqa/README.md) | Physical Interaction Question Answering tasks to test physical commonsense reasoning. | English |
| [polemo2](polemo2/README.md) | Sentiment analysis and emotion detection tasks based on Polish language data. | Polish |
| [portuguese_bench](portuguese_bench/README.md) | Collection of tasks in European Portuguese encompassing various evaluation areas. | Portuguese |
| [prost](prost/README.md) | Tasks requiring understanding of professional standards and ethics in various domains. | English |
| [pubmedqa](pubmedqa/README.md) | Question answering tasks based on PubMed research articles for biomedical understanding. | English |
| [qa4mre](qa4mre/README.md) | Question Answering for Machine Reading Evaluation, assessing comprehension and reasoning. | English |
| [qasper](qasper/README.md) | Question Answering dataset based on academic papers, testing in-depth scientific knowledge. | English |
| [race](race/README.md) | Reading comprehension assessment tasks based on English exams in China. | English |
| realtoxicityprompts | Tasks to evaluate language models for generating text with potential toxicity. | |
| [sciq](sciq/README.md) | Science Question Answering tasks to assess understanding of scientific concepts. | English |
| [score](score/README.md) | Systematic consistency and robustness evaluation for LLMs on 3 datasets(MMLU-Pro, Agi Eval and MATH) | English |
| [scrolls](scrolls/README.md) | Tasks that involve long-form reading comprehension across various domains. | English |
| [siqa](siqa/README.md) | Social Interaction Question Answering to evaluate common sense and social reasoning. | English |
| [spanish_bench](spanish_bench/README.md) | Collection of tasks in Spanish encompassing various evaluation areas. | Spanish |
| [squad_completion](squad_completion/README.md) | A variant of the SQuAD question answering task designed for zero-shot evaluation of small LMs. | English |
| [squadv2](squadv2/README.md) | Stanford Question Answering Dataset version 2, a reading comprehension benchmark. | English |
| [storycloze](storycloze/README.md) | Tasks to predict story endings, focusing on narrative logic and coherence. | English |
| [super_glue](super_glue/README.md) | A suite of challenging tasks designed to test a range of language understanding skills. | English |
| [swag](swag/README.md) | Situations With Adversarial Generations, predicting the next event in videos. | English |
| [swde](swde/README.md) | Information extraction tasks from semi-structured web pages. | English |
| [tinyBenchmarks](tinyBenchmarks/README.md) | Evaluation of large language models with fewer examples using tiny versions of popular benchmarks. | English |
| [tmmluplus](tmmluplus/README.md) | An extended set of tasks under the TMMLU framework for broader academic assessments. | Traditional Chinese |
| [toxigen](toxigen/README.md) | Tasks designed to evaluate language models on their propensity to generate toxic content. | English |
| [translation](translation/README.md) | Tasks focused on evaluating the language translation capabilities of models. | Arabic, English, Spanish, Basque, Hindi, Indonesian, Burmese, Russian, Swahili, Telugu, Chinese |
| [triviaqa](triviaqa/README.md) | A large-scale dataset for trivia question answering to test general knowledge. | English |
| [truthfulqa](truthfulqa/README.md) | A QA task aimed at evaluating the truthfulness and factual accuracy of model responses. | English |
| [turkishmmlu](turkishmmlu/README.md) | A multiple-choice QA test modeled after MMLU, written in Turkish based on Turkish high-school level exams. | Turkish |
| [unitxt](unitxt/README.md) | A number of tasks implemented using the unitxt library for flexible, shareable, and reusable data preparation and evaluation for generative AI. | English |
| [unscramble](unscramble/README.md) | Tasks involving the rearrangement of scrambled sentences to test syntactic understanding. | English |
| [webqs](webqs/README.md) | Web-based question answering tasks designed to evaluate internet search and retrieval. | English |
| [wikitext](wikitext/README.md) | Tasks based on text from Wikipedia articles to assess language modeling and generation. | English |
| [winogrande](winogrande/README.md) | A large-scale dataset for coreference resolution, inspired by the Winograd Schema Challenge. | English |
| [wmdp](wmdp/README.md) | A benchmark with the objective of minimizing performance, based on potentially-sensitive multiple-choice knowledge questions. | English |
| [wmt2016](wmt2016/README.md) | Tasks from the WMT 2016 shared task, focusing on translation between multiple languages. | English, Czech, German, Finnish, Russian, Romanian, Turkish |
| [wsc273](wsc273/README.md) | The Winograd Schema Challenge, a test of commonsense reasoning and coreference resolution. | English |
| [xcopa](xcopa/README.md) | Cross-lingual Choice of Plausible Alternatives, testing reasoning in multiple languages. | Estonian, Haitian, Indonesian, Italian, Quechua, Swahili, Tamil, Thai, Turkish, Vietnamese, Chinese |
| [xnli](xnli/README.md) | Cross-Lingual Natural Language Inference to test understanding across different languages. | Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, Chinese |
| [xnli_eu](xnli_eu/README.md) | Cross-lingual Natural Language Inference tasks in Basque. | Basque |
| [xquad](xquad/README.md) | Cross-lingual Question Answering Dataset in multiple languages. | Arabic, German, Greek, English, Spanish, Hindi, Romanian, Russian, Thai, Turkish, Vietnamese, Chinese |
| [xstorycloze](xstorycloze/README.md) | Cross-lingual narrative understanding tasks to predict story endings in multiple languages. | Russian, Simplified Chinese, Spanish, Arabic, Hindi, Indonesian, Telugu, Swahili, Basque, Burmese |
| [xwinograd](xwinograd/README.md) | Cross-lingual Winograd schema tasks for coreference resolution in multiple languages. | English, French, Japanese, Portuguese, Russian, Chinese |
| [haerae](haerae/README.md) | Tasks focused on assessing detailed factual and historical knowledge. | Korean |
| [headqa](headqa/README.md) | A high-level education-based question answering dataset to test specialized knowledge. | Spanish, English |
| [hellaswag](hellaswag/README.md) | Tasks to predict the ending of stories or scenarios, testing comprehension and creativity. | English |
| [hendrycks_ethics](hendrycks_ethics/README.md) | Tasks designed to evaluate the ethical reasoning capabilities of models. | English |
| [hendrycks_math](hendrycks_math/README.md) | Mathematical problem-solving tasks to test numerical reasoning and problem-solving. | English |
| [histoires_morales](histoires_morales/README.md) | A dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | French (Some MT) |
| [hrm8k](hrm8k/README.md) | A challenging bilingual math reasoning benchmark for Korean and English. | Korean (Some MT), English (Some MT) |
| [humaneval](humaneval/README.md) | Code generation task that measure functional correctness for synthesizing programs from docstrings. | Python |
| [ifeval](ifeval/README.md) | Interactive fiction evaluation tasks for narrative understanding and reasoning. | English |
| [inverse_scaling](inverse_scaling/README.md) | Multiple-choice tasks from the Inverse Scaling Prize, designed to find settings where larger language models perform worse. | English |
| [japanese_leaderboard](japanese_leaderboard/README.md) | Japanese language understanding tasks to benchmark model performance on various linguistic aspects. | Japanese |
| [kbl](kbl/README.md) | Korean Benchmark for Legal Language Understanding. | Korean |
| [kmmlu](kmmlu/README.md) | Knowledge-based multi-subject multiple choice questions for academic evaluation. | Korean |
| [kobest](kobest/README.md) | A collection of tasks designed to evaluate understanding in Korean language. | Korean |
| [kormedmcqa](kormedmcqa/README.md) | Medical question answering tasks in Korean to test specialized domain knowledge. | Korean |
| [lambada](lambada/README.md) | Tasks designed to predict the endings of text passages, testing language prediction skills. | English |
| [lambada_cloze](lambada_cloze/README.md) | Cloze-style LAMBADA dataset. | English |
| [lambada_multilingual](lambada_multilingual/README.md) | Multilingual LAMBADA dataset. This is a legacy version of the multilingual dataset, and users should instead use `lambada_multilingual_stablelm`. | German, English, Spanish, French, Italian |
| [lambada_multilingual_stablelm](lambada_multilingual_stablelm/README.md) | Multilingual LAMBADA dataset. Users should prefer evaluating on this version of the multilingual dataset instead of on `lambada_multilingual`. | German, English, Spanish, French, Italian, Dutch, Portuguese |
| [leaderboard](leaderboard/README.md) | Task group used by Hugging Face's [Open LLM Leaderboard v2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard). Those tasks are static and will not change through time | English |
| [lingoly](lingoly/README.md) | Challenging logical reasoning benchmark in low-resource languages with controls for memorization | English, Multilingual |
| [logiqa](logiqa/README.md) | Logical reasoning tasks requiring advanced inference and deduction. | English, Chinese |
| [logiqa2](logiqa2/README.md) | Large-scale logical reasoning dataset adapted from the Chinese Civil Service Examination. | English, Chinese |
| [mathqa](mathqa/README.md) | Question answering tasks involving mathematical reasoning and problem-solving. | English |
| [mbpp](mbpp/README.md) | A benchmark designed to measure the ability to synthesize short Python programs from natural language descriptions. | Python |
| [meddialog](meddialog/README.md) | Medical open-ended QA and Question Entailment stemming from the MedDialog dataset. | English |
| [medtext](medtext/README.md) | Medical open-ended QA from the MedText Clinical Notes dataset. | English |
| [mimic_repsum](mimic_repsum/README.md) | Medical report summarization from the MIMIC-III dataset. | English |
| [mc_taco](mc_taco/README.md) | Question-answer pairs that require temporal commonsense comprehension. | English |
| [med_concepts_qa](med_concepts_qa/README.md) | Benchmark for evaluating LLMs on their abilities to interpret medical codes and distinguish between medical concept. | English |
| [metabench](metabench/README.md) | Distilled versions of six popular benchmarks which are highly predictive of overall benchmark performance and of a single general ability latent trait. | English |
| [mediqa_qa2019](mediqa_qa2019/README.md) | Open-ended healthcare question answering benchmark from the MEDIQA 2019 challenge. | English |
| medmcqa | Medical multiple choice questions assessing detailed medical knowledge. | English |
| medqa | Multiple choice question answering based on the United States Medical License Exams. | |
| [meqsum](meqsum/README.md) | Healtcare Question Entailment benchmark from the MeqSum dataset. | |
| [mgsm](mgsm/README.md) | Benchmark of multilingual grade-school math problems. | Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, Telugu |
| [minerva_math](minerva_math/README.md) | Mathematics-focused tasks requiring numerical reasoning and problem-solving skills. | English |
| [mlqa](mlqa/README.md) | MultiLingual Question Answering benchmark dataset for evaluating cross-lingual question answering performance. | English, Arabic, German, Spanish, Hindi, Vietnamese, Simplified Chinese |
| [mmlu](mmlu/README.md) | Massive Multitask Language Understanding benchmark for broad domain language evaluation. Several variants are supported. | English |
| [mmlu_pro](mmlu_pro/README.md) | A refined set of MMLU, integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. | English |
| [mmlu-pro-plus](mmlu-pro-plus/README.md) | A new test set for evaluating shortcut learning and higher-order reasoning of LLMs. | English |
| [mmlusr](mmlusr/README.md) | Variation of MMLU designed to be more rigorous. | English |
| model_written_evals | Evaluation tasks auto-generated for evaluating a collection of AI Safety concerns. | |
| [moral_stories](moral_stories/README.md) | A crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | English
| [mts_dialog](mts_dialog/README.md) | Open-ended healthcare QA from the MTS-Dialog dataset. | English |
| [mutual](mutual/README.md) | A retrieval-based dataset for multi-turn dialogue reasoning. | English |
| [nq_open](nq_open/README.md) | Open domain question answering tasks based on the Natural Questions dataset. | English |
| [okapi/arc_multilingual](okapi/arc_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [okapi/hellaswag_multilingual](okapi/hellaswag_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (30 languages) **Machine Translated.** |
| okapi/mmlu_multilingual | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (34 languages) **Machine Translated.** |
| [okapi/truthfulqa_multilingual](okapi/truthfulqa_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [olaph](olaph/README.md) | Open-ended medical factuality Question Answering from the OLAPH dataset. | English |
| [openbookqa](openbookqa/README.md) | Open-book question answering tasks that require external knowledge and reasoning. | English |
| [paloma](paloma/README.md) | Paloma is a comprehensive benchmark designed to evaluate open language models across a wide range of domains, ranging from niche artist communities to mental health forums on Reddit. | English |
| [paws-x](paws-x/README.md) | Paraphrase Adversaries from Word Scrambling, focusing on cross-lingual capabilities. | English, French, Spanish, German, Chinese, Japanese, Korean |
| [pile](pile/README.md) | Open source language modelling data set that consists of 22 smaller, high-quality datasets. | English |
| [pile_10k](pile_10k/README.md) | The first 10K elements of The Pile, useful for debugging models trained on it. | English |
| [piqa](piqa/README.md) | Physical Interaction Question Answering tasks to test physical commonsense reasoning. | English |
| [polemo2](polemo2/README.md) | Sentiment analysis and emotion detection tasks based on Polish language data. | Polish |
| [portuguese_bench](portuguese_bench/README.md) | Collection of tasks in European Portuguese encompassing various evaluation areas. | Portuguese |
| [prost](prost/README.md) | Tasks requiring understanding of professional standards and ethics in various domains. | English |
| [pubmedqa](pubmedqa/README.md) | Question answering tasks based on PubMed research articles for biomedical understanding. | English |
| [qa4mre](qa4mre/README.md) | Question Answering for Machine Reading Evaluation, assessing comprehension and reasoning. | English |
| [qasper](qasper/README.md) | Question Answering dataset based on academic papers, testing in-depth scientific knowledge. | English |
| [race](race/README.md) | Reading comprehension assessment tasks based on English exams in China. | English |
| realtoxicityprompts | Tasks to evaluate language models for generating text with potential toxicity. | |
| [sciq](sciq/README.md) | Science Question Answering tasks to assess understanding of scientific concepts. | English |
| [score](score/README.md) | Systematic consistency and robustness evaluation for LLMs on 3 datasets(MMLU-Pro, Agi Eval and MATH) | English |
| [scrolls](scrolls/README.md) | Tasks that involve long-form reading comprehension across various domains. | English |
| [siqa](siqa/README.md) | Social Interaction Question Answering to evaluate common sense and social reasoning. | English |
| [spanish_bench](spanish_bench/README.md) | Collection of tasks in Spanish encompassing various evaluation areas. | Spanish |
| [squad_completion](squad_completion/README.md) | A variant of the SQuAD question answering task designed for zero-shot evaluation of small LMs. | English |
| [squadv2](squadv2/README.md) | Stanford Question Answering Dataset version 2, a reading comprehension benchmark. | English |
| [storycloze](storycloze/README.md) | Tasks to predict story endings, focusing on narrative logic and coherence. | English |
| [super_glue](super_glue/README.md) | A suite of challenging tasks designed to test a range of language understanding skills. | English |
| [swag](swag/README.md) | Situations With Adversarial Generations, predicting the next event in videos. | English |
| [swde](swde/README.md) | Information extraction tasks from semi-structured web pages. | English |
| [tinyBenchmarks](tinyBenchmarks/README.md) | Evaluation of large language models with fewer examples using tiny versions of popular benchmarks. | English |
| [tmmluplus](tmmluplus/README.md) | An extended set of tasks under the TMMLU framework for broader academic assessments. | Traditional Chinese |
| [toxigen](toxigen/README.md) | Tasks designed to evaluate language models on their propensity to generate toxic content. | English |
| [translation](translation/README.md) | Tasks focused on evaluating the language translation capabilities of models. | Arabic, English, Spanish, Basque, Hindi, Indonesian, Burmese, Russian, Swahili, Telugu, Chinese |
| [triviaqa](triviaqa/README.md) | A large-scale dataset for trivia question answering to test general knowledge. | English |
| [truthfulqa](truthfulqa/README.md) | A QA task aimed at evaluating the truthfulness and factual accuracy of model responses. | English |
| [turkishmmlu](turkishmmlu/README.md) | A multiple-choice QA test modeled after MMLU, written in Turkish based on Turkish high-school level exams. | Turkish |
| [unitxt](unitxt/README.md) | A number of tasks implemented using the unitxt library for flexible, shareable, and reusable data preparation and evaluation for generative AI. | English |
| [unscramble](unscramble/README.md) | Tasks involving the rearrangement of scrambled sentences to test syntactic understanding. | English |
| [webqs](webqs/README.md) | Web-based question answering tasks designed to evaluate internet search and retrieval. | English |
| [wikitext](wikitext/README.md) | Tasks based on text from Wikipedia articles to assess language modeling and generation. | English |
| [winogrande](winogrande/README.md) | A large-scale dataset for coreference resolution, inspired by the Winograd Schema Challenge. | English |
| [wmdp](wmdp/README.md) | A benchmark with the objective of minimizing performance, based on potentially-sensitive multiple-choice knowledge questions. | English |
| [wmt2016](wmt2016/README.md) | Tasks from the WMT 2016 shared task, focusing on translation between multiple languages. | English, Czech, German, Finnish, Russian, Romanian, Turkish |
| [wsc273](wsc273/README.md) | The Winograd Schema Challenge, a test of commonsense reasoning and coreference resolution. | English |
| [xcopa](xcopa/README.md) | Cross-lingual Choice of Plausible Alternatives, testing reasoning in multiple languages. | Estonian, Haitian, Indonesian, Italian, Quechua, Swahili, Tamil, Thai, Turkish, Vietnamese, Chinese |
| [xnli](xnli/README.md) | Cross-Lingual Natural Language Inference to test understanding across different languages. | Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, Chinese |
| [xnli_eu](xnli_eu/README.md) | Cross-lingual Natural Language Inference tasks in Basque. | Basque |
| [xquad](xquad/README.md) | Cross-lingual Question Answering Dataset in multiple languages. | Arabic, German, Greek, English, Spanish, Hindi, Romanian, Russian, Thai, Turkish, Vietnamese, Chinese |
| [xstorycloze](xstorycloze/README.md) | Cross-lingual narrative understanding tasks to predict story endings in multiple languages. | Russian, Simplified Chinese, Spanish, Arabic, Hindi, Indonesian, Telugu, Swahili, Basque, Burmese |
| [xwinograd](xwinograd/README.md) | Cross-lingual Winograd schema tasks for coreference resolution in multiple languages. | English, French, Japanese, Portuguese, Russian, Chinese |
lm_eval/tasks/careqa/README.md 0 → 100644
View file @ 7c9fbcf8
# CareQA
### Paper
Title: `Automatic Evaluation of Healthcare LLMs Beyond Question-Answering`
Abstract: [https://arxiv.org/abs/2502.06666](https://arxiv.org/abs/2502.06666)
CareQA originates from the Spanish Specialised Healthcare Training (MIR) exams by the
Spanish Ministry of Health. The close-ended version is a multiple-choice question
answering (MCQA) including 5,621 QA pairs across six categories: medicine, nursing,
biology, chemistry, psychology, and pharmacology, sourced from the 2020 to 2024 exam
editions. CareQA is available in both English and Spanish. The open-ended version
(English only) contains 3,730 QA pairs.
