"With the vast amount of work done in the field today, it helps to have a tool that people can use easily to share their results and use to check others to ensure reported numbers are valid. The LM Evaluation Harness is one such tool the community has used extensively. We want to continue to support the community and with that in mind, we’re excited to announce a major update on the LM Evaluation Harness to further our goal for open and accessible AI research."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0gDoM0AJAvEc"
},
"source": [
"Our refactor stems from our desires to make the following believed best practices easier to carry out. \n",
"\n",
"1. Never copy results from other papers\n",
"2. Always share your exact prompts\n",
"3. Always provide model outputs\n",
"4. Qualitatively review a small batch of outputs before running evaluation jobs at scale\n",
"\n",
"We also wanted to make the library a better experience to use and to contribute or design evaluations within. New features in the new release that serve this purpose include:\n",
"\n",
"1. Faster Evaluation Runtimes (accelerated data-parallel inference with HF Transformers + Accelerate, and commonly used or faster inference libraries such as vLLM and Llama-CPP)\n",
"2. Easier addition and sharing of new tasks (YAML-based task config formats, allowing single-file sharing of custom tasks)\n",
"3. More configurability, for more advanced workflows and easier operation with modifying prompts\n",
"4. Better logging of data at runtime and post-hoc"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nnwsOpjda_YW"
},
"source": [
"In this notebook we will be going through a short tutorial on how things work."
"Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->lm-eval==1.0.0) (3.3.2)\n",
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"Requirement already satisfied: python-dateutil<3.0.0,>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from typepy[datetime]<2,>=1.3.2->pytablewriter->lm-eval==1.0.0) (2.8.2)\n",
"Requirement already satisfied: pytz>=2018.9 in /usr/local/lib/python3.10/dist-packages (from typepy[datetime]<2,>=1.3.2->pytablewriter->lm-eval==1.0.0) (2023.3.post1)\n",
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"Building wheels for collected packages: lm-eval, rouge-score, sqlitedict\n",
" Building wheel for lm-eval (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for lm-eval: filename=lm_eval-1.0.0-py3-none-any.whl size=994254 sha256=88356155b19f2891981ecef948326ad6ce8ca40a6009378410ec20d0e225995a\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-9v6ye7h3/wheels/17/01/26/599c0779e9858a70a73fa8a306699b5b9a868f820c225457b0\n",
" Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24933 sha256=6bb0d44e4881972c43ce194e7cb65233d309758cb15f0dec54590d3d2efcfc36\n",
" Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
" Building wheel for sqlitedict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16863 sha256=5747f7dd73ddf3d8fbcebf51b5e4f718fabe1e94bccdf16d2f22a2e65ee7fdf4\n",
" Stored in directory: /root/.cache/pip/wheels/79/d6/e7/304e0e6cb2221022c26d8161f7c23cd4f259a9e41e8bbcfabd\n",
"Successfully built lm-eval rouge-score sqlitedict\n",
"## Create new evaluation tasks with config-based tasks\n",
"\n",
"Even within the same task, many works have reported numbers based on different choices of evaluation. Some report on the test sets, validation sets, or even subset of the training sets. Others have specialized prompts and verbalizers. We introduce YAMLs to allow users to easily make different variations. By leveraging the YAML configs to configure evaluations, the refactored LM-Eval takes the methods of the `Task` object and makes them configurable by setting the appropriate attributes in the config file. There, users can set the tasks they want by setting the name of the HF dataset (local tasks are also possible), the dataset splits used, and much more. Key configurations relating to prompting, such as `doc_to_text`, previously implemented as a method of the same name, are now configurable with jinja2 to allow high-level scripting to transform a HF dataset to text string as input to the model.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HYFUhhfOSJKe"
},
"source": [
"A core-feature to LM-Eval is to configure tasks with YAML configs. With configs, you can fill preset fields to easily set up a task.\n",
"\n",
"Here, we write a demo YAML config for a multiple-choice evaluation of BoolQ:"
