We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
MT model translating Acholi to English initialized with weights from [opus-mt-luo-en](https://huggingface.co/Helsinki-NLP/opus-mt-luo-en) on HuggingFace.
## Intended uses & limitations
Machine Translation experiments. Do not use for sensitive tasks.
#### How to use
```python
# You can include sample code which will be formatted
Trained on Jehovah Witnesses data so contains theirs and Christian views.
## Training data
Trained on OPUS JW300 data.
Initialized with weights from [opus-mt-luo-en](https://huggingface.co/Helsinki-NLP/opus-mt-luo-en?text=Bed+gi+nyasi+mar+chieng%27+nyuol+mopong%27+gi+mor%21#model_card)
## Training procedure
Remove duplicates and rows with no alphabetic characters. Used GPU
# Roberta Masked Language Model Trained On Financial Phrasebank Corpus
This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a Financial Phrasebank Corpus.
The model is built using Huggingface transformers.
The model can be found at :[Financial_Roberta](https://huggingface.co/abhilash1910/financial_roberta)
## Specifications
The corpus for training is taken from the Financial Phrasebank (Malo et al)[https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts].
## Model Specification
The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications:
1. vocab_size=56000
2. max_position_embeddings=514
3. num_attention_heads=12
4. num_hidden_layers=6
5. type_vocab_size=1
This is trained by using RobertaConfig from transformers package.
The model is trained for 10 epochs with a gpu batch size of 64 units.
## Usage Specifications
For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers
After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model.
IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT, XLM-R etc.) while it also achieves a performance on-par or better than these models.
The 12 languages covered by IndicBERT are: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.
The code can be found [here](https://github.com/divkakwani/indic-bert). For more information, checkout our [project page](https://indicnlp.ai4bharat.org/) or our [paper](https://indicnlp.ai4bharat.org/papers/arxiv2020_indicnlp_corpus.pdf).
## Pretraining Corpus
We pre-trained indic-bert on AI4Bharat's monolingual corpus. The corpus has the following distribution of languages:
IndicBERT is evaluated on IndicGLUE and some additional tasks. The results are summarized below. For more details about the tasks, refer our [official repo](https://github.com/divkakwani/indic-bert)
\* Note: all models have been restricted to a max_seq_length of 128.
## Downloads
The model can be downloaded [here](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/models/indic-bert-v1.tar.gz). Both tf checkpoints and pytorch binaries are included in the archive. Alternatively, you can also download it from [Huggingface](https://huggingface.co/ai4bharat/indic-bert).
## Citing
If you are using any of the resources, please cite the following article:
```
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
```
We would like to hear from you if:
- You are using our resources. Please let us know how you are putting these resources to use.
- You have any feedback on these resources.
## License
The IndicBERT code (and models) are released under the MIT License.
## Contributors
- Divyanshu Kakwani
- Anoop Kunchukuttan
- Gokul NC
- Satish Golla
- Avik Bhattacharyya
- Mitesh Khapra
- Pratyush Kumar
This work is the outcome of a volunteer effort as part of [AI4Bharat initiative](https://ai4bharat.org).
A BERT model pre-trained on PubMed abstracts and clinical notes ([MIMIC-III](https://mimic.physionet.org/)).
## Intended uses & limitations
#### How to use
Please see https://github.com/ncbi-nlp/bluebert
## Training data
We provide [preprocessed PubMed texts](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/pubmed_uncased_sentence_nltk.txt.tar.gz) that were used to pre-train the BlueBERT models.
The corpus contains ~4000M words extracted from the [PubMed ASCII code version](https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PubMed/).
* tokenizing the text using the [NLTK Treebank tokenizer](https://www.nltk.org/_modules/nltk/tokenize/treebank.html)
Below is a code snippet for more details.
```python
value=value.lower()
value=re.sub(r'[\r\n]+',' ',value)
value=re.sub(r'[^\x00-\x7F]+',' ',value)
tokenized=TreebankWordTokenizer().tokenize(value)
sentence=' '.join(tokenized)
sentence=re.sub(r"\s's\b","'s",sentence)
```
### BibTeX entry and citation info
```bibtex
@InProceedings{peng2019transfer,
author={Yifan Peng and Shankai Yan and Zhiyong Lu},
title={Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets},
booktitle={Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019)},
year={2019},
pages={58--65},
}
```
### Acknowledgments
This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of
Medicine and Clinical Center. This work was supported by the National Library of Medicine of the National Institutes of Health under award number 4R00LM013001-01.
We are also grateful to the authors of BERT and ELMo to make the data and codes publicly available.
We would like to thank Dr Sun Kim for processing the PubMed texts.