Homepage: \
[https://huggingface.co/datasets/HPAI-BSC/CareQA](https://huggingface.co/datasets/HPAI-BSC/CareQA)
#### Tasks
* `careqa_en`: MCQA in english.
* `careqa_es`: MCQA in spanish.
* `careqa_open`: Open-Ended QA in english.
* `careqa_open_perplexity`: Open-Ended QA in english, evaluated with perplexity.
### Citation
```bibtex
@misc{ariasduart2025automaticevaluationhealthcarellms,
title={Automatic Evaluation of Healthcare LLMs Beyond Question-Answering},
author={Anna Arias-Duart and Pablo Agustin Martin-Torres and Daniel Hinjos and Pablo Bernabeu-Perez and Lucia Urcelay Ganzabal and Marta Gonzalez Mallo and Ashwin Kumar Gururajan and Enrique Lopez-Cuena and Sergio Alvarez-Napagao and Dario Garcia-Gasulla},
year={2025},
eprint={2502.06666},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.06666},
}
```
lm_eval/tasks/careqa/careqa_en.yaml 0 → 100644
View file @ 7c9fbcf8
task: careqa_en
dataset_path: HPAI-BSC/CareQA
dataset_name: CareQA_en
test_split: test
output_type: multiple_choice
doc_to_text: !function utils.doc_to_text
doc_to_target: !function utils.doc_to_target
doc_to_choice: ['A', 'B', 'C', 'D']
metric_list:
- metric: acc
aggregation: mean
higher_is_better: True
metadata:
version: 1.0
lm_eval/tasks/careqa/careqa_es.yaml 0 → 100644
View file @ 7c9fbcf8
include: careqa_en.yaml
task: careqa_es
dataset_name: CareQA_es
lm_eval/tasks/careqa/careqa_open.yaml 0 → 100644
View file @ 7c9fbcf8
task: careqa_open
dataset_path: HPAI-BSC/CareQA
dataset_name: CareQA_en_open
description: >
Instructions: The following text is a medical question. Answer it in the most factual, concise and informative way possible"
output_type: generate_until
test_split: test
doc_to_text: !function utils_open.doc_to_text
doc_to_target: !function utils_open.doc_to_target
process_results: !function utils_open.process_results_gen
generation_kwargs:
until:
- "\n\n"
metric_list:
- metric: bleu
aggregation: nanmean
higher_is_better: true
- metric: rouge1
aggregation: nanmean
higher_is_better: true
- metric: rouge2
aggregation: nanmean
higher_is_better: true
- metric: rougeL
aggregation: nanmean
higher_is_better: true
- metric: bleurt
aggregation: nanmean
higher_is_better: true
- metric: bert_score
aggregation: nanmean
higher_is_better: true
metadata:
version: 1.0
lm_eval/tasks/careqa/careqa_open_perplexity.yaml 0 → 100644
View file @ 7c9fbcf8
include: careqa_open.yaml
task: careqa_open_perplexity
output_type: loglikelihood_rolling
doc_to_text: ""
doc_to_target: !function utils_open.doc_to_target
process_results: !function utils_perplexity.process_results
metric_list:
- metric: word_perplexity
higher_is_better: false
- metric: byte_perplexity
higher_is_better: false
- metric: bits_per_byte
higher_is_better: false
metadata:
version: 1.0
generation_kwargs: null
lm_eval/tasks/careqa/utils.py 0 → 100644
View file @ 7c9fbcf8
def doc_to_text(doc) -> str:
"""
Question: <question>
Choices:
A. <choice1>
B. <choice2>
C. <choice3>
D. <choice4>
Answer:
"""
if doc["question"] is None:
doc = {
"question": "In relation to the immune mechanism involved in the rejection of transplanted solid organs, indicate the incorrect answer:",
"op1": "Acute T-cell mediated rejection can be controlled through the use of drugs such as cyclosporine A or corticosteroids.",
"exam_id": 36,
"op3": "Chronic rejection or chronic graft injury is associated with endothelial damage mediated by anti-HLA antibodies.",
"category": "Medicine",
"unique_id": "5636d1af-e0b1-43b0-8a04-6f127dcf6785",
"op4": "Hyperacute rejection is mediated by cytotoxic T lymphocytes against donor antigens present in the recipient.",
"op2": "The presence of specific antibodies against the donor (DSA) in the recipient prior to transplantation is a contraindication for it.",
"cop": 4,
"year": 2024,
}
choices = [doc["op1"], doc["op2"], doc["op3"], doc["op4"]]
option_choices = {
"A": choices[0],
"B": choices[1],
"C": choices[2],
"D": choices[3],
}
prompt = "Question: " + doc["question"] + "\nChoices:\n"
for choice, option in option_choices.items():
prompt += f"{choice.upper()}. {option}\n"
prompt += "Answer:"
return prompt
def doc_to_target(doc) -> int:
return doc["cop"] - 1
lm_eval/tasks/careqa/utils_open.py 0 → 100644
View file @ 7c9fbcf8
import numpy as np
try:
import evaluate
bleu = evaluate.load("bleu")
rouge = evaluate.load("rouge")
bertscore = evaluate.load("bertscore")
bleurt = evaluate.load("bleurt", "bleurt-base-512", module_type="metric")
except (ModuleNotFoundError, ImportError):
raise ModuleNotFoundError(
"Please install evaluation metrics via pip install evaluate and pip install bert-score",
)
except Exception as e:
raise RuntimeError(
f"Error loading evaluation metrics: {str(e)}. Please check your installation."
)
def doc_eval(pred, refs):
try:
bleu_results = bleu.compute(predictions=pred, references=refs)
except Exception as e:
print(f"Bleu error: {e}")
bleu_results = {"bleu": np.NAN}
try:
rouge_results = rouge.compute(predictions=pred, references=refs)
except Exception as e:
print(f"Rouge error: {e}")
rouge_results = {"rouge1": np.NAN, "rouge2": np.NAN, "rougeL": np.NAN}
try:
bleurt_scores = bleurt.compute(predictions=pred, references=refs)["scores"]
except Exception as e:
print(f"Bleurt error: {e}")
bleurt_scores = [np.NAN]
try:
bert_scores = bertscore.compute(predictions=pred, references=refs, lang="en")[
"f1"
]
except Exception as e:
print(f"Bert error: {e}")
bert_scores = [np.NAN]
if bleu_results["bleu"] == 0:
# Sometimes bleu is 0.0 and this breaks the stderr computation.
bleu_results["bleu"] += 1e-5
results = {
"bleu": bleu_results["bleu"],
"rouge1": rouge_results["rouge1"],
"rouge2": rouge_results["rouge2"],
"rougeL": rouge_results["rougeL"],
"bleurt": np.mean(bleurt_scores),
"bert_score": np.mean(bert_scores),
}
return results
def doc_to_text(doc) -> str:
return doc["question"]
def doc_to_target(doc) -> str:
return doc["answer"]
def process_results_gen(doc, results):
pred, refs = [results[0]], [doc_to_target(doc)]
if len(refs[0]) < 1 or len(pred[0]) < 1:
return {
"bleu": np.NAN,
"rouge1": np.NAN,
"rouge2": np.NAN,
"rougeL": np.NAN,
"bleurt": np.NAN,
"bert_score": np.NAN,
}
results = doc_eval(pred, refs)
return {
"bleu": results["bleu"],
"rouge1": results["rouge1"],
"rouge2": results["rouge2"],
"rougeL": results["rougeL"],
"bleurt": results["bleurt"],
"bert_score": results["bert_score"],
}
def process_results_gen_w_repeats(doc, results):
pred, refs = [results[0]], [doc_to_target(doc)]
if len(refs[0]) < 1 or len(pred[0]) < 1:
return {
"bleu": np.NAN,
"rouge1": np.NAN,
"rouge2": np.NAN,
"rougeL": np.NAN,
"bleurt": np.NAN,
"bert_score": np.NAN,
}
results = doc_eval(pred, refs)
return {
"bleu": results["bleu"],
"rouge1": results["rouge1"],
"rouge2": results["rouge2"],
"rougeL": results["rougeL"],
"bleurt": results["bleurt"],
"bert_score": results["bert_score"],
}
lm_eval/tasks/careqa/utils_perplexity.py 0 → 100644
View file @ 7c9fbcf8
import math
import re
def doc_to_target(doc) -> str:
return doc["answer"]
def process_results(doc, results):
(loglikelihood,) = results
_words = len(re.split(r"\s+", doc_to_target(doc)))
_bytes = len(doc_to_target(doc).encode("utf-8"))
print(f"perplexity: {math.exp(-loglikelihood / _words)}")
return {
"word_perplexity": (loglikelihood, _words),
"byte_perplexity": (loglikelihood, _bytes),
"bits_per_byte": (loglikelihood, _bytes),
}
lm_eval/tasks/med_prescriptions/med_prescriptions_easy.yaml 0 → 100644
View file @ 7c9fbcf8
group: med_prescriptions
task: med_prescriptions_easy
dataset_path: devlocalhost/prescription-full
output_type: multiple_choice
training_split: train
validation_split: train
test_split: train
process_docs: !function utils.process_docs
doc_to_text: !function utils.doc_to_text_easy
doc_to_choice: !function utils.doc_to_choice_easy
doc_to_target: !function utils.doc_to_target
generation_kwargs:
until:
- "\n\n"
metric_list:
- metric: acc
aggregation: mean
higher_is_better: true
metadata:
version: 1.0
lm_eval/tasks/med_prescriptions/med_prescriptions_hard.yaml 0 → 100644
View file @ 7c9fbcf8
include: med_prescriptions_easy.yaml
task: med_prescriptions_hard
doc_to_text: !function utils.doc_to_text_hard
doc_to_choice: !function utils.doc_to_choice_hard
lm_eval/tasks/med_prescriptions/utils.py 0 → 100644
View file @ 7c9fbcf8
import ast
import random
import re
import datasets
full_med_list = [
"Cap Pregabalin, before breakfast and dinner, 1 week",
"Etoshivic 90mg, before breakfast, 1 month",
"Lyse, before breakfast, 1 month",
"EBAST M TAB, after food, 15 days",
"AZEE 500MG TAB, after food, 5 days",
"VOLTOP DSR TAB, before food, 5 days",
"ASCORIL D PLUS SYP, after food, 5 days",
"Sup. Afinal SR",
"ECOSPIRIN 75mg, daily, 3 months",
"OMEZ 20mg, daily, more than 1 month",
"TAMDURA OD, daily, more than 3 months",
"TELMA-CT 40/6-25, daily, 3 months",
"Col, once a day, 4 days",
"Syrup Allegra, once a day, 4 days",
"Atarax liquid, 1-0-1, 12 nights",
"Atogla Cream, 1-0-1, 15 days",
"Mox Mar 17, 1-0-1, 7 days",
"Paracetamol, 3 times a day, 6 days",
"Tassolure, 1 time a day, 6 days",
"Taloplex, 1 time a day, 6 days",
"Amoxicillin 625 mg, after breakfast and after dinner, 4 days",
"NA",
"NA",
"NA",
"Gabapin 300, before breakfast and before dinner, 20 days",
"Hosit Ds, before breakfast and before dinner, 20 days",
"T. Rifametrosy, x 5b",
"T. Anxipan",
"Ramitorva, bbf",
"Nerve-D",
"Glucobay M (25/500), al ad",
"Dapanormis (10/100), abf",
"Pioz (7.5), bbf",
"Cyblex M (80), bb, bl, bd",
"Syrup Paracetamol, morning, afternoon, night, 5 day(s)",
"Syrup LactobacillusRiboflavin, morning, afternoon, night, use if there is a need",
"Syrup Metanemic Acid, night, 3 day(s)",
"Syrup, morning, afternoon, night, 3 day(s)",
"Syrup Fexofenadine, morning, night, after food",
"PAN 40, morning and night, 2 months",
"Elsoud, night, 2 months",
"Ado, morning and night, 2 months",
"PAN 40, morning and night, 3 to 4 days",
"paracetamol, morning and night, 3 to 4 days",
"Guckelar spen, �5 /ar",
"Rmal Sy, x5la",
"Zincilokal. SI, e- e gez",
"Tab Letsi 2.5 mg, � 5 days",
"Tab. MCBM69, � 1 month",
"Teb. Ecosprin 75mg, � 1 month",
"Teb. Thyronoen 12.5 mg, � 1 month",
"Tab. Metformin 500 mg, � 1 month",
"Syf. Acima10-200, od",
"maltill, sos",
"Calpol, morning and night",
"Mucolite, morning and night",
"Advent, morning and night, 14/11/22",
"Tab. Thursmm, 2 times a day, 10 days",
"COD LOTTAti2, once a day, 10 days",
"PAN 40, before breakfast, 10 days",
"BP 4140/90m.R, 1-0-1",
"PR 7 70imim",
"For USG- Abdominal Pelvis",
"Syrup Cetalore-M, twice a day, 10 days",
"Drop Mosi (Eye), three times a day, 5 days",
"Syrup Selzita, twice a day, 3 weeks",
"Cap. Itaspor 100mg, morning and night, x 30 days",
"Tab Dazit 5mg, morning and night, x 30 days",
"Zedouf (200, x 8 days",
"Tab Montamarc",
"Aerodie, x 21 days",
"Sup, -(10) x 2days.",
"Tab Fepanil 650",
"Budesal (0.5mg), 1 � 5 days",
"Volini Gel, twice a day, 1 month",
"Ibuprofen, thrice a day, 1 week",
"PAN 40, morning and night, 30 days",
"Renova, morning and night, 60 days",
"VoliDer, morning",
"Pan 40, morning and night",
"Rosalix, morning and night",
"NA",
"PAN 40, before breakfast, before dinner, 3 weeks",
"Tab Gement, before breakfast, before dinner, 1 month",
"VILDAPHAGE-M, morning and night, 120",
"GRYCIPAAGE & 2, morning, afternoon, night, 3",
"AMLOSAFE-AT, morning, 60",
"As- lastochandile. ?, sos ypor or shle, brugal la 1-07 � 5day",
"Random 4ml-0-4aux stay, blunt d3 sachet r, 1 daily x sday",
"Clinic: 112, First Floor, Vikas Galaxy, Station Road, Sanewadi, Badlapur (W) M .: 8390268487",
"SHIKO, 3 times a day",
"PAN 40, morning and night",
"Velten 04mg, before lunch",
"ECOSPRIN 150 MG, after food - daily, 4 weeks",
"ROTACIUM, after food - daily, 6 weeks",
"SUPRADYN, after food - daily, 6 weeks",
"VIT D 60 K, after food - weekly, 6 weeks",
"PROSCO DHA, daily",
"SCOR HB, after food - daily",
"Lubiprostone, once daily, 30 days",
"Aprepitant, once daily, 30 days",
"Sildenafil, once daily, 30 days",
"Voglibose, once daily, 30 days",
"Jardiance, once daily, 30 days",
"Metformin, twice a week, 30 days",
"Pregabalin, once daily, 30 days",
"Insulin, before breakfast and before dinner, 30 days",
"Betnovate, apply twice daily, 30 days",
"PAN 40, morning, before breakfast, 10 days",
"DOLO 650, morning, afternoon, night, 5 days",
"AZITHRAL 500, morning, night, 3 days",
"Monocor-1, b.d (twice a day), 14 days",
"Arocon 500, 1x1 (once a day)",
"Rabe DSR, 1x2 (once in the morning and twice at night)",
"PAN 40, 1-0-1, 28 days",
"Pan 40, morning and night, 5 days",
"Calcium, 1-0-1, 30 days",
"Cachar, 1-0-1, 7 days",
"Rl olocal, 2, 4",
"Pal olocal, 8, 23",
"T. Augmentin 675 mg, 5 days",
"T. Enzoflam, 5 days",
"NA",
"NA",
"NA",
"NA",
"NA",
"NA",
"Toto. Fluconazol 100mg� 5 days., candid suppel - &l, 3 days",
"NA",
"NA",
"JU-CRiXAN (250) 100, bd, 5 days",
"Jal. DEFWORT (6) 107, bd, 10 days",
"Ta. MUCiNAT- AB 10, bd, 10 days",
"Tal. LUKOTAS- HD 100, bd, 10 days",
"Syp. LUPITUSS, bd, 5 days",
"PAN 40, 3 times a day, 2 months",
"POP",
"Sugar",
"Surgery",
"PAN 40, morning and night, 14 days",
"Saazole, morning and night, 14 days",
"Celen 22000, morning and night, 14 days",
"RB NB 4911",
"coolora cables, 3 times a day",
"montina-Fx, 12 days",
"pulmones 250, 6 days",
"Sup. Rapitus plus",
"Tab. PriJa-",
"Tab. vreit -200",
"NA",
"PAN 40, morning and night, 5 days",
"Paracetamol, morning and night, 5 days",
"Amoxicillin 250mg, morning and night, 5 days",
"Argesic -1, morning and night, 100",
"Glycinhage (500), 2 times a day, 1 month",