"And we can now run evaluation on this task, by pointing to the config file we've just created:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "LOUHK7PtQfq4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-11-29:11:54:55,156 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:54:55.942051: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:54:55.942108: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:54:55.942142: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:54:57.066802: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:55:00,954 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:55:11,038 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:55:11,038 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:55:11,046 INFO [__main__.py:205] Selected Tasks: ['demo_boolq']\n",
"2023-11-29:11:55:11,047 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:55:11,110 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:56:18,658 WARNING [task.py:614] [Task: demo_boolq] metric acc is defined, but aggregation is not. using default aggregation=mean\n",
"2023-11-29:11:56:18,658 WARNING [task.py:626] [Task: demo_boolq] metric acc is defined, but higher_is_better is not. using default higher_is_better=True\n",
"Often, tasks are part of a larger group used to measure different capabilities. The dynamism of the field today means new dimensions of evaluation can come about which would mix and match new and older tasks alike. In LM-Eval, We can also group tasks and call that the group name to evaluate on a set of tasks easily. In this instance, let's evaluate the tag `yes_or_no_tasks` which comprise of the tasks `demo_boolq` and `demo_cola`; tasks which are multiple choice tasks with options `yes` and `no` as the name suggests.\n",
"\n",
"<!-- making new groups is easier than ever, allowing user to work bottom-up by makiing individual tasks and linking them to a group or Top-Down, making a new group by listing existing tasks.\n",
"\n",
"We also show the aggregate across samples besides only showing the aggregation between subtasks. This may come in handy when certain groups want to be aggregated as a single task. -->\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "fthNg3ywO-kA"
},
"outputs": [],
"source": [
"YAML_cola_string = '''\n",
"tag: yes_or_no_tasks\n",
"task: demo_cola\n",
"dataset_path: glue\n",
"dataset_name: cola\n",
"output_type: multiple_choice\n",
"training_split: train\n",
"validation_split: validation\n",
"doc_to_text: \"{{sentence}}\\nQuestion: Does this sentence make sense?\\nAnswer:\"\n",
"doc_to_target: label\n",
"doc_to_choice: [\"no\", \"yes\"]\n",
"should_decontaminate: true\n",
"doc_to_decontamination_query: sentence\n",
"metric_list:\n",
" - metric: acc\n",
"'''\n",
"with open('cola.yaml', 'w') as f:\n",
" f.write(YAML_cola_string)"
]
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Qw83KAePAhaS"
},
"source": [
"# Releasing LM-Evaluation-Harness v0.4.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z7k2vq1iAdqr"
},
"source": [
"With the vast amount of work done in the field today, it helps to have a tool that people can use easily to share their results and use to check others to ensure reported numbers are valid. The LM Evaluation Harness is one such tool the community has used extensively. We want to continue to support the community and with that in mind, we’re excited to announce a major update on the LM Evaluation Harness to further our goal for open and accessible AI research."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0gDoM0AJAvEc"
},
"source": [
"Our refactor stems from our desires to make the following believed best practices easier to carry out. \n",
"\n",
"1. Never copy results from other papers\n",
"2. Always share your exact prompts\n",
"3. Always provide model outputs\n",
"4. Qualitatively review a small batch of outputs before running evaluation jobs at scale\n",
"\n",
"We also wanted to make the library a better experience to use and to contribute or design evaluations within. New features in the new release that serve this purpose include:\n",
"\n",
"1. Faster Evaluation Runtimes (accelerated data-parallel inference with HF Transformers + Accelerate, and commonly used or faster inference libraries such as vLLM and Llama-CPP)\n",
"2. Easier addition and sharing of new tasks (YAML-based task config formats, allowing single-file sharing of custom tasks)\n",
"3. More configurability, for more advanced workflows and easier operation with modifying prompts\n",
"4. Better logging of data at runtime and post-hoc"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nnwsOpjda_YW"
},
"source": [
"In this notebook we will be going through a short tutorial on how things work."