### Disclaimer
This tool shows the results of research conducted in the Computational Biology Branch, NCBI. The information produced
on this website is not intended for direct diagnostic use or medical decision-making without review and oversight
by a clinical professional. Individuals should not change their health behavior solely on the basis of information
produced on this website. NIH does not independently verify the validity or utility of the information produced
by this tool. If you have questions about the information produced on this website, please see a health care
professional. More information about NCBI's disclaimer policy is available.
We provide [preprocessed PubMed texts](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/pubmed_uncased_sentence_nltk.txt.tar.gz) that were used to pre-train the BlueBERT models.
The corpus contains ~4000M words extracted from the [PubMed ASCII code version](https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PubMed/).
* tokenizing the text using the [NLTK Treebank tokenizer](https://www.nltk.org/_modules/nltk/tokenize/treebank.html)
Below is a code snippet for more details.
```python
value=value.lower()
value=re.sub(r'[\r\n]+',' ',value)
value=re.sub(r'[^\x00-\x7F]+',' ',value)
tokenized=TreebankWordTokenizer().tokenize(value)
sentence=' '.join(tokenized)
sentence=re.sub(r"\s's\b","'s",sentence)
```
### BibTeX entry and citation info
```bibtex
@InProceedings{peng2019transfer,
author={Yifan Peng and Shankai Yan and Zhiyong Lu},
title={Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets},
booktitle={Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019)},
year={2019},
pages={58--65},
}
```
### Acknowledgments
This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of
Medicine and Clinical Center. This work was supported by the National Library of Medicine of the National Institutes of Health under award number 4R00LM013001-01.
We are also grateful to the authors of BERT and ELMo to make the data and codes publicly available.
We would like to thank Dr Sun Kim for processing the PubMed texts.
### Disclaimer
This tool shows the results of research conducted in the Computational Biology Branch, NCBI. The information produced
on this website is not intended for direct diagnostic use or medical decision-making without review and oversight
by a clinical professional. Individuals should not change their health behavior solely on the basis of information
produced on this website. NIH does not independently verify the validity or utility of the information produced
by this tool. If you have questions about the information produced on this website, please see a health care
professional. More information about NCBI's disclaimer policy is available.
EnDR-BERT - Multilingual, Cased, which pretrained on the collecting of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \
## EnDR-BERT
EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QuaRTz](https://allenai.org/data/quartz) for **QA** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the dataset 📚
**QuaRTz** is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs). The QuaRTz dataset V1 contains 3864 questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs).
The dataset is split into:
|Set | Samples|
|-----|--------|
|Train | 2696 |
|Valid | 384 |
|Test | 784 |
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28). The *question*, *context* (`para` field) and *options* (`choices` field) are concatenated and passed to the **encoder**. The **decoder** receives the right *answer* (by querying `answerKey` field). More details about the dataset fields/format [here](https://huggingface.co/nlp/viewer/?dataset=quartz)
This is a fine-tuned model on Medical domain for German language and based on German BERT.
This is a fine-tuned model on Medical domain for German language and based on German BERT. This model has only been trained to improve on target task (Masked Language Model). It can later be used to perform a downstream task of your needs, while I performed it for NTS-ICD-10 text classification task.
## Overview
## Overview
**Language model:** bert-base-german-cased
**Language model:** bert-base-german-cased
...
@@ -30,7 +31,12 @@ This is a fine-tuned model on Medical domain for German language and based on Ge
...
@@ -30,7 +31,12 @@ This is a fine-tuned model on Medical domain for German language and based on Ge
- Although had to train for upto 25 epochs for classification.
- Although had to train for upto 25 epochs for classification.
## Performance (Micro precision, recall and f1 score for multilabel code classification)
## Performance (Micro precision, recall and f1 score for multilabel code classification)
The model is used to generate Chinese couplets. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-couplet][couplet].
Since the parameter skip_special_tokens is used in the pipelines.py, special tokens such as [SEP], [UNK] will be deleted, and the output results may not be neat.
## How to use
You can use the model directly with a pipeline for text generation:
Contains 700,000 Chinese couplets collected by [couplet-clean-dataset](https://github.com/v-zich/couplet-clean-dataset).
## Training procedure
Models are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). We pre-train 25,000 steps with a sequence length of 64.
The model is used to generate Chinese ancient poems. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-poem][poem].
Since the parameter skip_special_tokens is used in the pipelines.py, special tokens such as [SEP], [UNK] will be deleted, and the output results may not be neat.
## How to use
You can use the model directly with a pipeline for text generation:
Contains 800,000 Chinese ancient poems collected by [chinese-poetry](https://github.com/chinese-poetry/chinese-poetry) and [Poetry](https://github.com/Werneror/Poetry) projects.
## Training procedure
The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). We pre-train 200,000 steps with a sequence length of 128.
author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Sam Shleifer, Patrick von Platen, Sylvain Gugger, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Sam Shleifer, Patrick von Platen, Sylvain Gugger, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
author_email="thomas@huggingface.co",
author_email="thomas@huggingface.co",
description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