"Diaberon, 2 times a day, 1 month",
"D-fix, 1 time a day, 1 month",
"Ultracet, 1 time a day, 20 days",
"Lipiland - F, 1 time a day, 1 month",
"Ade P-650, 1 time daily, 30 days",
"Tab ACOGURD NT, 1 time daily, 30 days",
"T3 LC, 1 time daily, 30 days",
"D-360, 4 times a day, 30 days",
"HAcorpus + NANOFAST FEL",
"NA",
"Pan 40, morning and night, 1 week",
"San 52, morning and night, 1 week",
"Calapiso liver, morning and night, 1 week",
"Abronzo pasda, morning and night, 1 week",
"PAN 40, morning, 7 days",
"Paracetamol, morning and night, 3 days",
"Antibiotics, morning and night, 7 days",
"scor hb plus, 4 week(s)",
"radibone, 4 week(s)",
"NA",
"PAN 40, morning and night, 1 week",
"Nab Duolic, night, 1 week",
"Aze 50mg, night, 1 week",
"Mantar Le, morning and night, 1 week",
"Pan-D, morning and night, 1 week",
"Sy, morning and night, 1 week",
"Auchakind, morning and night, 1 week",
"SYP IBUGESIC PLUS 45, bbf, 5 days",
"SYP MEFTAL P, bbf",
"SYP MAXTRA, bbf",
"SYP KUFRIL LS, bbf",
"SYP ONDEM, bbf",
"VIAL ENTEROGERMINA",
"Arteliar, 1-0-1, 14 days",
"Cafin Dehave, 1-0-0, 7 days",
"PENTANE, 1-0-1, 30 days",
"Tab. Folineo, once a day, for intafol-d far",
"Tab. Dychease, once a day, ongoing",
"Tab. Ecosprin 75, once a day, for 15 days",
"Tab. Rasicap 100, once a day, ongoing",
"Tab. Calshine, once a day, from start of treatment",
"Protein powder, once a day",
"Tab. Imumust, once a day, for 10 days",
"Syp-Digecaine, twice a day, for 18 days",
"Tab. Condibiotic, once a day",
"Richglow lotion",
"Tab. Tidilan, once a day",
"Pan 40, after meal, 7 days",
"Paracetamol, after meal, 3 days",
"Tab. CCM, before meal, 14 days",
"Colifino, twice a day, 10 days",
"Calivol Softgel, once a day, 8 days",
"Livogen-2, once a day, 2 months",
"Pan 40, once a day, indefinite",
"Zenha, before bedtime, indefinite",
"Apelo poglute",
"TT 0.5 ml",
"ARV, 0, 3, 7, 14, 28",
"R.B.S (28)",
"Blood Sugar",
"BP-125/77 mmH1",
"PR-86/min",
"Hb-11jm, 25/02/23",
"Calpsor C Ointment, 1-0-1, 3 week(s)",
"Allegra 180 Tablet, 1-0-1, 3 week(s)",
"Venusia Max Cream, 1-0-1, 3 week(s)",
"IV. R.L .O I, morning and night",
"Iv. Auguriurl (1.2m) IB, morning and night",
"IV. M.N. I IO, morning and night",
"IV. oflox(00m) IO., morning and night",
"TAB AZILLUP 500 / AZOTOX 500, morning and night",
"TAB ORTHOTIME BR, morning",
"OINT CHETOMESH SF, as per requirement",
"TAB VITAPLUS, after lunch",
"Im Duvadilon, 10, for days",
"Cap Lanx 00, 5",
"VILDAPHAGE-M, 1-0-1, 120 tablets",
"AMLOSAFE, 1-0-0, 60 tablets",
"Cosader, 1-0-1, 120 tablets",
"Ostosline, 1 week, 10 days",
"Zerodol CR, as needed",
"Gabancuran 100mg, daily",
"Pau 40mg, daily, 10 days",
"Prochoscary, as required, not specified",
"cox gel v-V, once daily, not specified",
"Anoxlief dialiment, renew after 3 weeks, not specified",
"UPRISE D3 GUK, once daily",
"D3 60 K, once daily",
"Tab 924, every evening",
"Tenovate cream, 3 weeks",
"Tab. HCQS (400g), 3 weeks",
"Mantoux test",
"Quantiferon - TB Gold test",
"Tar lepodem, 1-0-1, 7 days",
"T. Sompraz 40mg, morning, night, 3 days",
"T. CINTARRO, night, 4 days",
"Sucafel-o, morning, 3 days",
"NORMAXIN, morning, afternoon, night, 7 days",
"Tab. Nejor, bbf, 1 month",
"Tab. Defical, cair, 1 month",
"Ij Osteo D3, stat, 1 month",
"Cariminic Syrup, td, 3 days",
"Sumol, td, 3 days",
"Lactovil, td, 10 days",
"T SOMPRAZ L, before food 7days",
"BIFILAC GG, 10 days",
"Glycomet, bbf, 10 days",
"Glimore-M, bbf, 10 days",
"Tenegem-M, 001, 6 months",
"Telmanos-MT, 001, 6 months",
"Dafine, al, 10 days",
"PAN 40, morning and night, 14 days",
"Paracetamol, morning and night, 3 days",
"Telmikind 20 Mg, morning, night",
"Gliminyle M 1 Tab, before meals",
"Dia-Pace (Metformin) 500 mg, after lunch",
"Bixro 16 Mg (Betahistine), morning, afternoon, night",
"Pantapace D, before food",
"Gabacrine M (Gabapentine 300 mg + Methyl Cobalamine 500), after dinner",
"Tab SITADAPA - 10/100, 1-0-0, 3 months",
"Tab CALCIBUS, 1 pill daily, 3 months",
"Tab DRISE (VIT D), 1 pill per month, 3 months",
"Suma-ES (250), 3 times a day, 3 days",
"Mestat P 12.5 mg, 3 times a day, 3 days",
"Meospann-H Ointment, 5 times a day",
"Augmentin ES (Amox 600mg), 3 times a day, 7 days",
"PAN 40, morning and night, 5 days",
"Ocupar Dy cream, as required",
"Lys Mega ce for, before breakfast, 5 days",
"Thromboprob oil, 2 times a day, for 4 days",
"Nadoxin cream, as prescribed",
"Tmedlar, once daily, 14 days",
"Zerofel-sp, as prescribed",
"72 DM, daily",
"PAN 40, daily",
"GUILD BITOSTRI CODITID",
"Tab. Daylen, twice daily, 42 days",
"SANSTICS - HEALTHIER LIVING, not specified, not specified",
"NovoRapid SL, morning, afternoon, night",
"Levemir, night",
"Dielie Consusane, morning, afternoon, night",
"T. pregabid-ME 75mg, after meal, 2 weeks",
"T. Acesse 100 mg, before meal, 2 weeks",
"Syrup Atarax, twice a day, 5 days",
"Cream Flutivate (Skin), twice a day, 5 days",
"Calibnie, 1 mg daily, 6 months",
"PAN 40, before breakfast and before dinner",
"DIGENE, before breakfast",
"SURIC ACID",
"Tab. Folite 5mg, daily, 30 days",
"Tab. Folitrax 2.5 mg, daily, 30 days",
"Cap. Pan-D, before meals, 30 days",
"Tab. Ricorsia, after meals, 30 days",
"Tab. Saaz DS, morning and night, 30 days",
"Tab. Nurokind OD, morning and night, 30 days",
"Tab. Product 4, sunday off, 30 days",
"Tab. Orcerin 6M, monday and wednesday, 30 days",
"Tab. Celin 500, monday and wednesday, 30 days",
"Tab. Ecosprin 150, sunday off, 15 days",
"Tab. Ecosprin Gold 10/75, daily, 30 days",
"Tab. Medvol 2mg, at night, 30 days",
"Tab. Pan - D, before dinner, 30 days",
"Cap. Ecosprin 75, sunday off, 30 days",
"Tab. Orcerin 6M, monday, wednesday, and friday, 30 days",
"Tab. Celin 500, monday, wednesday, and friday, 30 days",
"Ranitidine, 1-0-0, 6 days",
"Paracetamol, 1-0-1, 5 days",
"Azithromycin, 0-1-0, 3 days",
"Suspension Drotin DS, every 8 hours, 5 days",
"Tablet Lanspro 15 mg, once a day, 5 days",
"Syrup Tummy Soft, twice a day, 5 days",
"PAN 40, after food, 7 days",
"Paracetamol, after food, 3 days",
"Tab. Bifolate, 0-0-1, x continue.",
"Tab. Doxinate, 1-0-1, x continue.",
"CIFRAN 500MG TAB, bd (twice daily), 1 month",
"RELENT PLUS 8J, bd, 5 days",
"RANTAC 88, od",
"Tab. AlfsN, bd",
"Tab Octobix, bd",
"Tab Ler, bd",
"Tab Pzae, bd",
"Tab Euognix, bd",
"Mamadialitu con, bd",
"Ag preg, bd",
"- FRS/2hPP BS, bd",
"PENTAXIM, opd",
"PRIORIX, opp",
"MEASLES, MUMPS, AND RUBELLA VACCINE (LIVE) IP, 6 hourly for fever >99�f",
"ZOFRAN, 1�15 days",
"ZINCOVIT ZINCITOTAL, 3 months",
"cox gel v-V, once a day, 3 weeks",
"Anoxlief dialiment-0-, not specified, renew after 3 weeks",
"Tab Martifur MR 100 mg, after food - daily - 4 days",
"Novefos Sachet 3 gm, bed time - single dose",
"Tab Urispas, after food - sos",
"Syp Alkasol, after food - daily - 3 days",
"Hifenac-SP, before food, 10 days",
"Pan-10, before food, 10 days",
"Tolyb/Chymoral forte, after food, 10 days",
"Resner plus | Fibrogenic-N/Max-mala-NT",
"FOLSAFE-L, 30 days, 4 to 5 months",
"Tab. Lipi 200, 6",
"Tab. Recotar, 6",
"Cap. Ormed 20, 6",
"FLUVIR 75MG TABLET, after food - daily - 5 days, 5 days",
"CALPOL 650MG TABLET, 1-1-1, 3 days",
"XYZAL M TABLET, 0-0-1, 5 days",
"REBEZ DSR CAPSULE, 1-0-0, 5 days",
"Tab Laregas (300), morning and night",
"Tab Nauto (S), morning",
"Tos Remae CP3 101, morning and night",
"Tab Cryopan DSR 101, morning",
"Ade P-650, bd, 30 days",
"ACOGURD NT, bd, 30 days",
"T3 LC, bd, 30 days",
"D-360, bbf, 4 weeks",
"HAcorpus, bd, 30 days",
"NANOFAST FEL, bd, 30 days",
"Aceclo plus, morning and night",
"Gamot, night, 10 days",
"Tendojoy, morning and night",
"PAN 40, morning and night, 6.1",
"Paracetamol, after breakfast and before dinner",
"Xiosoy, before breakfast",
"Avil 25, once daily, 5 days",
"PanDro, once daily, 7 days",
"Tab TENOFOVIR ALEFANAMIDE 25mg, to continue",
"SOL PNA",
"HEC",
"DIABETES CARE",
"INSULIN LANTUSS",
"ILECOSPRIN AV 75",
"TELFAST 40",
"TABAK VITE S",
"CIPCAL 500",
"TAB.RYBELSUS 14 MG, 1 -- 0 -- 0, 30 days",
"TAB.GLEDEPA 10 MG/OXRA 10 MG, 1 -- 0 -- 0, 30 days",
"TAB.GLYCOMET SR 500 MG, 0 -- 0 -- 1, 30 days",
"TAB.SYMBAL (30MG), 0 -- 0 -- 1, 30 days",
"CAP.RABONIK DSR, 0 -- 0 -- 1",
"TAB.RAZEL F 5 MG, 0 -- 0-(1), 30 days",
"R MEGANEURONE OD, daily, 30 days",
"SYP.TOPUP D3 Cholecalciferol 60000IU, after 4 days",
"Sumaalla Jamba, 15 days",
"Nasal Spray, bd (twice daily), 15 days",
"Antacid Plus 200, 5 days",
"Inj Bett, stat",
"PAN 40, morning and night, 4-5 days",
"Paracetamol, morning and night, 4-5 days",
"Azithromycin, morning and night, 4-5 days",
"Ecosprin AV, od",
"Night spadives",
"T. CLOPILET-A 75/175",
"T. ROSLOY-F 20/160",
"T. BUERT OD 24",
"T. RAZO-A 20/10",
"T-AMLOKIND AT 5/150",
"Greenor, before breakfast and dinner",
"ACITRON, before breakfast, 3 days",
"GING, after dinner, 3 days",
"ACITROM (4 mg), 6pm",
"TELMA AM (UDS), after dinner",
"Solopart (8), 10pm",
"Atarax 5mol-5001-5ml, 1-0-1, 1 month",
"Syp- Goun DS/ Campol -250, 1-0-1",
"Syp Bevon, 0-1-0, 1 month",
"NA",
"NA",
"NA",
"Cepodem XP 325 Tablet, 5 days",
"Nimus P Tablet, 3 days",
"Ebast M Tablet, 5 days",
"Corex DX Syrup, 5 days",
"CLAVAM (Augmentin - 625), twice a day, 15 days",
"DOWO-650, twice a day",
"LBC",
"Evion 400mg, morning, afternoon, night, 2 months",
"Dolo 650, morning, afternoon, night, 3-4 days",
"Primosa 100, morning, night, 2 months",
"Etoshine MR, twice daily, 5 days",
"Paracetamol, once daily, 3 days",
"GALOC, bd",
"corred, al",
"Tab Aspirin, bbf, 30 day",
"PAN 40, morning and night, 14 days",
"Paracetamol, morning and night, 3 days",
"Syp. CREMAFFIN, bd, 5 days",
"CONZAFIT, 1-0-1",
"PAN 40, 1-0-1",
"ONVISTA, 0-0-1",
"PAN 40, 1-0-1, 10 days",
"Crocin, 3 times a day, 5 days",
"Azithromycin, 1-0-0, 3 days",
"Parin, before breakfast",
"Tab. Mysolvo, before breakfast",
"Ta Martinus, before breakfast",
"syp andem sil som, calimesa sul, po �5 days.",
"syp",
"Syp, � 1 week",
"FOLSAFE-L, once daily, 30 days",
"Generic Medicine, once a day, 3 days",
"Eye Drops, three times a day, 7 days",
"Tu MICROBACT 500mg, 0-62",
"SIGNOFLAM",
"SOMPRAZ 20mg, 0-52",
"PAN 40 (Pantosec), before food, 5 days",
"Colafix, after food, 5 days",
"Olocal, after food, 5 days",
"Lanol ER, 1 - 0 - 1, 5 days",
"Pushan D3 (60K), 5 ml - once a week, 10 weeks",
"Pan 40, before breakfast and dinner, 6 days",
"Sommore, morning and night, 12 months",
"Upini D3, before dinner, 3 months",
"Levetiracetam 500 mg, morning and night, 6 months",
"Alzil-M Forte, morning and night, 6 months",
"T.100.2, in the morning",
"p-250 sJp, per day",
"Ambrail ph",
"Remy celles",
"compt 100",
"Demander Alle",
"BANH 81",
"Tab. Stemelia B, bd",
"Ventyr 1 Sp",
"Das Rovastat 10mgts",
"Cap Alten Aslan",
"Das Wetrans",
"PAN 40, before breakfast, before dinner, 3 months",
"AKT-4",
"Zincovon",
"Dicorate-ER 500mg, 1-0-0, 1 month",
"Mebodep-CD3, 1-0-0, 1 month",
"Tryptomer-25mg, 0-0-1, 1 month",
"Writex 7G, 0-0-1, 1 month",
"Napra D 500mg, sos, 9",
"HEPR, daily, 3 days",
"BIOVAC 1 Chocin DS, daily, 3 days",
"ALP, 1, 10 days",
"MR, 1, 3 days",
"Tota, 1, 6 days",
"Tab Nxfor Sp 10, 1, 10 days",
"T-3",
"NA",
"NA",
"Go-calm 250, before meals, 5 days",
"Ses Admit 457, after meals, 14 days",
"dis Bnsector, 3 times a day, 5 days",
"SISCOX TH TAB 10S, oral, 10 day",
"SOMPRAZ 40MG TAB 15S, oral, 10 day",
"NERVMAX SR 75 TAB, oral, 10 day",
"SYSTAFLAM 50GM GEL, oral, 30 day",
"Pan 40, 1-0-0, 7 days",
"Dolo 650, 1-0-1, 3 days",
"Augmentin 625, 1-0-1, 5 days",
"PARACETAMOL, 5 days",
"ZINC SUPPLEMENT, 5 days",
"COUGH SYRUP, 5 days",
"PAN 40, morning, 5 days",
"Rantac, bd (before dinner)",
"Osyp. Random PD, tds (three times a day)",
"Blo 50 5mg, tds (three times a day)",
"Ibugesic, prn (as needed)",
"@syr. capot (250), prn (as needed)",
"syp. Rady (200), qhs (before bedtime)",
"Pan 40, after breakfast",
"Ady, after lunch",
"Colfor ps, after dinner",
"Sebisher Dermalils, 2, 7",
"Teb. Trthustive, 2, 5",
"Slikkoby lacion D, 1, 5",
"c-wey shampoo, 1",
"candles 2/c",
"PAN 40, bd (before dinner), 7 days",
"PCM 500, tds (three times a day), 7 days",
"Suspension Ibugesle Plus, 6 hourly (10 ml - 10 ml - 10 ml - 10 ml), 3 days",
"Suspension Meftal P (60 ml), 10 ml, sos at fever before 6 hrs of ibugesic",
"Jij Methy Cobal, bbf",
"Das Deterrol, bbf",
"Vice-M (500), bbf",
"Das Stalit-D, bbf",
"Das Enite (40), bbf",
"DIVYA CHIRAYTA KWATH 100 GM, morning and evening, 30 days",
"DIVYA GILOY KWATH 200 GM, 1 hour before meal, 30 days",
"DIVYA SARVAKALP KWATH 100 GM, 30 days",
"DIVYA IMMUNOGRIT 60 N 33 OM, 30 minutes before breakfast/lunch/dinner",
"DIVYA MADHUNASHINI VATI EXTRA POWER 60 GM, 30 minutes after breakfast/lunch/dinner, 30 days",
"DIVYA MADHUGRIT TABLET 60 N 38 GM, 30 minutes after breakfast/lunch/dinner, 30 days",
"DIVYA TRIPHALA GUGGUL 40 GM, 30 days",
"PAT NUTRELA DAILY ACTIVE CAPSULE 750 MG, 30 minutes after breakfast-lunch-dinner, 30 days",
"DIVYA SHUDDHI CHURNA 100 GM, bedtime, 30 days",
"Tablet Lanol ER (650 mg), 15 days, after breakfast. after dinner",
"2 Tablet Gabamax NT 50/10, 15 days, before dinner, at 8pm",
"Gel Dolcinac Mr, 15 days, after breakfast. after dinner",
"Tablet Collacium Strong, to continue, after breakfast",
"Tablet Etody (90 mg), sos, for severe pain",
"Tablet Trioflex Tablet, 1 month, after breakfast, after dinner",
"Hidraslim lotion livice aday",
"Jab Teezie Song 505",
"Chloramphenicol, morning and night, 7 days",
"Paracetamol, morning and night, 7 days",
"Azithromycin, morning and night, 7 days",
"Boxy Boule, morning, afternoon, night",
"crepetart, morning, afternoon, night, till recovery",