"2023-11-29:11:56:33,016 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:56:33.852995: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:56:33.853050: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:56:33.853087: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:56:35.129047: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:56:38,546 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:56:47,509 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:56:47,509 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:56:47,517 INFO [__main__.py:205] Selected Tasks: ['yes_or_no_tasks']\n",
"2023-11-29:11:56:47,520 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:56:47,550 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:57:08,743 WARNING [task.py:614] [Task: demo_cola] metric acc is defined, but aggregation is not. using default aggregation=mean\n",
"2023-11-29:11:57:08,743 WARNING [task.py:626] [Task: demo_cola] metric acc is defined, but higher_is_better is not. using default higher_is_better=True\n",
"Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->lm-eval==1.0.0) (3.3.2)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->lm-eval==1.0.0) (6.0.4)\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->lm-eval==1.0.0) (4.0.3)\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->lm-eval==1.0.0) (1.9.2)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->lm-eval==1.0.0) (1.4.0)\n",
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"Requirement already satisfied: pytz>=2018.9 in /usr/local/lib/python3.10/dist-packages (from typepy[datetime]<2,>=1.3.2->pytablewriter->lm-eval==1.0.0) (2023.3.post1)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8->lm-eval==1.0.0) (2.1.3)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score>=0.0.4->lm-eval==1.0.0) (8.1.7)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8->lm-eval==1.0.0) (1.3.0)\n",
"Building wheels for collected packages: lm-eval, rouge-score, sqlitedict\n",
" Building wheel for lm-eval (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for lm-eval: filename=lm_eval-1.0.0-py3-none-any.whl size=994254 sha256=88356155b19f2891981ecef948326ad6ce8ca40a6009378410ec20d0e225995a\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-9v6ye7h3/wheels/17/01/26/599c0779e9858a70a73fa8a306699b5b9a868f820c225457b0\n",
" Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24933 sha256=6bb0d44e4881972c43ce194e7cb65233d309758cb15f0dec54590d3d2efcfc36\n",
" Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
" Building wheel for sqlitedict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16863 sha256=5747f7dd73ddf3d8fbcebf51b5e4f718fabe1e94bccdf16d2f22a2e65ee7fdf4\n",
" Stored in directory: /root/.cache/pip/wheels/79/d6/e7/304e0e6cb2221022c26d8161f7c23cd4f259a9e41e8bbcfabd\n",
"Successfully built lm-eval rouge-score sqlitedict\n",
"The following is a yaml made to evaluate the specific subtask of `high_school_geography` from MMLU. It uses the standard prompt where the we choose the letters from the options with most likelihood as the model's prediction."
"## Create new evaluation tasks with config-based tasks\n",
"\n",
"Even within the same task, many works have reported numbers based on different choices of evaluation. Some report on the test sets, validation sets, or even subset of the training sets. Others have specialized prompts and verbalizers. We introduce YAMLs to allow users to easily make different variations. By leveraging the YAML configs to configure evaluations, the refactored LM-Eval takes the methods of the `Task` object and makes them configurable by setting the appropriate attributes in the config file. There, users can set the tasks they want by setting the name of the HF dataset (local tasks are also possible), the dataset splits used, and much more. Key configurations relating to prompting, such as `doc_to_text`, previously implemented as a method of the same name, are now configurable with jinja2 to allow high-level scripting to transform a HF dataset to text string as input to the model.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HYFUhhfOSJKe"
},
"source": [
"A core-feature to LM-Eval is to configure tasks with YAML configs. With configs, you can fill preset fields to easily set up a task.\n",
"\n",
"Here, we write a demo YAML config for a multiple-choice evaluation of BoolQ:"
"with open('mmlu_high_school_geography.yaml', 'w') as f:\n",
" f.write(YAML_mmlu_geo_string)\n"
]
},
"name": "stdout",
"output_type": "stream",
"text": [
"2023-11-29:11:54:55,156 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:54:55.942051: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:54:55.942108: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:54:55.942142: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:54:57.066802: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:55:00,954 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:55:11,038 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:55:11,038 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:55:11,046 INFO [__main__.py:205] Selected Tasks: ['demo_boolq']\n",
"2023-11-29:11:55:11,047 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:55:11,110 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:56:18,658 WARNING [task.py:614] [Task: demo_boolq] metric acc is defined, but aggregation is not. using default aggregation=mean\n",
"2023-11-29:11:56:18,658 WARNING [task.py:626] [Task: demo_boolq] metric acc is defined, but higher_is_better is not. using default higher_is_better=True\n",
"Often, tasks are part of a larger group used to measure different capabilities. The dynamism of the field today means new dimensions of evaluation can come about which would mix and match new and older tasks alike. In LM-Eval, We can also group tasks and call that the group name to evaluate on a set of tasks easily. In this instance, let's evaluate the tag `yes_or_no_tasks` which comprise of the tasks `demo_boolq` and `demo_cola`; tasks which are multiple choice tasks with options `yes` and `no` as the name suggests.\n",
"\n",
"<!-- making new groups is easier than ever, allowing user to work bottom-up by makiing individual tasks and linking them to a group or Top-Down, making a new group by listing existing tasks.\n",
"\n",
"We also show the aggregate across samples besides only showing the aggregation between subtasks. This may come in handy when certain groups want to be aggregated as a single task. -->\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "fthNg3ywO-kA"
},
"outputs": [],
"source": [
"YAML_cola_string = \"\"\"\n",
"tag: yes_or_no_tasks\n",
"task: demo_cola\n",
"dataset_path: glue\n",
"dataset_name: cola\n",
"output_type: multiple_choice\n",
"training_split: train\n",
"validation_split: validation\n",
"doc_to_text: \"{{sentence}}\\nQuestion: Does this sentence make sense?\\nAnswer:\"\n",
"doc_to_target: label\n",
"doc_to_choice: [\"no\", \"yes\"]\n",
"should_decontaminate: true\n",
"doc_to_decontamination_query: sentence\n",
"metric_list:\n",
" - metric: acc\n",
"\"\"\"\n",
"with open(\"cola.yaml\", \"w\") as f:\n",
" f.write(YAML_cola_string)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "XceRKCuuDtbn"
},
"outputs": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "jyKOfCsKb-xy"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-11-29:11:57:23,598 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:57:24.719750: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:57:24.719806: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:57:24.719847: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:57:26.656125: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:57:31,563 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:57:40,541 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:57:40,541 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:57:40,558 INFO [__main__.py:205] Selected Tasks: ['demo_mmlu_high_school_geography']\n",
"2023-11-29:11:57:40,559 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:57:40,589 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:56:33,016 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:56:33.852995: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:56:33.853050: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:56:33.853087: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:56:35.129047: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:56:38,546 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:56:47,509 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:56:47,509 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:56:47,517 INFO [__main__.py:205] Selected Tasks: ['yes_or_no_tasks']\n",
"2023-11-29:11:56:47,520 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:56:47,550 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:57:08,743 WARNING [task.py:614] [Task: demo_cola] metric acc is defined, but aggregation is not. using default aggregation=mean\n",
"2023-11-29:11:57:08,743 WARNING [task.py:626] [Task: demo_cola] metric acc is defined, but higher_is_better is not. using default higher_is_better=True\n",
"The following is a yaml made to evaluate the specific subtask of `high_school_geography` from MMLU. It uses the standard prompt where the we choose the letters from the options with most likelihood as the model's prediction."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "GTFvdt9kSlBG"
},
"outputs": [],
"source": [
"YAML_mmlu_geo_string = \"\"\"\n",
"task: demo_mmlu_high_school_geography\n",
"dataset_path: cais/mmlu\n",
"dataset_name: high_school_geography\n",
"description: \"The following are multiple choice questions (with answers) about high school geography.\\n\\n\"\n",
"with open(\"mmlu_high_school_geography.yaml\", \"w\") as f:\n",
" f.write(YAML_mmlu_geo_string)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "jyKOfCsKb-xy"
},
"outputs": [
{
"cell_type": "markdown",
"metadata": {
"id": "jyKOfCsKb-xy"
},
"source": [
"We could also evaluate this task in a different way. For example, instead of observing the loglikelihood of the letters, we can instead evaluate on the choices themselves as the continuation. This is done by simply changing `doc_to_choice` from a list of letters to the corresponding `choices` field from the HF dataset. We write `\"{{choices}}\"` so that the string field is interpreted as jinja string that acquires the list from the HF dataset directly.\n",
"\n",
"Another convenient feature here is since we're only modifying the `doc_to_choice` and the rest of config is the same as the task above, we can use the above configuration as a template by using `include: mmlu_high_school_geography.yaml` to load the config from that file. We'll need to add a unique task name as to not colide with the existing yaml config we're including. For this case we'll simply name this one `mmlu_high_school_geography_continuation`. `doc_to_text` is added here just for sake of clarity."