"PREGALIN SR 75MG, after food - daily, 30 days",
"PAN 40, morning and night, 12 days",
"Thyrofit 100, morning, 30 days",
"Metformin 500, morning and night, 60 days",
"TELMA LN BETA 50 TABLET, after food - daily, 1 month",
"CLONOTRIL 0.25MG TABLET, after dinner - daily, 1 month",
"ALDACTONE 25MG TABLET, after food - daily, 1 month",
"Tab Allegra 180mg, hi,ac, 100� 10 day",
"RBS, (mono -10.00am)",
"Sk(2) Pab, (7to 73 pm darle), � 20 day",
"Tan - Appar 65g, 2 times a day, 18 days",
"Cap Attop- DJ-2, 1 time a day, 18 days",
"Sy citar, 1 time a day, 18 days",
"Syp Cocintus, three times a day, 7 days",
"Syp Augmentin Duo, three times a day, 7 days",
"Nasivion, as needed",
"Syp Marx, as needed",
"PAN 40, after breakfast and dinner, 3-4 days",
"Paracetamol, after breakfast and dinner, 3-4 days",
"NA",
"As plast risodaless, tamonth",
"Veagreat 10, clo: morning and night",
"Paracetamol, 1-0-1, 3 days",
"Raufen 150mg, 1 before dinner",
"Zincenit, 1 after lunch",
"Folsafe-L, daily, 30 days",
"Suspension ATM XL (200 mg) - Azithromycin, 5 days, before food",
"Suspension P 250 - Paracetamol, till required",
"Syrup Cetzine - Cetirizine, till required",
"ALBENDOL, bd, due today",
"ILOVIT NANO 60K, bd, 16th (sun) x 10 days",
"Gemal-p 2-54 RD, bd, 18/9/23",
"Suspension Meftal P (60 ml)",
"Syrup Solvin LS",
"Drop Votriz cold drops",
"Syrup Bactoclav DS",
"OKALET (5mg), morning and night, 5 days",
"ENZOCORT, morning and night, 5 days",
"CEFOLA, morning and night, 5 days",
"OTRIVINsal, morning and night, 5 days",
"SYP IBUGESIC PLUS 45 (ML/100 MG), bd, z days",
"YOUMO-PARACETAMOL 12.5A (ML/100 MG), bd, z days",
"SYP MEFTAL P (5 ML/100 MG), bd, z days",
"MEFENAMIC ACID, as per requirement, z days",
"SYP MAXTRA, as per requirement, z days",
"CPM DG-PHENYWINE SUG (2.5 ML), as per requirement, z days",
"SYP KUFRIL LS (5 ML/0.5 MG), not mentioned, z days",
"LEVOSALBUTAMOL, as per requirement, z days",
"SYP ONDEM (5 ML), not mentioned, z days",
"ONDANSETRON, as per requirement, z days",
"VIAL ENTEROGERMINA, as per requirement, z days",
"BACILLUS CLAUSE, as per requirement, z days",
"Alocepodem 100-DT, once daily, 10 days",
"Sp Asthalin, as required, -",
"Ibugeric play, as required, -",
"HHLEVO M KID 60ML SUSPENSION, after food - daily, 10 days",
"VENUSIA SOFT LOTION, after bath - daily, 2 weeks",
"ATONIDE 20GM GEL, after bath - daily, 15 days",
"OMNACORTIL ORAL SUSPENSION, after food - daily, 1 week",
"PAN 40, morning and night, 2 weeks",
"Ankle Binder / Splint, all day except shower, 2 weeks",
"Tax, before dinner",
"Mandyic party, 1-0-1, 14 days",
"MyOnR, 1-0-1, 5 days",
"pregabalin, 1-0-1, 5 days",
"Dokln, 1-0-1, 5 days",
"Clofain Lack, morning and night",
"Tab ALTRADAY, morning and night",
"Teb Shelle XT, morning and night",
"PAN 40, morning and night, 24 portand 4og livs11",
"Montop-11, morning and night",
"Tab Aceclo plus, after breakfast, after lunch",
"Tab Gamot, after dinner, 10 days",
"Tab Tendojoy, after breakfast",
"Paracetamol, twice a day, 7 days",
"Dolo, twice a day",
"Coscorit, once a day",
"Solibar, bd",
"LANOL ER, 5 days",
"SISCOX TH TAB 10S, oral, for 10 days",
"SOMPRAZ 40MG TAB 15S, oral, for 10 days",
"NERVMAX SR 75 TAB, oral, for 10 days",
"SYSTAFLAM 50GM GEL, oral, for 30 days",
"PAN 40, morning and night",
"Paracetamol, morning and night",
"Amoxicillin, morning and night",
"Paracetamol, after meals, 7 days",
"Diclofenac, morning and night, 7 days",
"Pantoprazole, before breakfast, 7 days",
"PAN 40, after meals, 6 days",
"Allegra(120), after meals, 6 days",
"Chilkul DSR, after meals, 6 days",
"INFLUVAC TETRA 0.5 ml, once, single dose",
"SYP. CALDOL (250mg/5ml), twice, 3 days",
"THYROX 112.5 mcg, empty stomach, ongoing",
"SUPRACAL PRO-ICAL, after dinner, 3 months",
"D-SOL 60 K, once monthly, ongoing",
"TELSAR BETA 25, after breakfast, 3 months",
"CILNIKEM 10 mg, after breakfast, ongoing",
"VILDAPHAGE-M, morning and night, 120",
"GRYCIPAAGE, before breakfast, lunch, and dinner, 5",
"AMLOSAFE, morning, 60",
"Cosader, morning, 120",
"PAN 40, morning and night, 5 days",
"Tab Zahoren - 03 Br, morning and night, 5 days",
"Tab Rabilir, night, 5 days",
"Pan 40, daily, 10 days",
"Clavum 625, daily, 5 days",
"Nebistar 2.5, daily, 14 days",
"Nasta DATISVIK 2%, 3 times a day, 8 days",
"SYP. AUGPEN DS, 3 times a day, 8 days",
"SYP. MAXTRA, 1 time a day, 8 days",
"Tab. GLYCOMET -TRIO 2mg, daily",
"Tab. DAPANORM TRIO (10/100/500), daily",
"Tab. THYRONORM 75 mcg, daily",
"PAN 40, morning and night, 10 days",
"Azithromycin, once a day, 5 days",
"Silofast, 1-0-1, 10 days",
"ORAHELP GEL, 5 days",
"Sy. FEVFAST DS, every 6 hours",
"Sy. DELCON, 3 times a day",
"Sy. VENTISOL JR., 3 times a day",
"Sy. AZIFINE 200, once a day for 3 days",
"PAN 40, morning and night, 3 days",
"Ach",
"U.S.9",
"Augustin ADS, before breakfast and dinner, 10 days",
"Sap. I bri plu y me thy, bedtime, 10 days",
"ciplor idp B/Eg 64, after lunch, 4 days",
"EuMD, 2 sce-1",
"T. DUTACOSIN",
"T. Amitriplilie",
"Avil 25, bd, 5 days",
"PanDro 100, bd, 7 days",
"Pan 40, bd, 7 days",
"ASHoke, 1-0-1, bizlerer",
"SomMA, 1-0-1, rendat",
"MAPSORD, 1-0-1, tut's",
"Damprosen 101, 0-0-1, tubs",
"IAB. UYIUK SMG, after food - sos",
"TAB. URIMAX D, after dinner - daily",
"TAB. NEXPRO 20MG, before breakfast - daily",
"TAB. VYMADA 100 MG, after food - daily",
"CAP. ROZAVEL A 75MG, after lunch - daily",
"TAB. EZEDOC 10MC, after dinner - daily",
"TAB. TIDE 10MG, after breakfast - daily",
"TAB. SARAPID 1.0MG TAB, before food - daily",
"PAN 40, morning and night, 7 days",
"Recuvac, night, 10 days",
"Br2 6 Th, morning and night, 7 days",
"PAN 40, morning and night, 10 days",
"Asthalin, 3 times a day, 5 days",
"Levolin, morning and night, 10 days",
"Astroscemi Aec Renan, morning and night",
"PAN 40, morning and night, 15 days",
"NEMOCID, 2 times a day, 2 days",
"PAN 40, morning and night, 30 days",
"LMD, , ",
"EPD, , ",
"Tablet Amlokind (5 mg), after breakfast, to continue",
"Tablet Vertizac, after breakfast, after dinner, 5 days",
"Tablet Diligan (25 mg), after breakfast, after dinner, 5 days",
"Tablet Full B12 SR Tablet, after breakfast, after dinner, 10 days",
"Tablet Olmetime (20 mg), after dinner, 10 days",
"Tablet Detrab dsr, 15 days",
"Tablet Folvite (5 mg), 15 days",
"Injection Vitcofol, 4 weeks, alt. day 2cc, 5 doses over 10 days",
"Tablet Enuff, 3 days",
"Capsule Calfix k2, 5 days",
"Injection Carmijen 6 lac iu, 4 weeks, intramuscular",
"Tablet Vibpreg mnt, 15 days",
"TAB TY PRO T4, morning and night, ongoing",
"TAB EGNERVE NP, night, 10 days",
"TAB CALCESAVE PLUS, morning, 2 months",
"VED GOK NANO SHOPS, once a week, 12 weeks",
"NOTTID Allegy, bd, 7 days",
"Co pain, bd, 7 days",
"T. Infinito vietato, bd, 7 days",
"Budesal (0.5mg), 1 � 5 days",
"TAB. OLYMPR IX M 500MG",
"TAB. TELMIKIND 40 MG, 3 months",
"CAP. EC OSPRIN AV 75MG",
"PAN 40mg, morning, night, before 18-may-23",
"Vallday, morning, night, before 18-may-23",
"Gratisin, morning, night, before 18-may-23",
"HIGH FIBER DIET",
"SITZ BATH THRICE A DAY WITH BETADINE LOTION",
"TAB ZOCEF CV 500, 7 days, 7 days",
"CAP PENRAB DSR, 7 days, 7 days",
"TAB DOLO 650, 5 days, then as needed, 5 days, then as needed",
"LACTIFIBER POWDER, 7 days, 7 days",
"TAB VITACOVER 5 G, 7 days, 7 days",
"BETADINE DRESSING AND PACKING LOCALLY AFTER EACH SITZ BATH",
"BRILINTA 90 mg, morning and night",
"ECOSPRIN 75 mg, before breakfast",
"STORVAS 80 mg, morning",
"CONcoe 2.5 mg, morning",
"CARDACE 1.25 mg, night",
"URIMAX D, after lunch",
"Phenoxymethylpenicillin, after breakfast and dinner, 7 days",
"Diclofenac, after breakfast, 7 days",
"PAN 40, before breakfast, 5 days",
"Aurin 625, before breakfast, 5 days",
"Da 108, before breakfast, 5 days",
"Tas-oflex-02, bd",
"Ths. Rebelt DSn, bbf",
"Spor 9rx, bd",
"Crocin DROPS, maximum 4 times/24 hours",
"Masoclearsal drop",
"Dolgan P 1x9, 10",
"( Zydees, 100, 3",
"Allegre 120g, 3",
"Nemaber fat",
"Levocetgine, 1 175, 3",
"Den., anatax o, 200g 4",
"(Zyders), 3",
"Tab metro chon, p - 60/m, 2-3 days",
"Mykool Cream, pr, 15-20 years",
"Tal. zerofor-P, before 1 week, colonoscopy sos",
"Taxing, 1-0-1 (3 times a day), 3 days",
"Stuurst-AF, 1-0-1 (3 times a day), 3 days",
"Ventryl, 1-0-1 (3 times a day), 3 days",
"Mental-BS, sos",
"Misogesic SR, once daily, 10 days",
"T. Oatzy, once daily, 300 days",
"Tab Torfix 400, morning and night, 7 days",
"Danga Lite, morning and night, 17 days",
"Car sizumas Rich (ML), morning and night, 17 days",
"Tablet Partivit 300mg BD, 25+2",
"Tab (alview facts",
"Tab worviday op",
"Cepodem 200 Tablet, daily, 3 days",
"Lezyncet-D Tablet (Levocetirizine 2.5 mg + Phenylephrine 10 mg), daily, 3 days",
"Immu C-Plus Chewable Tablets (Ascorbic Acid 500 mg + Vitamin D2 400 IU + Zinc Sulphate Monohydrate 5 mg), daily, 3 days",
"Reswas Syrup (Chlorpheniramine 2 mg + Levodropropizine 30 mg), daily, 3 days",
"Pacimol 650 Tablet (PARACETAMOL 650 mg), daily, 2 days",
"PAN 40, morning and night, 3 months",
"Caps D-RISE (Gok), once a month, 1 year",
"T. cifan- CT, 1-0-1, 5 days",
"T. Nimica plus, 1-1-1",
"Esogren 40, 1-0-0, 5 days",
"c. EnnGt 100, 0-1-0, 2 days",
"c. Imudium, 1-0-0",
"Pan 40, 18 days, 10 days",
"T. Acticont, 18 days, 10 days",
"T. Cyaa, 18 days, 10 days",
"Nosekund nose drops, 18 days, 10 days",
"Monucet 200, bd, 10 days",
"Hiponal MR-10, bd, 12 days",
"Calpal 500, bbf, 27 days",
"SANOGESIC P, 1-0-1, for 5 days",
"CEPODEM 200 MG, 1-0-1, for 5 days",
"MONDESLOR, 0-0-1, for 5 days",
"SISBONE K2, 0-0-1, for 15 days",
"Flucold AF oral deap, 1-0-1, 1 day",
"Calpol deogs, 1-0-1, 1 day",
"Extend Total Tablet, 1-0-1, 10 days",
"Sertafic 2% Ointment, 1-0-1, 5 days",
"Zentel Chewable Tablet, 0-0-1, 3 days",
"Espainin Hiber, morning and night, 7 days",
"Puissangue, morning and night, 7 days",
"Dolimon, morning and night, 7 days",
"PAN 40, after meals, 7 days",
"Pan 40, after meals, 7 days",
"Paracetamol, after meals, 5 days",
"Azithral, before meals, 3 days",
"Pansec 40 mg, before food, 5 days",
"Etoshine 60 mg, after food, 5 days",
"Nicoprotine Drop 15ML, 2 times a day, 7 days",
"Depura Kids Drop 15ML, once a day (morning)",
"Calpol (Pedia) Drops - 15ML, sos",
"OR-76 Som, once a day, 13 days",
"Lycored, twice a day, 12 capsules",
"Rentre D 160, once a day, 21 tablets",
"Vomitrat-op, once a day, 31 tablets",
"Di Aregest 3R, twice a day, 100 tablets",
"Vistogreat, unspecified, 5 sachets",
"Supraper-0-gel, 3 times a day, ongoing",
"Cilacar tc 12.5 tablet, once daily, 1 month(s)",
"Nicip tablet, twice daily, 1 month(s)",
"Gubi NT 100, before meals, 10 days",
"Nurich M., after meals, 14 days",
"Micel 14/12, after meals, 10 days",
"Panosuc-Don, before breakfast, 14 days",
"Fer 6, once daily, 4 days",
"Asthatin, every 3 hours, 2 days",
"AZITHRAL-XL 200, once daily, 4 days",
"DOM-DT 10mg, every 12 hours, 2 days",
"Cap Famocid (40), before breakfast, 14 days",
"Coop Innorfol Ds, daily, 14 days",
"Zincosit, after lunch, 14 days",
"Lupiheme, after dinner, 14 days",
"Zecal Gold, morning and night, 8 days",
"Gestoff SR (300), after breakfast, 14 days",
"Vivamon, after lunch, 14 days",
"Allegra 180, after dinner, 10 days",
"sol, 1-0-1",
"T Alet 6m, 1-0-1",
"Tpolude, 1-0-1",
"T Mont ce",
"-R. Ascout",
"MONTAIR 5mg tablet, morning and night, 5 days",
"Hy Machery Junior, morning and night, 5 days",
"Budecort New 1mg Respule, morning and night, 5 days",
"LIFE'S ON D, before breakfast, before dinner, to continue",
"TAB GABAPENTIN NT 400/10, 10 am, 3 pm, bedtime (10 pm), to continue",
"CAP QUENTIA BD [MULTIVITAMIN], to continuePregabid-ME 75mg, morning and night, 14 days",
"Aceclofenac 100mg, morning and night, 14 days",
"Ban 40, morning and night, 7 days",
"Tab. Criminale plus",
"Tab Olyvit m, morning 10 a.m. to 2 p.m., evening 5 p.m. to 10 p.m.",
"SYF Kapry Exp., tds",
"SYR Cakine, hls",
"Syp Angmolistet",
"Amoxicillin, 1-0-1, 5 days",
"Rantac, 1-0-1, 5 days",
"Paracetamol, 1-1-1, 2 weeks",
"REPAN D, morning and night, 4 days",
"ZERODOL P, morning and night, 20 days",
"DMR 30, morning and night, 94 days",
"L DIO 1 M, morning and night, 4 days",
"CMAXY GOLD, morning and night, 90 days",
"Tab Razo (20 mg), 1 time daily, 30 days",
"Tab Menoctyl, 1 time daily, 30 days",
"Tab Cizaspa, 2 times daily, 200 days",
"Tab TENOFOVIR ALEFANAMIDE 25mg, 0-0, to continue",
"PAN 40, before breakfast and dinner, 15 days",
"REGGIE NEX PRO-L, before breakfast and dinner, 15 days",
"MET NATURALARE, before breakfast and dinner, 15 days",
"PAN 40, morning and night, 10 days",
"Cepodem 200, morning and night, 10 days",
"Kaya Top-Nerm� x6, morning and night, 10 days",
"Ban 40, morning and night",
"NA",
"NA",
"PAN 40, morning, 7 days",
"Paracetamol, morning and night, 3 days",
"Amoxicillin, morning and night, 5 days",
"Tab Esocafe, before breakfast",
"Tab somfy 1, morning and night",
"Cap Evion 400, before breakfast",
"Tab. Carfine 1, morning and night",
"Tab. Etobrush T 1, morning and night",
"Drop Nasoclear Nasal",
"Drop Crocin",
"Drop Maxtra",
"Respule Levolin (0.31 mg)",
"Augmentin 625mg, twice a day, 5 days",
"Pan 40mg, once a day before breakfast, 5 days",
"Zerodal-P, thrice a day, 3 days",
"Rebert DSR, 2-2-2",
"Paracetamol 650mg, 1-",
"Metroxy1 400mg, bbf",
"Aceite, 0-0-1, 5 days",
"REST",
"Pantosec, 1-0-1, 5 days",
"corexx MIR, 5 days",
"Colafix",
"Olocal, 0-1-0, 5 days",
"Pan 40, bbf (before breakfast), 6 days",
"Zipedroo, bd (before dinner), 6 days",
"Macbok protein powder, 3 times a day",
"T. Protonese, 1 tablet, 6 weeks",
"Eunorm sachet, 3 times a day",
"T. Dolow, morning and night, 10 days",