]
},
"name": "stdout",
"output_type": "stream",
"text": [
"2023-11-29:11:57:23,598 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:57:24.719750: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:57:24.719806: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:57:24.719847: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:57:26.656125: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:57:31,563 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:57:40,541 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:57:40,541 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:57:40,558 INFO [__main__.py:205] Selected Tasks: ['demo_mmlu_high_school_geography']\n",
"2023-11-29:11:57:40,559 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:57:40,589 INFO [huggingface.py:120] Using device 'cuda'\n",
"We could also evaluate this task in a different way. For example, instead of observing the loglikelihood of the letters, we can instead evaluate on the choices themselves as the continuation. This is done by simply changing `doc_to_choice` from a list of letters to the corresponding `choices` field from the HF dataset. We write `\"{{choices}}\"` so that the string field is interpreted as jinja string that acquires the list from the HF dataset directly.\n",
"\n",
"Another convenient feature here is since we're only modifying the `doc_to_choice` and the rest of config is the same as the task above, we can use the above configuration as a template by using `include: mmlu_high_school_geography.yaml` to load the config from that file. We'll need to add a unique task name as to not colide with the existing yaml config we're including. For this case we'll simply name this one `mmlu_high_school_geography_continuation`. `doc_to_text` is added here just for sake of clarity."
"with open('mmlu_high_school_geography_continuation.yaml', 'w') as f:\n",
" f.write(YAML_mmlu_geo_string)\n"
]
},
"name": "stdout",
"output_type": "stream",
"text": [
"2023-11-29:11:58:21,284 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:58:22.850159: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:58:22.850219: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:58:22.850254: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:58:24.948103: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:58:28,460 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:58:37,935 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:58:37,935 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:58:37,969 INFO [__main__.py:205] Selected Tasks: ['demo_mmlu_high_school_geography_continuation']\n",
"2023-11-29:11:58:37,972 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:58:38,008 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:58:59,758 INFO [task.py:355] Building contexts for task on rank 0...\n",
"2023-11-29:11:58:59,777 INFO [evaluator.py:319] Running loglikelihood requests\n",
"100% 40/40 [00:02<00:00, 16.23it/s]\n",
"fatal: not a git repository (or any of the parent directories): .git\n",
"If we take a look at the samples, we can see that it is in fact evaluating the continuation based on the choices rather than the letters."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "duBDqC6PAdjL"
},
"outputs": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "-_CVnDirdy7j"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-11-29:11:58:21,284 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:58:22.850159: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:58:22.850219: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:58:22.850254: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:58:24.948103: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:58:28,460 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:58:37,935 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:58:37,935 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:58:37,969 INFO [__main__.py:205] Selected Tasks: ['demo_mmlu_high_school_geography_continuation']\n",
"2023-11-29:11:58:37,972 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:58:38,008 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:58:59,758 INFO [task.py:355] Building contexts for task on rank 0...\n",
"2023-11-29:11:58:59,777 INFO [evaluator.py:319] Running loglikelihood requests\n",
"100% 40/40 [00:02<00:00, 16.23it/s]\n",
"fatal: not a git repository (or any of the parent directories): .git\n",
"To prepare a task we can simply fill in a YAML config with the relevant information.\n",
"\n",
"`output_type`\n",
"The current provided evaluation types comprise of the following:\n",
"1. `loglikelihood`: Evaluates the loglikelihood of a continuation, conditioned on some input string.\n",
"2. `loglikelihood_rolling`: evaluate the loglikelihood of producing a string, conditioned on the empty string. (Used for perplexity evaluations)\n",
"3. `multiple_choice`: Evaluates loglikelihood among the a number of choices predicted by the model.\n",
"4. `greedy_until`: Model outputs greedy generation (can be configured to to use beam search and other generation-related parameters)\n",
"\n",
"The core prompt revolves around 3 fields.\n",
"1. `doc_to_text`: Denotes the prompt template that will be used as input to the model.\n",
"2. `doc_to_choice`: Available choices that will be used as continuation for the model. This is used when the `output_type` is `multiple_choice`, and otherwise can be left as `None`.\n",
"3. `doc_to_target`: When `output_type` is `multiple_choice`, this can be an index that corresponds to the correct answer, or the answer string itself (must be a subset of `doc_to_choice`). For other tasks, this is expected to be a string. You can fill this field with a feature name from the HF dataset so long as the resulting feature follows the conditioned described.\n",
"\n",
"These three fields can be expressed as strings, column names from the source dataset, or as Jinja2 templates that can use fields from the source dataset as variables.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6p0-KPwAgK5j"
},
"source": [
"## What if Jinja is not Sufficient?\n",
"\n",
"There can be times where the Jinja2 templating language is not enough to make the prompt we had in mind. There are a few ways to circumvent this limitation:\n",
"\n",
"1. Use `!function` operator for the prompt-related fields to pass a python function that takes as input the dataset row, and will output the prompt template component.\n",
"2. Perform a transformation on the dataset beforehand."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, we show an example of using `!function` to create `doc_to_text` from a python function:"
"If we take a look at the samples, we can see that it is in fact evaluating the continuation based on the choices rather than the letters."