"T. RA 20, morning, 10 days",
"Cap. Pegaba-m 75, morning, 10 days",
"SULTA, morning and night",
"INT PAI, morning and night",
"PAN 40, morning and night",
"Tab. Evion-LC, 5 days",
"Tab. Venvit, 10 days",
"Tab. Calpol 650mg, 3 days",
"BEE 25",
"FUL",
"T. TOFACINITA TOFADEZ, 1-1-1, 7 days",
"MESACOL-OD 1.2, (continu)",
"Domstal, 3 times a day, 3 days",
"Ecogram GG, 1 time a day, 5 days",
"Carmicide Ped, 3 times a day, 5 days",
"Inj Bett, stat",
"Cap Finacid Dsr, once a day, 30 days",
"Cap Pink Xt, once a day, 30 days",
"SYP SOVENTUS JR(0.5 MG / 5 ML), 5 days",
"SYP LEVOCET M KID(2.5 MG / 5 ML), 5 days",
"Nebulization, bd, 10 days",
"vomik, al",
"Sur. Adward (220), 1-0-1",
"S.P.200-1, occasional, rx",
"SBR-200, dr. brijesh gupta (b.h.m.s.)",
"NA",
"NOMIN CARE",
"T. Letrohope 5mg, � 5 days",
"PAN 40, morning and night, 6 days",
"Cheston Cold, morning and night, 6 days",
"Azithromycin, morning, 3 days",
"TIX",
"Impression den",
"Cementation don",
"Veloz (20), 1-1, 15 days",
"Ecospres-Av(75/10), -1",
"cosabrad/ Loslas (5), -1",
"Azmaids",
"Vyamada (200), 1-1, 12 days",
"Oxhamel 10/1gm",
"E Corbus (2.5)",
"zylori (100)",
"Dutan T(X-1)",
"Thystime(375)",
"Dytor 20",
"Dytor 10",
"Ca+ CD3, continue",
"Lanoxin (.25)",
"Reftar Wetrosave",
"PAN 40, daily",
"Visnet 6128 pp, daily",
"Lipids, daily",
"Nirmadhu Tablet, after food, for diabetes",
"Prashaantha Tablet, after food, for bp",
"Coldol, 4-64",
"Montair / Telekast / Romilast, i tab, once only - evening, 30 day(s)",
"Budecort / Budate inhaler 100, 12 hrly (fix dose), 5-15 day(s)",
"Cetzine Syp, night, 5-15 day(s)",
"Budenase Nasal Sprey, 12 hourly, 5-15 day(s)",
"Protinex Drigmal powder, 12 hrly, 45 day(s)",
"E Levolin / Salbair inhaler, 3 doses stat, 1 dayisa",
"Levolin / Saltiair inhaler (severity of cough), 2-4-6-8 hrly, 5 dayis",
"Toba[Sun] 0.3 %W/V, 3 times per day, for 4 day/s",
"Syrp Relent Plus[Dr], 3 times per day, for 3 day/s",
"T. PAH 20mg, 3 months, 3 months",
"Calpol 120, 3 times a day, until symptoms subside",
"Levolin, twice a day, 3 days",
"ECOSPRIN 75 mg, morning and night",
"ATORVA 20 mg, morning",
"PAN 40 mg, morning",
"Diofos, morning and night, 5 days",
"Entro Hora, morning, 5 days",
"S Weltam, morning, 5 days",
"Colinexmo, evening, 5 days",
"Ensure Rice, evening, 5 days",
"Pan 40, after meal, 5 days",
"Paracetamol 500mg, after meal, 5 days",
"Tossex SF, after meal, 5 days",
"ELIQUIS 5mg, morning and night, 1 month",
"ROSEDAY A 75/20mg, night",
"CONCOR COR 2.5mg, night",
"SACURISE 50mg, morning and night",
"DAPAHENZ 10mg, morning",
"ALDACTONE 50mg, morning",
"BRIVASURE 50mg, morning and night",
"NA",
"NA",
"NA",
"Tab upra 400, bd, 20",
"3 cap Cerealx, bd, 20",
"4 Tab Livoganz, bd, 20",
"Anyarpan 18 (12.5), morning and night, 31/03/2013 - 24/03/2023",
"Thyromarm (62.5), morning, as per requirement",
"Contorcer (1.20), morning, as per requirement",
"Dimed, automatic, as per requirement",
"Vego (0.3), morning, as per requirement",
"Forglip-1 (20/150), morning, as per requirement",
"Zaphrt 18, morning, as per requirement",
"Neptun (50), morning, as per requirement",
"Astor, morning, as per requirement",
"Capiret & (35), morning, as per requirement",
"Eucalmine, morning, as per requirement",
"Incomplete, 3 times a day, undefined",
"Levipiv 750, 10-1-0, 7 days",
"Oxeral 300, 10-0-0, 15 days",
"PAN 40, morning and night, 10 days",
"Paracetamol, morning and night, 7 days",
"Antitussive syrup, morning and night, 7 days",
"Syrup Ibugesic Plus 45, 3 times a day, 7 days",
"Youmo-Paracetamol 125mg, 3 times a day, 7 days",
"Syrup Meftal P, 3 times a day, 7 days",
"Syrup Maxtra, 3 times a day, 7 days",
"Syrup Kufril LS, 3 times a day, 7 days",
"Syrup Ondem, 3 times a day, 7 days",
"Vial EnteroGermina, once a day, 7 days",
"T. Linapride M (2.5/500), morning, 30 days",
"T. Panon-D, night, 30 days",
"T. Ke todan, morning, 15 days",
"T. Nacsare, morning, 31 days",
"T. Midotab 2.50g, not mentioned, 15+15 days",
"T. Pruloo 3 2mg, morning, 15 days",
"Ark get (LA), not mentioned, -",
"T. Benitab 4mg, morning, 15 days",
"Syp. DuphalacC, -, 10ml",
"T. Acicurb 19m, not mentioned, 15+15 days",
"& Panton",
"8. 97554-4",
"PAN 40, morning",
"SLRT",
"Delinical Abd Pelosis",
"Cap Synkron 513, 15 days, 10",
"Tab Celdet 6 mg, 10",
"Tab Ricert 20 mg, 10",
"Ssp Welleine -P (250), every 5 days",
"Mekut DS, only, 5 days",
"Top Duit DDS (200)",
"Unknown",
"Tafi, morning, 1 day",
"Be Well, morning, afternoon, night, 1 day",
"Foly's Catluter, morning and night, 1 day",
"Suspension Ibukind Plus (IBUPROFEN(100 MG) + PARACETAMOL(162.5 MG)), after food, till required",
"Inj: Imax 5",
"Normal Saline/ Tv set/ Blue Conula/ Disposable byring & Decale -0",
"Syp. Ux Joy-1",
"Syp. Descorange-1, a",
"Syp. Ibugesic PLUS, 1-0-1, 5 days",
"DEPURA, 1-0-1, 4 weeks",
"ULCIWELL DSR, before dinner, 10 days",
"THEOLIFE, after breakfast, after dinner, 1 week",
"LC BIT MK KID, after dinner, 1 week",
"zental 400, after dinner, stat",
"TUNEVIT, after lunch, 10 days",
"Mebit plus, stat, intramuscular",
"Compas",
"OT GEMER K VL",
"DJ Eloum or",
"Pan 40, once daily, 10 days",
"Augmentin 625, twice daily, 10 days",
"Montek LC, once daily, 10 days",
"TAB PAN 40, once daily, 5 days",
"Tab . Signoflom, once daily, 5 days",
"Shelcal, once daily, 5 days",
"Dicorate-ER 500mg, 1 - 0 - 0, 1 month",
"Mebodep-CD3, 1 - 0 - 0, 1 month",
"Tryptomer-25mg, 0 - 0 - 1, 1 month",
"Writex 7G, 0 - 0 - 1, 1 month",
"Napra D 500mg, as required",
"Syrup Zyrcold, twice a day, 5 days",
"Syrup Montek LC Kid, once a day, 2 weeks",
"Alocepodem 100-DT, twice a day, 10 days",
"Sp Asthalin, as needed",
"Ciplamid - 3, at bedtime, 5 days",
"Dato, 1-",
"Frend, 1-",
"E Flo Eyedrop Eye Drops, till 02 oct 2023, both eyes",
"E Flo Eyedrop Eye Drops, till 04 oct 2023, both eyes",
"E Flo Eyedrop Eye Drops, till 06 oct 2023, both eyes",
"E Flo Eyedrop Eye Drops, till 08 oct 2023, both eyes",
"HYLA OINTMENT Eye Ointment, both eyes",
"Capsule ITASPOR-SB, 1-0-1, 7 days",
"NAILROX CREAM, 1-0-0, 21 days",
"Tablet JUPISHINE, 1-0-1, 1 month",
"Azithromycin 500mg, after meals, 3 days",
"Allegra 120mg, before meals, 7 days",
"Paracetamol 500mg, after meals, 3 days",
"CAP CEFTURSN, 0-0-1, 5 days",
"CAP ECOFLORA, 1-0-0, 15 days",
"CAP RyTH MIXA, 1-0-1, 13 days",
"TAB EGOSPO75, 1-0-0, 7 days",
"Cap Somtraz 1, 10 days, 10 days",
"Tab Sompraz 40, 15 days, 15 days",
"Val Leemide 25, 7 days, 7 days",
"Gaviscon, after meal",
"Tas Metosantan 40/25, 1 month, 1 month",
"Tab Sompraz 20, 20 days, 20 days",
"Dolo, as needed",
"PAN 40mg, after meal, 2 days",
"JUMP, before meal, 2 days",
"Putop-2, after meal, 3 days",
"Gerne MPS, before meal, 3 days",
"Somprag 40mg, before meal",
"TI CINTARRO, after meal",
"Sur Sumafilo, after meal",
"NORMAXIN, after meal, 3 days",
"PAN 40, before breakfast and dinner",
"NAD, as needed",
"TIBROZEN, once daily",
"Pan 40, bd, 2 months",
"Cap Doxy 100 mg, bd, 2 months",
"Cap Vizylar, bd, 2 months",
"Jab Mekogyl 400mg, bd, 2 months",
"Tablet Minti AZ, morning and night",
"APPSMarcas 250 MG, morning and night",
"Tablet Diominie Dea, morning, afternoon, and night",
"Tablet Akilos p, before breakfast, after lunch, and evening",
"Syrup Coscopin Plus, before breakfast, lunch, and dinner",
"Capsule Happi D (20 & 30), morning",
"T Gluconet Mini, morning and night",
"Alco-pm, before bedtime",
"HTN + RX",
"Telmiland, after lunch",
"CT",
"Telmisartan 40/6.25, after dinner, 10 days",
"Carbiphage XR 500, after breakfast and dinner, 10 days",
"Bio-D3 Max, after lunch, 7 days",
"Rosuvas Ford 10, after dinner, 10 days",
"Febrap 40, after breakfast",
"Flokind 0.4, before bedtime, 5 days",
"Merogal GR 600, after dinner, 10 days",
"POLYCLAVE 625 MG, twice daily",
"BENZ, once daily",
"TUSQ LOZENGES, four times daily",
"Paracetamol, 3 times a day, 5 days",
"Montair-By, 1 time a day, 5 days",
"Microcap 200, 1 time a day, 5 days",
"Dolo 650mg, 5 times a day, full course",
"Enterogermina Suspension, 3 days, full course",
"Dynapar Injection, 5 days",
"Tab. Veronal SP, 5 days",
"Tab. R-Drive 20, 5 days",
"PAN 40, morning and night, 5 days",
"Paracetamol, morning and night, 5 days",
"Comger Talegront Duur, twice a day, 5 days",
"Pastadosr, twice a day, 5 days",
"Tra de Cetinsta CV, twice a day, 5 days",
"Antle binder -CD, twice a day, 5 days",
"AMARYL 2 mg, morning and night, till review",
"VILDAPRIDE 50 mg, morning and night, till review",
"ROCROS 10, afternoon, till review",
"NEXITO FORTE, afternoon, till review",
"PEVESCA PLUS, afternoon, till review",
"TH Eltroxin, 1-0-1, 30",
"[missing medicineme], 1-1-1, 30",
"[missing medicineme], 1-1-1, [missing course duration]",
"Paracetamol, morning and night, 4 days",
"Amoxicillin, morning and night, 4 days",
"Omeprazole, morning and night, 4 days",
"Novamente, morning and night, 10 days",
"PAN 40, morning and night, 10 days",
"Calcium, morning and night, 10 days",
"Tas-Mittel Spas, after breakfast, 5 days",
"Tax-Domperidone, after breakfast, 5 days",
"Tus-Partie 500mg, after lunch, 5 days",
"Tab Mahacef 200, after meals, 5 days",
"Tab Liv 52 DS, after meals, 5 days",
"Tab Becosules Z, after meals, 5 days",
"Sur. Oxipad 1001",
"Folsafe-L, once daily, 30 days",
"Feri B. wash, once daily, 21 days",
"F. Alde, once daily, 14 days",
"Terbinafine 500, once daily, 20 days",
"Inj Imax 5, as prescribed",
"Normal Saline, as prescribed",
"Syp Ux Joy, as prescribed",
"Syp Descorange, as prescribed, a",
"Syp. Duphalac, 0-0, 7 days",
"Lignocaine jelly 2% (xylocaine), tds",
"J- Sitcom (forte), 0, 1",
"clo BIL Pedal odema",
"CALLESR, 2-2- 2, 1 month",
"Unimot, 1 month",
"D-fix, 1 month",
"Ultracet, 20 day",
"Lipiland - F, 1 month",
"PROLOMET XL 50MG TABLET, before breakfast - daily, 6 weeks",
"SZETALO PLUS TABLET, bed time - daily, 6 weeks",
"LONAZEP MD 0.25MG TABLET, after dinner - daily, 6 weeks",
"ROSUVAS F 10MG TABLET, after dinner - daily, 6 weeks",
"HEPADO, after food - daily, 6 weeks",
"CYRA D CAPSULE, before breakfast - daily, 15 days",
"SURBEX XT TABLET, after food - daily, 15 days",
"CIPCAL 500 mg TABLET, alternate day, 3 weeks",
"Cipcal D3 Granules, once a week, 5 weeks",
"Neurobion Forte Tablet 30's, alternate day, 3 weeks",
"TAB SANOGESIC P, 1-0-1, 5 day",
"TAB CEPODEM 200 MG, 1-0-1, 5 day",
"TAB MONDESLOR, 0-0-1, 5 day",
"CAP SISBONE K2, 1-0-1, 15 day",
"PAN 40, thrice daily, 30 days",
"Ferradol, once daily, 60 days",
"Progesterone, thrice daily, 90 days",
"PAN 40, morning and night, 59 days",
"T Norminol, morning and night, 59 days",
"DL ID, you, morning and night, 59 days",
"T. Reclid� MR 30, bd (before breakfast and dinner), 2 months",
"T. Vildaparido M 50/150, bd (before breakfast and dinner), 2 months",
"Med T Glycomit SR 500, od (once a day), 2 months",
"Peuvent (Rf) Gibi Oleraa bunt, 1-0-1, x 5 days",
"Tab CHYMORAL FORTE, 1-0-1, x 5 days",
"Tab HIFENAC MR, 1-0-1, x 5 days",
"Tab. Altroday, after breakfast, 14 days",
"Tab. Ultramed D, after lunch, 21 days",
"Joe Collamer Plus Idaes, after dinner, 3 months",
"Kup T98M, 1-0-1",
"Backoder M, 1-0-1",
"Pintor Cs, 1-0-1",
"Inj PCM 1, stat, one dose",
"Jaraben, 1-0-1, 6 days",
"Bayar 10%, 1-0-1, 6 days",
"Kehm Pero 1mg, 1-0-1, 6 days",
"Toonceyzong Ty de marketing, 1-0-1, 6 days",
"T. Etibenny Plus, 1-0-1, 6 days",
"T. Metaguy 500k, 1-0-1, 6 days",
"Gymerbal 4151, 1-0-1, 6 days",
"Elucilla DS 6.5, 10-1.10, 14",
"Azgoodx(oc), -, 6",
"Arm.",
"Sumal Plus, twice a day, 3 days",
"Velof-D, three times a day, 3 days",
"Nasoact Nib, once, 1 day",
"Chemamila Pills, once, 1 day",
"Colimex, once, 1 day",
"Betnesol, 2 doses, 24 hours apart",
"Cephalexin, bd",
"Adw",
"pees",
"Vinpocetine",
"PAN 40, morning",
"BP 114168, morning and night",
"Motival, morning and night",
"Taf, before breakfast and dinner, 1 month",
"MONTAIR- fx, 5 days, 5 days",
"Ciplox 500mg, 5 days, 5 days",
"T. Dolo 650 mg, 3 days, 3 days",
"Syp Ascol- 1, 1-0-0, 5 days",
"A well ag, 12 weeks, 12 weeks",
"BP Medication, once daily",
"Vitamin D3, once daily",
"Taxim-O 200, twice daily, 7 days",
"AS PLV, once daily, 10 days",
"TAB RAZO 20mg, 1-0-1, 30 days",
"TAB MENOCTYL, 1-0-1, 30 days",
"TAB CIZASPA, 1-0-1, 200 days",
"Tanto 75M, 1-0-0",
"DIT Enlargafly, 1-1-1",
"Lase & Gutem, 1-0-0",
"Labelsc, 1-0-1",
"Istrot, 1-0-0",
"Tal Eldict, 1-1-1",
"Plain xy all, 1-0-1",
"Chut oha",
"Stomatab.c, before breakfast",
"Dksix.c, before food",
"Elathy.c, before breakfast",
"Kasa.c, before food",
"Swasawa.c, before food",
"Ceralgin, after food",
"Kasa kasayam, before food",
"Vasilha, before food",
"BarronilG, before food",
"Dermittal, before food",
"Livoral, before food",
"PP.C, before food",
"Decofycin, before food",
"vasa Leg, after food",
"vizyme.c",
"MANYATA, morning and night",
"Tab. WAXDOM 500 mg, morning and night",
"Tab. TRYPTOMER, morning",
"No-Mark ointment",
"Alloederim",
"Pan 40, morning and night, 5 days",
"Dolo 650, morning and night, 5 days",
"T. Janumet 50/500, daily",
"T. Remyelin D, weekly, 8 weeks",
"T. D sise 60k, weekly, 8 weeks",
"Meftal P, as needed for fever, repeat after 6 hours if required",
"Ascoril LS, thrice a day, 1 week",
"Ambroxol + Levosalbutamol, thrice a day, 1 week",
"Telekast L Kid, once a day at night, 15 days",
"Azee XL 200, once a day, 5 days",
"Polyethylene Glycol + Sodium Bicarbonate, once a day, 1 month",
"Bandy, once a day tonight and repeat after two weeks",
"Albendazole",
"Cyclopam, as needed for pain abdomen",
"Simethicone + Dicyclomine, as needed for pain abdomen",
"D3 must Nano Shots 60 K, once a week, 10 weeks",
"Cholecalciferol 60,000 IU, once a week",
"Aptimust, once a day before food, in the evening at 7 pm, 3 months",
"Cyproheptadine",
"TRYptoMER lome, 3 times a day, 3 months",
"MSTRONG, 1 month",
"THMANO 3, 1 month",
"PAN 40, bd, 5 days",
"CALCIUM SUPPLEMENT (66k), bd, 400k",
"JUNCAL-P SUSPENSION, bd, 2 days",
"SANOGESIC P, 1-0-1, 5 days",
"CEPODEM, 1-0-1, 5 days",
"MONDESLOR, 0-0-1, 5 days",
"SISBONE K2, 0-0-1, 15 days",
"RSS Syp. oruzyme, bd",
"Syp offarmarim, bd, 12 days",
"Esp. Mexico-forte, bd, 5 days",
"Paracetamol, tds, 5 days",
"Budesonide, bd, 5 days",
"Im Mikastar 500, morning and night, 10 days",
"Zanocin-02, morning and night, 6 days",
"Sponolac-AS, morning and night, 6 days",
"Ban 40, before breakfast and before dinner, 1 month",
"Cap Protete 15, before breakfast, 1 month",
"Tab. comoufer vor �s, before dinner, 1 month",
"Typhilet gm, after dinner, 1 month",
"Tab. Unizyme, before breakfast and before dinner, 1 month",
"Tab. welcheline 2CB, before breakfast and before dinner, 1 month",
"Cap uprise 13 60, before breakfast, 1 month",
"PAN 40, 1, 14",
"Esocet-D",
"Clingen Forte, 0-1",
"PAN 40, bd, 7 days",
"TAB MEFTAL SPAS, as needed",
"VERTIN, once daily",
"Tab. Vetory, x 5 days",
"Tab. Acilac 150mg, x 5 days",
"Depo medrol, bd � 5 days",
"Tab. Auloc 15, br 3d � 5 days",
"PAN 20, after food, 10 days",
"Nauser, as needed",
"A Deslusion, as needed",
"Mildergashi, as needed",
"PR-112, before food, 10 days",
"liderin, as needed",
"TAB DILNIP 5 MG, after meals",
"TAB PRIMEZOLE 40 MG, before meals",
"TAB GLARISURE, before meals",
"Cap Sampras, morning and night, 2 weeks",
"Tab Sampras, morning and night, 2 weeks",
"Syp Fueral, before dinner, 2 weeks",
"Pan 40mg, before breakfast",
"Tramadol, as needed",
"Ilike",
"T. Livogen, morning and night, 5 days",
"T. cltrocaples, morning and night, 5 days",
"Macprotein powder, morning, 5 days",
"Zifi (200), morning, 5 days",
"Proroms9 (300), morning and night, 5 days",
"vizylac, morning and night, 5 days",
"Econoroom, morning and night, 5 days",
"Proff vachet, morning and night, 5 days",
"Flagyl 400, morning, 5 days",
"Flagyl 1000, morning, 5 days",
"Livogen, morning, 5 days",
"Fornig, morning, 5 days",
"Tustacoplar, morning, 5 days",
"jarix, morning, 5 days",
"Proffane 19 (300), morning and night, 5 days",
"Incomplete",
"Incomplete",
"Incomplete",
"NA",
"NA",
"NA",
"Anal Blog Ass, 1-0-0",
"Crabapun 100, 1-0-0",
"Zenopa 00, 1-0-0",
"Dolopar Creel",
"PAN 40",
"Triple H",
"Cap Ontoxid HC, once daily, 30 days",
"Desowen Cream, as directed, as directed",
"Jakta forte ointment, as directed, as directed",
"Job Allegra 180, as directed, as directed",
"Tab. DIZIBEAT, 5 days",
"Lesum, after food (9pm)",
"ECG, 5 days",
"Tab. Typerom, after meals, 5-7 days",
"Tab. Pop 120, before breakfast",
"Tab. APF 159, before breakfast",
"Inj. Insulien 50/50, after meals",
"Inj. En Sugen 30/70, after meals",
"Tab. Guywase 5, after meals, 30 days",
"Tab. Torhup 50, after meals",
"Tab. CruxIT 10, after meals",
"Tab. J. C. HOPACE 25, after meals",
"Tab. T. NUROKIND -LE, after lunch, 10 ml x 2mts",
"Syp. Cremaptin Bins, after meals",
"PAN 40, bd, 30",
"Renova GM, bd, 60",
"VoliDer, -",
"Amoxylaw, after breakfast",
"LK-D, before dinner",
"Mountain, before dinner, 6 days",
"Parto P-D, before breakfast, 6 days",
"Syr. on & on. Cough, after meals",
"Toul, before bedtime",
"Galvus.Met, bd, 1 week",
"Telnyk CH (40/12.5), od, 1 week",
"starpress XL 100, od, 1 week",
"Rosuvas, od, 1 week",
"Febustat, od, 1 week",
"Thyronorm, od, 1 week",
"Nasal Spray, bd, 15 days",
"T. Lenono, bd, 15 days",
"Anniy Plus 200, od, 5 days",
"1. Etaliler",
"-1. cease-sp",
"mysi cialement faut",
"Laformin GV, 0-0-1, 3 months",
"Sitahenz D 5/50, 1-0-0, 3 months",
"Enzoflam, 1-0-1, 5 days",
"Mandyic party",
"L.S.Shaw.",
"out Dokln-1",
"HIFENAC MAX TABLETS 10'S, 1-0-0, 1 month(s)",
"IT MAC 100MG STRIP OF 10 CAPSULES, 1-0-0, 1 month(s)",
"TRIBEN PLUS CREAM, once daily, 3 days",
"NEMOCID, twice daily, 2 days",
"NST (Non-Stress Test), weekly, ongoing",
"Depamethasone, 24 hours, ongoing",
"ALDACTONE 100 MG, after food - daily, ongoing",
"DIANE 35, after food - daily, 21 days",
"Tab FRANCAC CZ, 60, 81",
"Tab PEPFERRIN, 60",
"3 Tab SQADD1 2L",
"(a) Tab SUGAVILDASO, 120",
"Tab OD2 sunday,, 15 days 1",
"Tab CABERDOPA 0,5",
"2) COTIDOL Soap, te 0 0%",
"8 ZOBIDOLE Lotcon",
"OMNACORTIL 20MG TABLET, after breakfast - daily, 5 days",
"PANTIN 40MG TABLET, before breakfast - daily, 10 days",
"GLYMED 100ML LOTION, after bath - daily, 1 month",
"CLONATE 20GM OINTMENT, after bath - daily, 10 days",
"ALENIX 5 MG TABLET, after food - daily, 10 days",
"PAN 40, before breakfast and before dinner, 4 cycles",
"PAN 40, bd, 10 days",
"T. Chycometsk, al",
"Pigelmin, bbf",
"T. SitzkemxR, bd",
"Clan, bd",
"T. XEPAMH 100, bd",
"T. Zyfoly (3) 010, bd, 30 days",
"T. Ampnoch, bd",
"Cap. odhinab 100, bd",
"Sprig81af, al, 2 months",
"Ilal in Illera 10mg i.v. jeg0 ml, iv",
"Iv. auch 2 horas, iv",
"Avil, H. coltiv Het",
"Guzee",
"Poz Vala vuol 212",
"Apelo poglute -ai, tt, 28",
"ARV, 0, 3 7 , 14 28",
"lesibil 250 151, 300",
"opetal 300 100, (150)",
"NA, 50",
"TAB. OLYNZ M 500MG, 3 months",
"TAB. TELMIKIND 40 MG, 3 months",
"CAP. ECOSPRIN AV 75MG",
"Pan 40, morning and night, 14 days",
"Paracetamol, morning and night, 5 days",
"COAX, morning, 1 day",
"VILDAPHAGE-M, 01-0-0, 1-5/1120",
"AMLOSAFE, 01-0-0, 120",
"Cosader, 01-0-0, 16",
"Syp. Phenycip, 3 times a day, 7 days",
"Breathawaysal drop, 3 times a day, 7 days",
"Salvin Cola E, bd, 12 weeks",
"swiss OK, 001",
"PAN 40, morning and night, 7 days",
"Ecoprin 75mg, morning and night, 7 days",
"Ton Super 300mg, morning and night, 7 days",
"PAN 40, morning and night, 7 days",
"Orden MD, after lunch",
"Ban 40, 3 weeks, 3 weeks",
"Rabzia D, 10 days, 10 days",
"Entrygen-DS, 10 days, 10 days",
"Ru, sup Rinifol, > todail, 10w as adesed",
"di TreLlOR Lanol (30)",
"Gup Cyclopen, 7.5 w/ 60r, 7days",
"Guy Mofasi, 6",
"Waysone E a drop",
"Rx, T.prosyn 250, -",
"T. JelloR lanzo1 (15), quiral myositis",
"voreRantaes/ vovi Ran, 7",
"Doxt-SL Capsule, daily, 2 weeks",
"Glocin Gel CLINDAMYCIN (1/4 %) Gel, 1 time, 4 weeks",
"Minoz-BPO Gel Adapalene (0.1 %) + Benzoyl Peroxide (2.5 %) Gel, 1 time, 4 weeks",
"Ahaglow S Foaming Face Wash 100 ml, 2 times, 4 weeks",
"Acnemoist Cream 30 g, 2 times, 4 weeks",
"Hospipow zesto cold 11, bbf, 10 days",
"Otalet, bbf, 10 days",
"Desolid 10, al, 10 days",
"Pan 40, morning, 10 days",
"Gemino-300, morning and night, 100 days",
"Syp Augmentin DDS, 2-3 times daily, 1 day",
"Syp Thinic, once at bedtime",
"Crocint - 6 ml, morning and night",
"Angiglam",
"PAN 40, morning and night, 3 days",
"MEFTAL SPAS, morning and night, 5 days",
"ENTEGERMINA, morning and night, 7 days",
"Pan 40, morning and night, 10 days",
"Zimoces, morning and night, 10 days",
"Unobiotics, morning and night, 3 days",
"Pan 40, morning and night, 14 days",
"Paracetamol, morning and night, 10 days",
"Cough Syrup, morning, afternoon, and night, 7 days",
"Bluiban, bbf",
"chent hans, 001",
"Rib Belt, bd",
"T. Rifagut 550, morning and night",
"Bifitar ho, morning, afternoon, and night",
"Syp cinovic sul, morning, afternoon, and night",
"Cap Beusule 2, morning, afternoon, and night",
"syp sucrafil 0, morning, afternoon, and night",
"Soje, before breakfast, before dinner, 10 days",
"Vibalt DS, before breakfast, before dinner, 5 days",
"Raciper D, before breakfast, before dinner, 10 days",
"Tar lepodem, 1-0-1, 7 days",
"TAB. COLLAFLEZ PRO PLUS CAP., after food - daily - 5 days",
"TAB. AFLAROSE PLUS TAB, after food - daily - 5 days",
"TAB. ROCKBON, after food - daily - 5 days",
"TAB. ULTRAKING, after food - daily - 5 days",
"GUFIBIS OIL 20ML, daily - 5 days",
"TAB. HQTOR *, after food - daily - 5 days",
"CAP. PRECOOL *, after food - daily - 5 days",
"Optojest, 30 days",
"Preynocure, 30 days",
"Galan ma De, 30 days",
"Calcimax Forte, jul",
"Bandy plus, 70ml",
"Meganeuron-MF, after breakfast",
"Telista 20, after dinner, 20 days",
"Pantospe D&R, after meals",
"Zeprest plus, after meals",
"Besohow 2.5 Los, before bedtime",
"Tab. Nejor, bd, 1 month",
"Tab. Defical, bd, 1 month",
"Ij Osteo D3, stat, 1 month",
"Tab Cefoxim 500, twice daily, 5 days",
"Tab Pan 40, once daily, 10 days",
"Tab Zerodol SP, twice daily, 5 days",
"Sepia Im (4)",
"All. capa 200",
"Petro 200",
"April 200",
"SGH 2020, once only - evening, 30 day(s)",
"Coldol Cream 250 ml, night, 0",
"Montair / Telekast / Romilast - 5 mg, 0, 5-15 day(s)",
"Budecort / Budate inhaler 100, 12 hrly (fix dose), 5-15 day(s)",
"Cetzine Syp, night, 5-15 day(s)",
"Budenase Nasal Spray, 12 hourly, 5-15 day(s)",
"Protinex Dry Mix, 12 hourly, 45 day(s)",
"Levolin / Salbair inhaler, 3 doses stat, 1 day(s)",
"Levolin / Salbair inhaler (severity of cough), 2-4-6-8 hrly, 5 day(s)",
"TAB OMNACORTIL 2.5 mg 100, before breakfast and before dinner",
"TAB CARDIVAS 6.125, before breakfast and before dinner",
"TAB FOLVITE 5mg 100, before breakfast and before dinner",
"TAB RABLET 20, before breakfast, 15 days",
"CAP. CYCLOSPORIN 25, after lunch",
"CAP. DANAZOL 50, after dinner",
"TAB Du tor 5, after dinner",
"TAB Amlodip. 5, before breakfast",
"IM AUGPLAT 500 once weekly (Tuesday), before breakfast",
"TAB Zincovit, before breakfast and before dinner, -",
"ACID NIT, bd, start now",
"SCROPH NODOSA, bbf, morning",
"HAMAMELIS, al, afternoon",
"MYRISTICA, 001, night",
"NA",
"NA",
"NA",
"Montek Lc, morning, night, 6 days",
"Azithromycin, morning, 3 days",
"Paracetamol, morning, afternoon, night, 5 days",
"Syp Ibugeste, 3 times a day, 10 days",
"A02, 1-0-1, 5 days",
"Tab Foldege D, bd, 5 days",
"Pu Sampai 7mg, od, 25.26 days",
"PAN 40, morning and night",
"Forvite, night",
"Drogayde, morning and night",
"PAN 40, bbf",
"Paracetamol, bd",
"Dufladac 800, 3 times a day",
"Dechilea Jaim",
"Ro",
"x-1 -x 5deup",
"Rifagut 400 Tablet, after food, 10 days",
"Rifaximin 400mg, after food, 10 days",
"Dobesil 500mg Capsule, after food, 3 months",
"Calcium Dobesilate 500mg, after food, 3 months",
"Stone 1 B6 syrup, after food, 2 month",
"Enterogermina, after food, 5 days",
"pine, morning and night, 1-1.5 month",
"Mourefloor",
"Paulus, bd",
"Polybir, bd",
"C",
"Paracetamol, daily, 5 days",
"Cetirizine, daily, 5 days",
"Antacid, daily, 4 days",
"AVIRIT, 5 5, 5 days",
"Paracetamol, morning and night, 5 days",
"Montair-By, morning and night, 5 days",
"Microcap 200, morning and night, 5 days",
"CALPOL/CROCIN/PARACETAMOL DROPS, 6 am, 12 pm, 2 days",
"Hiper MK",
"Tipau 40mg, 2 days",
"Dicloget 401",
"Jij Methy Cobal 1 amp alt day, alternate days",
"Das Deterrol Oncealle x 100ks, daily",
"Vice-M (500)",
"Das Stalit-D, daily",
"Dos Tri olmasa@ 41",
"Das Enite (40), daily",
"Pan 40, morning and night, 7 days",
"Sinarest, morning and night, 7 days",
"Entero-Germ, morning and night, 7 days",
"PAN 40, bd, 21 days",
"DESO ALLERGY, bd, 21 days",
"CALCIUM, bbf, 21 days",
"Tenovate cream, 3 weeks",
"HCQS (400g), 3 weeks",
"Tab oxyflam MR, before breakfast and before dinner, 20 days",
"Cap Oxyorb LS, before breakfast and before dinner",
"Cap OXY-Q- 300, before breakfast and before dinner",
"Rb INOTOR 5, before breakfast and before dinner, 20 days",
"Tab Acoxia MR, before breakfast and before dinner",
"Cap Hbneuron PLUS, before breakfast and before dinner",
"Tap Alrical Gold, before breakfast and before dinner",
"Tab Mega carnit-, before breakfast and before dinner, 20 days",
"Gesic Liniment for massage",
"Zerfan MPS 10ml 50%",
"Syrup Zyrcold, twice a day, 5 days",
"Syrup Montek LC Kid, once a day, 2 weeks",
"NA",
"NA",
"NA",
"QIng. Didlo-1, tid, 3 days",
"zibi 200, bo, 3 days",
"TUS Q Dx-1",
"Pan-40, bd, 3 days",
"A+o2, 5 days",
"Tab. Moxyrin cu. 625mg, before food, 5 days",
"Tab. Raberin.D, before food, 5 days",
"Tab. Montriy-le, after food, 5 days",
"Tab. 0010-650-> 505",
"TAB Darf 4mg, after breakfast, 3 days",
"TAB Rosucoup 10 mg, before dinner, 1 year",
"VENUSIA MAX 300ML LOTION, daily, 30 days",
"ATODERM MOUSSANT, daily, 30 days",
"MOMATE 15GM CREAM, daily, 30 days",
"XYZAL 60ML SYRUP, daily, 30 days",
"TACROZ FORTE 10GM OINTMENT, daily, 30 days",
"TAB PAN 40, 1 daily, 5 days",
"Tab Signoflom 1, 1 daily, x5",
"Shelcal, xxo",
"Tab Cepodem, bd, 5 days",
"Tab ASTM, od, 5 days",
"Tab Panum D, ac, 10 days",
"Syp Ato 2, bd, 7 days",
"Syp BAPC, bd, 7 days",
"Tab SA 100, ac, 5 days",
"Tab Etowin 60, after breakfast and after dinner, 10 days",
"Tab Ban 40, before dinner, 10 days",
"Calpol drops, every 6 hrs",
"PFS TRESIVAC[SERUM], once",
"INJ MENACTRA[SANOFI], once",
"HbA1c",
"High Sensitivity C -Reactive Protein(HsCRP)",
"Iron Study",
"Tab. AlfsNC, 1-0-1",
"Tab Octobix, 1-0-1",
"Tab Ler15, 1-0-1",
"Tab Pzae, 1-0-1",
"Tab Euognix, 1-0-1",
"Mamadialitu con1, 1-0-1",
"Ag preg, 1-0-1",
"- FRS/2hPP BS, 1-0-1",
"PAN 40, morning and night, 4 days",
"Paracetamol, morning and night, 4 days",
"PAN 40, after meals",
"Erace, before meals",
"CRP AV 50, after meals",
"O.T. Dolo 650, bd",
"Lorsaid of, bbf",
"Devocetim/ my/cast, bbf",
"T. DISPERZYME, bbf, 5 days",
"Calpol 250mg Tablet, as needed, 5 days",
"Syp Alex Junior 5mg/5ml, 3 times a day, 5 days",
"Predmet, morning and night, 10 days",
"PAN 40, morning, 5 days",
"Paracetamol, morning and night, 24/2/23",
"Pan 40, morning and night, 3-4 days",
"Paracetamol, morning and night, 3-4 days",
"PAN 40, before breakfast and before dinner",
"Morten le, night",
"Defakind, after meals",
"Delafloxacin, morning and night, 5 days",
"EL Cumple MR, before dinner, 10 days",
"Co Codamol, 5 days",