]
"name": "stdout",
"output_type": "stream",
"text": [
"2023-11-29:11:59:08,312 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:59:09.348327: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:59:09.348387: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:59:09.348421: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:59:10.573752: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:59:14,044 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:59:23,654 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:59:23,654 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:59:23,678 INFO [__main__.py:205] Selected Tasks: ['demo_mmlu_high_school_geography_function_prompt']\n",
"2023-11-29:11:59:23,679 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:59:23,708 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:59:44,516 INFO [task.py:355] Building contexts for task on rank 0...\n",
"2023-11-29:11:59:44,524 INFO [evaluator.py:319] Running loglikelihood requests\n",
"100% 40/40 [00:02<00:00, 15.41it/s]\n",
"fatal: not a git repository (or any of the parent directories): .git\n",
"Next, we'll also show how to do this via preprocessing the dataset as necessary using the `process_docs` config field:\n",
"\n",
"We will write a function that will modify each document in our evaluation dataset's split to add a field that is suitable for us to use in `doc_to_text`."
"We hope that this explainer gives you a sense of what can be done with and how to work with LM-Evaluation-Harnes v0.4.0 ! \n",
"\n",
"For more information, check out our documentation pages in the `docs/` folder, and if you have questions, please raise them in GitHub issues, or in #lm-thunderdome or #release-discussion on the EleutherAI discord server."
"To prepare a task we can simply fill in a YAML config with the relevant information.\n",
"\n",
"`output_type`\n",
"The current provided evaluation types comprise of the following:\n",
"1. `loglikelihood`: Evaluates the loglikelihood of a continuation, conditioned on some input string.\n",
"2. `loglikelihood_rolling`: evaluate the loglikelihood of producing a string, conditioned on the empty string. (Used for perplexity evaluations)\n",
"3. `multiple_choice`: Evaluates loglikelihood among the a number of choices predicted by the model.\n",
"4. `greedy_until`: Model outputs greedy generation (can be configured to to use beam search and other generation-related parameters)\n",
"\n",
"The core prompt revolves around 3 fields.\n",
"1. `doc_to_text`: Denotes the prompt template that will be used as input to the model.\n",
"2. `doc_to_choice`: Available choices that will be used as continuation for the model. This is used when the `output_type` is `multiple_choice`, and otherwise can be left as `None`.\n",
"3. `doc_to_target`: When `output_type` is `multiple_choice`, this can be an index that corresponds to the correct answer, or the answer string itself (must be a subset of `doc_to_choice`). For other tasks, this is expected to be a string. You can fill this field with a feature name from the HF dataset so long as the resulting feature follows the conditioned described.\n",
"\n",
"These three fields can be expressed as strings, column names from the source dataset, or as Jinja2 templates that can use fields from the source dataset as variables.\n"
]
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"source": [
"## What if Jinja is not Sufficient?\n",
"\n",
"There can be times where the Jinja2 templating language is not enough to make the prompt we had in mind. There are a few ways to circumvent this limitation:\n",
"\n",
"1. Use `!function` operator for the prompt-related fields to pass a python function that takes as input the dataset row, and will output the prompt template component.\n",
"2. Perform a transformation on the dataset beforehand."
]
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"source": [
"Below, we show an example of using `!function` to create `doc_to_text` from a python function:"
"2023-11-29:11:59:08,312 INFO [utils.py:160] NumExpr defaulting to 2 threads.\n",
"2023-11-29 11:59:09.348327: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-11-29 11:59:09.348387: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-11-29 11:59:09.348421: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-11-29 11:59:10.573752: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2023-11-29:11:59:14,044 INFO [__main__.py:132] Verbosity set to INFO\n",
"2023-11-29:11:59:23,654 WARNING [__main__.py:138] --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.\n",
"2023-11-29:11:59:23,654 INFO [__main__.py:143] Including path: ./\n",
"2023-11-29:11:59:23,678 INFO [__main__.py:205] Selected Tasks: ['demo_mmlu_high_school_geography_function_prompt']\n",
"2023-11-29:11:59:23,679 WARNING [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.\n",
"2023-11-29:11:59:23,708 INFO [huggingface.py:120] Using device 'cuda'\n",
"2023-11-29:11:59:44,516 INFO [task.py:355] Building contexts for task on rank 0...\n",
"2023-11-29:11:59:44,524 INFO [evaluator.py:319] Running loglikelihood requests\n",
"100% 40/40 [00:02<00:00, 15.41it/s]\n",
"fatal: not a git repository (or any of the parent directories): .git\n",
"Next, we'll also show how to do this via preprocessing the dataset as necessary using the `process_docs` config field:\n",
"\n",
"We will write a function that will modify each document in our evaluation dataset's split to add a field that is suitable for us to use in `doc_to_text`."
"We hope that this explainer gives you a sense of what can be done with and how to work with LM-Evaluation-Harnes v0.4.0 ! \n",
"\n",
"For more information, check out our documentation pages in the `docs/` folder, and if you have questions, please raise them in GitHub issues, or in #lm-thunderdome or #release-discussion on the EleutherAI discord server."
"A task YAML file was found to contain a `group` key. Groups which provide aggregate scores over several subtasks now require a separate config file--if not aggregating, you may want to use the `tag` config option instead within your config. Setting `group` within a TaskConfig will be deprecated in v0.4.4. Please see https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/task_guide.md for more information."
)
ifself.tagisNone:
self.tag=self.group
else:
raiseValueError(
"Got both a `group` and `tag` entry within a TaskConfig. Please use one or the other--`group` values will be deprecated in v0.4.4."
)
ifself.generation_kwargsisnotNone:
ifself.output_type!="generate_until":
eval_logger.warning(
...
...
@@ -1511,7 +1498,7 @@ class ConfigurableTask(Task):
# we expect multiple_targets to be a list.
elifself.multiple_target:
gold=list(gold)
eliftype(gold)!=type(result):
eliftype(gold)isnottype(result):
# cast gold to the same type as result
gold=type(result)(gold)
...
...
@@ -1594,7 +1581,7 @@ class ConfigurableTask(Task):
@@ -226,6 +230,11 @@ class OpenAIChatCompletion(LocalChatCompletion):
key=os.environ.get("OPENAI_API_KEY",None)
ifkeyisNone:
raiseValueError(
"API key not found. Please set the OPENAI_API_KEY environment variable."
"API key not found. Please set the `OPENAI_API_KEY` environment variable."
)
returnkey
defloglikelihood(self,requests,**kwargs):
raiseNotImplementedError(
"Loglikelihood (and therefore `multiple_choice`-type tasks) is not supported for chat completions as OpenAI does not provide prompt logprobs. See https://github.com/EleutherAI/lm-evaluation-harness/issues/942#issuecomment-1777836312 or https://github.com/EleutherAI/lm-evaluation-harness/issues/1196 for more background on this limitation."
| [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) |
...
...
@@ -25,6 +26,7 @@
| [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 |
...
...
@@ -42,6 +44,7 @@
| [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 |
| [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 |
...
...
@@ -86,6 +89,7 @@
| [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 |
...
...
@@ -95,6 +99,7 @@
| [sciq](sciq/README.md) | Science Question Answering tasks to assess understanding of scientific concepts. | 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 |
...
...
@@ -107,6 +112,7 @@
| [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 |
"`group` and `group_alias` keys in TaskConfigs are deprecated and will be removed in v0.4.5 of lm_eval. "
"The new `tag` field will be used to allow for a shortcut to a group of tasks one does not wish to aggregate metrics across. "
"`group`s which aggregate across subtasks must be only defined in a separate group config file, "
"which will be the official way to create groups that support cross-task aggregation as in `mmlu`. "
"Please see the v0.4.4 patch notes and our documentation: https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/new_task_guide.md#advanced-group-configs "
"for more information."
)
print_info=False
# attr = "tag"
attr_list=config[attr]
ifisinstance(attr_list,str):
attr_list=[attr_list]
fortaginattr_list:
iftagnotintasks_and_groups:
tasks_and_groups[tag]={
"type":"tag",
"task":[task],
"yaml_path":-1,
}
eliftasks_and_groups[tag]["type"]!="tag":
self.logger.info(
f"The tag {tag} is already registered as a group, this tag will not be registered. "
"This may affect tasks you want to call."
)
break
else:
tasks_and_groups[tag]["task"].append(task)
_populate_tags_and_groups(
config,task,tasks_and_groups,print_info
)
else:
self.logger.debug(f"File {f} in {root} could not be loaded")
BasqueBench is a benchmark for evaluating language models in Basque tasks. This is, it evaluates the ability of a language model to understand and generate Basque text. BasqueBench offers a combination of pre-existing, open datasets and datasets developed exclusivelly for this benchmark. All the details of BasqueBench will be published in a paper soon.
The new evaluation datasets included in BasqueBench are:
| Task | Category | Homepage |
|:-------------:|:-----:|:-----:|
| MGSM_eu | Math | https://huggingface.co/datasets/HiTZ/MGSM-eu |
| WNLI_eu | Natural Language Inference | https://huggingface.co/datasets/HiTZ/wnli-eu |
The datasets included in BasqueBench that have been made public in previous pubications are:
| Task | Category | Paper title | Homepage |
|:-------------:|:-----:|:-------------:|:-----:|
| Belebele_eu | Reading Comprehension | [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884) | https://huggingface.co/datasets/facebook/belebele |
| EusExams | Question Answering | [Latxa: An Open Language Model and Evaluation Suite for Basque](https://arxiv.org/abs/2403.20266) | https://huggingface.co/datasets/HiTZ/EusExams |
| EusProficiency | Question Answering | [Latxa: An Open Language Model and Evaluation Suite for Basque](https://arxiv.org/abs/2403.20266) | https://huggingface.co/datasets/HiTZ/EusProficiency |
| EusReading | Reading Comprehension | [Latxa: An Open Language Model and Evaluation Suite for Basque](https://arxiv.org/abs/2403.20266) | https://huggingface.co/datasets/HiTZ/EusReading |
| EusTrivia | Question Answering | [Latxa: An Open Language Model and Evaluation Suite for Basque](https://arxiv.org/abs/2403.20266) | https://huggingface.co/datasets/HiTZ/EusTrivia |
| FLORES_eu | Translation | [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) | https://huggingface.co/datasets/facebook/flores |
| QNLIeu | Natural Language Inference | [BasqueGLUE: A Natural Language Understanding Benchmark for Basque](https://aclanthology.org/2022.lrec-1.172/) | https://huggingface.co/datasets/orai-nlp/basqueGLUE |
| XNLIeu | Natural Language Inference | [XNLIeu: a dataset for cross-lingual NLI in Basque](https://arxiv.org/abs/2404.06996) | https://huggingface.co/datasets/HiTZ/xnli-eu |
| XStoryCloze_eu | Commonsense Reasoning | [Few-shot Learning with Multilingual Generative Language Models](https://aclanthology.org/2022.emnlp-main.616/) | https://huggingface.co/datasets/juletxara/xstory_cloze |
### Citation
Paper for BasqueBench coming soon.
### Groups and Tasks
#### Groups
-`basque_bench`: All tasks included in BasqueBench.
-`flores_eu`: All FLORES translation tasks from or to Basque.
#### Tasks
The following tasks evaluate tasks on BasqueBench dataset using various scoring methods.
-`belebele_eus_Latn`
-`eus_exams_eu`
-`eus_proficiency`
-`eus_reading`
-`eus_trivia`
-`flores_eu`
-`flores_eu-ca`
-`flores_eu-de`
-`flores_eu-en`
-`flores_eu-es`
-`flores_eu-fr`
-`flores_eu-gl`
-`flores_eu-it`
-`flores_eu-pt`
-`flores_ca-eu`
-`flores_de-eu`
-`flores_en-eu`
-`flores_es-eu`
-`flores_fr-eu`
-`flores_gl-eu`
-`flores_it-eu`
-`flores_pt-eu`
-`mgsm_direct_eu`
-`mgsm_native_cot_eu`
-`qnlieu`
-`wnli_eu`
-`xcopa_eu`
-`xnli_eu`
-`xnli_eu_native`
-`xstorycloze_eu`
Some of these tasks are taken from benchmarks already available in LM Evaluation Harness. These are:
-`belebele_eus_Latn`: Belebele Basque
-`qnlieu`: From BasqueGLUE
### Checklist
* [x] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation?
* [ ] Yes, original implementation contributed by author of the benchmark
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