"Co Adtol, 20 days",
"TRIVOLIB FORTE-1 TABLET, 60, 120 bf",
"THYRONORM 150MCG TABLET, 60, 60 bf",
"NA",
"Rabeprazole, 1-0-1",
"Atorvastatin, 1-0-0",
"Ivabradine, 1-0-1",
"Apixaban, 1-0-0",
"Azmoth, 1-0-0",
"Arnoza, 1-0-0",
"Bisoprolol, 1-0-1",
"Zyloric, 1-0-0",
"Ductus T, 1-0-0",
"Dytor 20 em, 1-0-0",
"Dytor 10, 1-0-1",
"Lanoxin, 1-0-3",
"Tonact Plus, 1-0-1",
"Dolonex Dt Tablet 20mg, bbf, 5 days",
"Augmentin 625mg Tablet, bd, 3 days",
"Dompan Tablet, al, 5 days",
"Dolo 650mg Tablet, al, 5 days",
"T. Dzotute 10mg, once a day",
"Rest atall",
"LUKOTAS HD TABLET, after dinner - daily, 20 days",
"MONTEMAC AL, after dinner - daily, 20 days",
"AMBROXOL 75 MG, daily, 20 days",
"LEVOCETIRIZINE 5 MG, daily, 20 days",
"MONTELUKAST 10 MG, daily, 20 days",
"PAN 40",
"Smart pain plan- tropper fs",
"NA",
"Pan 40, every morning, 7 days",
"Dapsone, every morning, 7 days",
"Carcit, morning and night, 7 days",
"Reglas Labor Com, morning and night, 7 days",
"140, every morning, 7 days",
"TAB - SHELCAL CT, 1, after breakfast and dinner",
"TAB - THYRONORM 125 MCG, 1, on empty stomach (1 tab mon to sat , 2 tab on sunday)",
"CAP - D RISE, 1, once a month",
"TAB AMLONG 5 MG, morning",
"Rantac, after meals, 5 days",
"Nasivion, bd (before bed), 5 days",
"Ibugesic Plus, after meals, 5 days",
"Capotril, after meals, 7 days",
"Syp. Rady, after meals, 5 days",
"Calvin-D3 Drops, once a day, 1 month",
"Nasivion S (Nasal) Drops, 5 times a day, 4 days",
"Sodium Chloride (0.65% w/v) + Benzalkonium Chloride (0.03% w/v) Nostrils Drops, 5 times a day, 4 days",
"PAN 40, before breakfast, 2-3 weeks",
"Tab B-29 1m, before dinner",
"Tab Dolo 600, after lunch",
"TABLET DELTONE (60 mg), 30 min before food, 30 day(s)",
"TABLET LESURIDE 25MG (25MG ), 30 min before food, 30 day(s)",
"TABLET NEXITO 5MG (5 mg), after food, 30 day(s)",
"LIQUID Aristozyme (10/50mg), 15 ml three times daily after food, 30 day(s)",
"Suspension DIGERAFT MINT FLAVOUR (10 ML), after food, 10 day(s)",
"ORS, tbd, tbd",
"Sp. Oftrivselog, tbd, tbd",
"Soc. DiFisac 1-14, tbd, tbd",
"Syl. cyccorona(5mm) +84, tbd, tbd",
"Sys. Partene Su 1 mg, tbd, tbd",
"MEN-39, tbd, tbd",
"Azithromycin, 1-0-1, 5 days",
"Paracetamol, 1-1-1, 5 days",
"Rib Belt, n/a, as per requirement",
"PAN 40, morning and night, 10 days",
"Paracetamol, morning and night, 10 days",
"Diclofenac, morning and night, 10 days",
"PAN 40, twice daily, not mentioned",
"Nimulid, only at night, not mentioned",
"Entimy Plus, once daily, not mentioned",
"81T60060",
"LDIT60060",
"Dr.T.F@br &p&Turfun M.B.B.S ., M.S.(OBG),D.G.O ., F.A.G.E .,",
"T. Somprag 40mg, before breakfast and before dinner, 3 days",
"CINTARRO, before breakfast, lunch, and dinner",
"Sucrafel-o, after lunch, 14 days",
"NORMEXIN, 3 times a day, 3 days",
"Misogesic SR, 3 times a day, 10 days",
"T. Oatzy, before breakfast, 30 days",
"T. Cobafisch, before dinner",
"Tab. DIZIBEAT, 5 days",
"Seruna Vit. D, 5 days",
"Lesum Vit. B12",
"Tub FlozerAA -15, 01�5 day",
"Tab Mahaaf x 20",
"Tab Dolokind MR-, 1/2h",
"Tab Parkand",
"PAN 40, thrice a day",
"Florent- Fourth",
"Te CALS",
"Gel - Ora Help, 5 days, 5 days",
"Syp. Bandy Plus, hs, 15 days",
"Pan 40, 3 days, 7 days",
"Ecosprin AV (15/10) OD, morning, 30 days",
"Night spadives, morning and night, 30 days",
"NA",
"NA",
"NA",
"Tab. Augmentin Duo 625mg, 10 days, 10 days",
"Tab. Colpa-D, 5 days, 5 days",
"Syp Ascoril-LS, 1 week, 1 week",
"Yab. Relent, 1 week, 1 week",
"Cap. Somplas-0, 1 week, 1 week",
"Nirmadhu Tab, after food, 45 days",
"DIME E Wock, after food, 45 days",
"Prashaantha Tab, after food, 45 days",
"TAB.RYBELSUS 14 MG, 1 tablet before breakfast, 30 days",
"TAB.GLEDEPA 10 MG/OXRA 10 MG, 1 tablet before breakfast, 30 days",
"TAB.GLYCOMET SR 500 MG, 1 tablet before dinner, 30 days",
"TAB.SYMBAL (30MG), 1 tablet bedtime, 30 days",
"CAP.RABONIK DSR, 1 capsule before dinner, as needed",
"TAB.RAZEL F 5 MG, 1 tablet bedtime, 30 days",
"Jy. Sheprix, bd",
"Sp Dolo 250, 001",
"Sp. Bifolate, 001, 1 month",
"PAN 40, once daily, after 7 days",
"LORAZEPAM, as needed",
"VOLTAREN, as needed",
"Amoxicillin-Clavulanate 625mg, 5, 5 days",
"Dexamethasone 4mg, 5, 5 days",
"Paracetamol 500mg, 5, 5 days",
"Zinc Sulphate, 5, 5 days",
"Probiotics, 5, 5 days",
"PAN 40, before breakfast and before dinner, 1 month",
"Atarax, before bedtime, 5 days",
"Goun DS/Campol -250, if needed",
"Tab Tricum Max, 1 month",
"Tab Tendoshot, 1 month",
"Cap Indo Cap SR, 10 days",
"Tab Tryptomer, 10 days",
"Tab Rablet, 10 days",
"Tab ultrave",
"Ronder, bd, 4 days",
"Coldphan plus, bd, 4 days",
"Nazoplus marclep, tds, 4 days",
"Paracetamol-Mas, bd, 4 days",
"Livogen 1, 3 times a day, 15 days",
"Sheled xi, 6 times a day",
"Syrup Aptimust (CYPROHEPTADINE(2 MG)), before food, 2 weeks",
"Syrup Moktel (MULTI VITAMIN), 3 months",
"Medicine 3",
"Tab. Teczine 10 mg, morning, night, 100 days",
"Venusia Max Lotion",
"Diperbate Plus Lotion, morning, 100 days",
"PAN 40, morning and night, 10 days",
"Paracetamol, morning, afternoon and night, 5 days",
"Cough Syrup, morning and night, 7 days",
"Vonciel, morning and night",
"Pantop 2, morning and night",
"Ancin 625, night",
"Tls 0.1",
"PAN 40, once a day, 7 days",
"Paracetamol, 3 times a day, 5 days",
"Syp Dufladac, twice a day, 10 days",
"Mesar Plus, bbf, 35 days",
"Rowlip F 10, al, continue",
"Job Nuhenz D, bd, 1 month",
"Bio-Deplus, bd, 1 month",
"Gen Dono 60, 18 weeks",
"Pan 40, before breakfast and dinner, 5 days",
"Alvily, bd, 10 days",
"Taz Lansoprazol, bd, 15 days",
"Tab Switch 200, al, 10",
"Tab Calpol - 2, 001, 10",
"Sinarest, bd, 10",
"Tab par D, bd, 10",
"ACICURB OS, 5 ml after meals, 5 days",
"ZINCOVIT, 0 after meals, 15 days",
"ATARAX (10), 1 if itching/if required, 10 days",
"PAN 40, morning and night",
"Tramadol, morning, afternoon, and night",
"Hing, morning",
"TAB. COLLAFLEZ PRO PLUS CAP., after food - daily - 5 days",
"TAB. AFLAROSE PLUS TAB, after food - daily - 5 days",
"TAB. ROCKBON, after food - daily - 5 days",
"TAB. ULTRAKING, after food - daily - 5 days",
"GUFIBIS OIL 20ML, daily - 5 days",
"TAB. HQTOR *, after food - daily - 5 days",
"CAP. PRECOOL, after food - daily - 5 days",
"Ban 40",
"Oftax-OR 1",
"Pantop-40mg",
"Nuovi 2TSFX",
"TAB AMLONG 5 MG",
"Sgp. RidAts, follow up date:",
"PTU 50 MG, morning, afternoon, and night, review in 1-2 days",
"Ban 40, morning and night",
"Pantop D, morning and night",
"Ancin 625, morning and night",
"Syp. Duphalac, 0-0, 7 days",
"Lignocaine jelly 2% LA (xylocaine), before meals, 7 days",
"J- Sitcom forte, after meals, 7 days",
"Rozecor ASP, bd (before dinner), 30 days",
"T AXCER 90mg, al (after lunch), bd (before dinner), 30 days",
"Carloc 3.125, bbf (before breakfast), al (after lunch), bd (before dinner), 30 days",
"Syrup Briolite, once a day, 10 days bedtime",
"LEVOCETIRIZINE (2.5MG/5ML), once a day, 10 days bedtime",
"MONTELUKAST (4MG/5ML) Oral Suspension",
"AMOXYCILLIN (400MG/5ML) + CLAVULANIC ACID (57MG/5ML) Oral Suspension, 12 hourly, 5 days",
"Ointment Mupin (Skin) (5 gm) MUPIROCIN(2%), 8 hourly, 5 days affected area",
"Crocin DS Suspension, 6 hourly, sos",
"Nasoclear Nasal Drop, 6 hourly, 5 days",
"Toned milk",
"OLESOFT MAX CREAM",
"Alaspan Tablet LORATADINE (10 mg)",
"Protar-K Solution Ketoconazole (2 %) + Coal Tar (4 %)",
"Momate Cream Mometasone (0.1 %)",
"SHIKO 21077572",
"VELTEN 04MG 3MSK",
"Pan 40, after meals, 7 days",
"Ibrida Junior, after meals, 7 days",
"Polycion L - 50, after meals, 7 days",
"Syp. oflox 100mg, 3 times a day, 5 days",
"Syp. Netafox, 2 times a day, 3 days",
"Sport 4410, 2 times a day, 3 days",
"H-GUT, 2 times a day, 3 days",
"Dallan ORL, 1 time a day, 3 days",
"Syp. crpar 250, 6 times a day, 5 days",
"Sp. Me/torp, 1 time a day, 3 days",
"PAN 40, morning and night, 15 days",
"NEMOCID, bd (before dinner), 2 days",
"Ciplar (10), bd",
"Peril MD (+25), bd",
"Syp Julire",
"Pan 40, morning and night",
"Liv 52, morning",
"Bestyme, morning",
"PAN 40, morning and night",
"Perzo-M, morning and night",
"Dermeden calse lotion, bd",
"Sompraz 40, morning and night",
"Levogastrof 25, morning and night",
"OFLOX 50 SUSPENSION, 0, 6ml",
"SPORCAC TABS, -, rantal synd",
"Emixt Sumup, 30-45 mins, byfor food",
"Tab. Mebtal-500, 20 day",
"Tab Absolut hold",
"Tab Pay kony, 23 day",
"Tab cetrizin cons",
"Tab Zontal 400mg, x every 3 days",
"Caman, before breakfast, 60 days",
"Toxin 650, before dinner, 3 days",
"Dyrapar, after lunch, 3 days",
"PAN 40, morning, afternoon, night",
"Cap Aclinch, morning, afternoon, night",
"Pan 40, morning and night, 6 days",
"Admad, 1-0-1, 5 days",
"GLIMEPIRIDE, 1-0-1, 3 months",
"METFORMIN, 1-0-1, 3 months",
"LOSARTAN, 1-0-0, 3 months",
"Neurovon fris",
"Pato de D50",
"Tas Acicion 400, 3 times a day, 8 days",
"Tabs Rabmor D, 1 time a day, 8 days",
"Calapure Lotion, as needed, 8 days",
"Tab Neurokind LC, 1 time a day, 8 days",
"Syr Polybion L, 1 time a day, 8 days",
"PAN 40, morning and night, 3 weeks",
"Anoxlief, morning and night, 3 weeks",
"PAN 40, after meals, 10 days",
"Bilinds sun screenDA, daily, until next visit",
"Cosmelite-next Cream2, daily, 1 month",
"Noclevon, 3 times a day",
"Frucola AF, once a day",
"Marberny PD, 3 times a day",
"Pan 40, before breakfast, 1 day",
"Dydrogesterone, before breakfast, 1 day",
"Ferrous Ascorbate, before breakfast, 1 day",
"Mega-CV Forte, 2 times a day, 3 days",
"Syp Flexon, 2 times a day, 5 days",
"Maxtra, 3 times a day, 7 days",
"Sinomet (P)sal drops, 3 times a day, 10 days",
"SAPDAvolac, 1 time a day, 11 days",
"L-MONTUS TAB, 0-0-1, 2 weeks",
"FURAMIST NASAL SPRAY 27.5mcg/1 puff, 1-0-1, 2 weeks",
"SINAREST TAB, as needed, 3 days",
"hydrochloride 10mg, chlorpheniramine maleate 2mg",
"Steam Inhalation",
"Tab Ultracet, ao y, 3 days",
"Ice Docks",
"Moxiforce CV 625mg Tab, after meal, 5 days",
"Wintop DSR, before meal, 5 days",
]
durations_list = [
"1 week",
"1 month",
"3 days",
"5 days",
"2 weeks",
"10 days",
"3 weeks",
"2 months",
"once",
]
frequencies_list = [
"before breakfast",
"before breakfast and dinner",
"after food",
"before food",
]
def get_full_med_list():
return full_med_list
def process_docs(dataset: datasets.Dataset):
def _transform(doc):
diagnosis = get_diagnosis(doc)
medicines_list = get_medicines_list(doc)
try:
if contains_indian_characters(diagnosis):
diagnosis = "NA"
except Exception:
pass
try:
if len(check_list_for_indian_characters(medicines_list)) > 0:
medicines_list = []
except Exception:
pass
gold, gold_position = doc_to_target_obtain(doc)
doc["keep"] = (diagnosis != "NA") and (len(medicines_list) != 0)
doc["gold"] = gold
doc["gold_position"] = gold_position
return doc
transformed_dataset = dataset.map(_transform)
# Now filter the dataset to keep only those where 'keep' is True
def _filter(doc):
return doc["keep"]
filtered_dataset = transformed_dataset.filter(_filter)
print(f"Final len filtered dataset: {len(filtered_dataset)}")
return filtered_dataset
def contains_indian_characters(text):
# Define Unicode ranges for Indian scripts
indian_script_ranges = [
(0x0900, 0x097F), # Devanagari
(0x0980, 0x09FF), # Bengali
(0x0A80, 0x0AFF), # Gujarati
(0x0A00, 0x0A7F), # Gurmukhi
(0x0C80, 0x0CFF), # Kannada
(0x0D00, 0x0D7F), # Malayalam
(0x0B80, 0x0BFF), # Tamil
(0x0C00, 0x0C7F), # Telugu
]
# Create a regular expression pattern for Indian scripts
pattern = "|".join(
[f"[{chr(start)}-{chr(end)}]" for start, end in indian_script_ranges]
)
# Check if the text contains any Indian script characters
return bool(re.search(pattern, text))
def check_list_for_indian_characters(string_list):
results = []
for text in string_list:
if contains_indian_characters(text):
results.append(text)
return results
def doc_to_text_easy(doc) -> str:
diagnosis = get_diagnosis(doc)
choices = doc_to_choice_easy(doc)
prompt = (
"You are a medical doctor. A patient presents the following diagnosis or complains: {}. What would you prescribe in this case? \nChoices: \n"
"A. {} \n"
"B. {} \n"
"C. {} \n"
"D. {} \nAnswer:".format(
diagnosis, choices[0], choices[1], choices[2], choices[3]
)
)
return prompt
def doc_to_text_hard(doc) -> str:
diagnosis = get_diagnosis(doc)
choices = doc_to_choice_hard(doc)
prompt = (
"You are a medical doctor. A patient presents the following diagnosis or complains: {}. What would you prescribe in this case? \nChoices: \n"
"A. {} \n"
"B. {} \n"
"C. {} \n"
"D. {} \nAnswer:".format(
diagnosis, choices[0], choices[1], choices[2], choices[3]
)
)
print(prompt)
return prompt
def get_diagnosis(doc):
results_dict = ast.literal_eval(doc["results"])
if "procedure" in results_dict:
if "chief_complaints_diagnosis" in results_dict["procedure"]:
diagnosis = results_dict["procedure"]["chief_complaints_diagnosis"]
elif "chief_complaints" and "diagnosis" in results_dict["procedure"]:
diagnosis = (
"Complains: "
+ results_dict["procedure"]["chief_complaints"]
+ ". Diagnosis: "
+ results_dict["procedure"]["diagnosis"]
)
else:
diagnosis = "NA"
elif "prescription_details" in results_dict:
try:
diagnosis = results_dict["prescription_details"]["disease_diagnosis"]
except Exception:
diagnosis = "NA"
elif "Symptoms/Complaints" in results_dict:
symptoms = results_dict["Symptoms/Complaints"]
diagnosis = ", ".join(symptoms)
else:
diagnosis = "NA"
return diagnosis
def get_medicines_list(doc):
results_dict = ast.literal_eval(doc["results"])
if (
"medicine_details" in results_dict
and len(results_dict["medicine_details"]) != 0
):
if "medicine_frequency" in results_dict["medicine_details"][0]:
try:
medicines_sample = [
f"{item['medicine_name']}, {item['medicine_frequency'].lower()}, {item['course_duration'].lower()}"
for item in results_dict["medicine_details"]
if len(item.keys()) > 1
]
except Exception:
try:
if "course_duration" not in results_dict["medicine_details"][0]:
medicines_sample = [
f"{item['medicine_name']}, {item['medicine_frequency'].lower()}"
for item in results_dict["medicine_details"]
if len(item.keys()) > 1
]
elif (
"medicine_frequency" not in results_dict["medicine_details"][0]
and "course_duration" in results_dict["medicine_details"][0]
):
medicines_sample = [
f"{item['medicine_name']}, {item['course_duration'].lower()}"
for item in results_dict["medicine_details"]
if len(item.keys()) > 1
]
else:
medicines_sample = []
except Exception:
medicines_sample = []
elif "medicine_dosage" in results_dict["medicine_details"][0]:
medicines_sample = [
f"{item['medicine_name']}, {item['medicine_dosage'].lower()}"
for item in results_dict["medicine_details"]
]
else:
medicines_sample = []
elif "Medicines Prescribed" in results_dict:
if "Duration" in results_dict["Medicines Prescribed"][0]:
medicines_sample = [
f"{item['Name']}, {item['Duration'].lower()}"
for item in results_dict["Medicines Prescribed"]
]
elif "Dosage" in results_dict["Medicines Prescribed"][0]:
medicines_sample = [
f"{item['Name']}, {item['Dosage'].lower()}"
for item in results_dict["Medicines Prescribed"]
]
else:
medicines_sample = []
medicines_sample = [
item.replace(", na", "")
.replace(" na", "")
.replace(" n/a", "")
.replace("NA", "")
.replace(" not specified", "")
.replace(" not provided", "")
for item in medicines_sample
]
medicines_sample = [item for item in medicines_sample if len(item) > 1]
if "[Medicine Name], [medicine frequency], [course duration]" in medicines_sample:
medicines_sample = []
return medicines_sample
def doc_to_target(doc):
return doc["gold_position"]
def doc_to_target_obtain(doc):
medicines_sample = get_medicines_list(doc)
if len(medicines_sample) == 0:
return "NA", 0
gold = random.choice(medicines_sample)
gold_position = random.randint(0, 3)
return gold, gold_position
def doc_to_choice_easy(doc):
gold, gold_position = doc["gold"], doc["gold_position"]
full_med_list = get_full_med_list()
random_items = random.sample(full_med_list, 3)
choices_easy = random_items[:gold_position] + [gold] + random_items[gold_position:]
return choices_easy
def doc_to_choice_hard(doc):
gold, gold_position = doc["gold"], doc["gold_position"]
medicines_sample = get_medicines_list(doc)
random_choices = []
new_list = [item for item in medicines_sample if item != gold]
if random.randint(0, 1) == 0:
fake_duration = random.sample(durations_list, 1)
fake_frequency = random.sample(frequencies_list, 1)
random_choices.append(
f"{gold.split(',')[0]}, {fake_frequency[0]}, {fake_duration[0]}"
)
if len(new_list) >= 2:
fake_duration = random.choices(durations_list, k=2)
fake_frequency = random.choices(frequencies_list, k=2)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[0]}, {fake_duration[0]}"
)
random_choices.append(
f"{new_list[1].split(',')[0]}, {fake_frequency[1]}, {fake_duration[1]}"
)
elif len(new_list) == 1:
fake_duration = random.choices(durations_list, k=2)
fake_frequency = random.choices(frequencies_list, k=2)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[0]}, {fake_duration[0]}"
)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[1]}, {fake_duration[1]}"
)
else:
fake_duration = random.choices(durations_list, k=2)
fake_frequency = random.choices(frequencies_list, k=2)
random_choices.append(
f"{gold.split(',')[0]}, {fake_frequency[0]}, {fake_duration[0]}"
)
random_choices.append(
f"{gold.split(',')[0]}, {fake_frequency[1]}, {fake_duration[1]}"
)
else:
if len(new_list) >= 3:
fake_duration = random.choices(durations_list, k=3)
fake_frequency = random.choices(frequencies_list, k=3)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[0]}, {fake_duration[0]}"
)
random_choices.append(
f"{new_list[1].split(',')[0]}, {fake_frequency[1]}, {fake_duration[1]}"
)
random_choices.append(
f"{new_list[2].split(',')[0]}, {fake_frequency[2]}, {fake_duration[2]}"
)
elif len(new_list) == 2:
fake_duration = random.choices(durations_list, k=3)
fake_frequency = random.choices(frequencies_list, k=3)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[0]}, {fake_duration[0]}"
)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[1]}, {fake_duration[1]}"
)
random_choices.append(
f"{new_list[1].split(',')[0]}, {fake_frequency[2]}, {fake_duration[2]}"
)
elif len(new_list) == 1:
fake_duration = random.choices(durations_list, k=3)
fake_frequency = random.choices(frequencies_list, k=3)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[0]}, {fake_duration[0]}"
)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[1]}, {fake_duration[1]}"
)
random_choices.append(
f"{new_list[0].split(',')[0]}, {fake_frequency[2]}, {fake_duration[2]}"
)
else:
fake_duration = random.choices(durations_list, k=3)
fake_frequency = random.choices(frequencies_list, k=3)
random_choices.append(
f"{gold.split(',')[0]}, {fake_frequency[0]}, {fake_duration[0]}"
)
random_choices.append(
f"{gold.split(',')[0]}, {fake_frequency[1]}, {fake_duration[1]}"
)
random_choices.append(
f"{gold.split(',')[0]}, {fake_frequency[2]}, {fake_duration[2]}"
)
choices_hard = (
random_choices[:gold_position] + [gold] + random_choices[gold_position:]
)
return choices_hard
lm_eval/tasks/med_text_classification/med_text_classification_easy.yaml 0 → 100644
View file @ 7c9fbcf8
group: med_text_classification
task: med_text_classification_easy
dataset_path: csv
dataset_name: null
dataset_kwargs:
data_files:
train: /gpfs/projects/bsc70/heka/data/datasets/med_text_class_train.csv
output_type: multiple_choice
training_split: train
validation_split: train
test_split: train
process_docs: !function utils.process_docs
doc_to_text: !function utils.doc_to_text_easy
doc_to_choice: !function utils.doc_to_choice_easy
doc_to_target: !function utils.doc_to_target_easy
generation_kwargs:
until:
- "\n\n"
metric_list:
- metric: acc
aggregation: mean
higher_is_better: true
metadata:
version: 1.0
lm_eval/tasks/med_text_classification/med_text_classification_hard.yaml 0 → 100644
View file @ 7c9fbcf8
include: med_text_classification_easy.yaml
task: med_text_classification_hard
dataset_kwargs:
data_files:
train: /gpfs/projects/bsc70/heka/data/datasets/mtsamples.csv
process_docs: !function utils.process_docs_hard
doc_to_text: !function utils.doc_to_text_hard
doc_to_choice: !function utils.doc_to_choice_hard
doc_to_target: !function utils.doc_to_target_hard
lm_eval/tasks/med_text_classification/utils.py 0 → 100644
View file @ 7c9fbcf8
import random
import datasets
def process_docs_hard(dataset: datasets.Dataset):
return dataset
def process_docs(dataset: datasets.Dataset):
def _helper(doc):
return doc
num_entries = len(dataset)
ten_percent_index = int(0.1 * num_entries)
# Select the first 10% of the dataset
filtered_dataset = dataset.select(range(ten_percent_index))
return filtered_dataset.map(_helper)
def doc_to_choice_easy(doc):
return [
"neoplasms",
"digestive system diseases",
"nervous system diseases",
"cardiovascular diseases",
"general pathological conditions",
]
def doc_to_text_easy(doc) -> str:
choices = doc_to_choice_easy(doc)
prompt = (
"Classify the topic of the following medical text into one of the following choices. \n"
"Text: {} \n"
"Choices: \n"
"A. {} \n"
"B. {} \n"
"C. {} \n"
"D. {} \n"
"E. {} \n Answer:".format(
doc["text"], choices[0], choices[1], choices[2], choices[3], choices[4]
)
)
return prompt
def doc_to_target_easy(doc):
return int(doc["class"]) - 1
def doc_to_text_hard(doc) -> str:
choices = doc_to_choice_hard(doc)
prompt = (
"Select the medical specialty the following text is talking about among the following choices. \n"
"Text: {} \n"
"Choices: {}\n"
" Answer:".format(doc["transcription"], choices)
)
return prompt
def doc_to_choice_hard(doc):
choices_list = [
" Bariatrics",
" Allergy / Immunology",
" Dentistry",
" Cardiovascular / Pulmonary",
" Urology",
" Hospice - Palliative Care",
" Radiology",
" Pediatrics - Neonatal",
" Neurology",
" Neurosurgery",
" Emergency Room Reports",
" IME-QME-Work Comp etc.",
" Office Notes",
" Surgery",
" Letters",
" Ophthalmology",
" Hematology - Oncology",
" Endocrinology",
" Cosmetic / Plastic Surgery",
" Diets and Nutritions",
" Rheumatology",
" Nephrology",
" Physical Medicine - Rehab",
" Podiatry",
" Chiropractic",
" Lab Medicine - Pathology",
" Orthopedic",
" Autopsy",
" Psychiatry / Psychology",
" Speech - Language",
" ENT - Otolaryngology",
" Sleep Medicine",
" Dermatology",
" SOAP / Chart / Progress Notes",
" General Medicine",
" Consult - History and Phy.",
" Obstetrics / Gynecology",
" Gastroenterology",
" Pain Management",
" Discharge Summary",
]
return choices_list
def doc_to_target_hard(doc):
choices = doc_to_choice_hard(doc)
gold = doc["medical_specialty"]
idx = choices.index(gold)
return idx
lm_eval/tasks/meddialog/README.md 0 → 100644
View file @ 7c9fbcf8
# Meddialog
### Paper
Title: `MedDialog: Large-scale Medical Dialogue Datasets`
Abstract: [https://aclanthology.org/2020.emnlp-main.743/](https://aclanthology.org/2020.emnlp-main.743/)
This task contains the english version of the MedDialog Medical Dialogue Dataset, divided in two tasks:
question entailment, and open-ended Question Answering (QA).
#### Tasks
* `meddialog_qsumm`: Question entailment in english.
* `meddialog_qsumm_perplexity`: Question entailment in english, evaluated with perplexity.
* `meddialog_raw_dialogues`: Open-Ended QA in english.
* `meddialog_raw_perplexity`: Open-Ended QA in english, evaluated with perplexity.
### Citation
```bibtex
@inproceedings{zeng-etal-2020-meddialog,
title = "{M}ed{D}ialog: Large-scale Medical Dialogue Datasets",
author = "Zeng, Guangtao and
Yang, Wenmian and
Ju, Zeqian and
Yang, Yue and
Wang, Sicheng and
Zhang, Ruisi and
Zhou, Meng and
Zeng, Jiaqi and
Dong, Xiangyu and
Zhang, Ruoyu and
Fang, Hongchao and
Zhu, Penghui and
Chen, Shu and
Xie, Pengtao",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.743/",
doi = "10.18653/v1/2020.emnlp-main.743",
pages = "9241--9250",
abstract = "Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build large-scale medical dialogue datasets {--} MedDialog, which contain 1) a Chinese dataset with 3.4 million conversations between patients and doctors, 11.3 million utterances, 660.2 million tokens, covering 172 specialties of diseases, and 2) an English dataset with 0.26 million conversations, 0.51 million utterances, 44.53 million tokens, covering 96 specialties of diseases. To our best knowledge, MedDialog is the largest medical dialogue dataset to date. We pretrain several dialogue generation models on the Chinese MedDialog dataset, including Transformer, GPT, BERT-GPT, and compare their performance. It is shown that models trained on MedDialog are able to generate clinically correct and doctor-like medical dialogues. We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks. It is shown that via transfer learning which finetunes the models pretrained on MedDialog, the performance on medical dialogue generation tasks with small datasets can be greatly improved, as shown in human evaluation and automatic evaluation. The datasets and code are available at \url{https://github.com/UCSD-AI4H/Medical-Dialogue-System}"
}
```
lm_eval/tasks/meddialog/meddialog_qsumm.yaml 0 → 100644
View file @ 7c9fbcf8
group: meddialog
include: meddialog_raw_dialogues.yaml
task: meddialog_qsumm
dataset_path: lighteval/med_dialog
dataset_name: icliniq
description: >
Instructions: The following text is contains a medical question. Extract and summarize the question.
output_type: generate_until
training_split: train
validation_split: validation
test_split: test
doc_to_text: !function utils.doc_to_text_qsumm
doc_to_target: !function utils.doc_to_target_qsumm
process_results: !function utils.process_results_gen_qsumm
lm_eval/tasks/meddialog/meddialog_qsumm_perplexity.yaml 0 → 100644
View file @ 7c9fbcf8
include: meddialog_qsumm.yaml
task: meddialog_qsumm_perplexity
output_type: loglikelihood_rolling
doc_to_text: ""
process_results: !function utils_perplexity.process_results_qsumm
metric_list:
- metric: word_perplexity
higher_is_better: false
- metric: byte_perplexity
higher_is_better: false
- metric: bits_per_byte
higher_is_better: false
metadata:
version: 1.0
lm_eval/tasks/meddialog/meddialog_raw_dialogues.yaml 0 → 100644
View file @ 7c9fbcf8
group: meddialog
task: meddialog_raw_dialogues
dataset_path: bigbio/meddialog
description: >
Instructions: The following text is from a collection of medical dialogues. What follows is the patients question. Answer how a doctor would, trying to be as helpful as possible.
output_type: generate_until
training_split: train
validation_split: train
test_split: train
doc_to_text: !function utils.doc_to_text_raw
doc_to_target: !function utils.doc_to_target_raw
process_results: !function utils.process_results_gen_raw
generation_kwargs:
until:
- "\n\n"
metric_list:
- metric: bleu
aggregation: nanmean
higher_is_better: true
- metric: rouge1
aggregation: nanmean
higher_is_better: true
- metric: rouge2
aggregation: nanmean
higher_is_better: true
- metric: rougeL
aggregation: nanmean
higher_is_better: true
- metric: bleurt
aggregation: nanmean
higher_is_better: true
- metric: bert_score
aggregation: nanmean
higher_is_better: true
metadata:
version: 1.0
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment