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README.md

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+# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
+
+# Abstract
+
+
+The impact of design choices on the performance
+of biomedical language models recently
+has been a subject for investigation. In
+this paper, we empirically study biomedical
+domain adaptation with large transformer models
+using different design choices. We evaluate
+the performance of our pretrained models
+against other existing biomedical language
+models in the literature. Our results show that
+we achieve state-of-the-art results on several
+biomedical domain tasks despite using similar
+or less computational cost compared to other
+models in the literature. Our findings highlight
+the significant effect of design choices on
+improving the performance of biomedical language
+models.
+
+# Model Description
+
+This model is fine-tuned on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ELECTRA-Base. 
+
+Huggingface library doesn't implement Layer-Wise decay feature, which affects the performance on SQuAD task. The reported result of BioM-ELECTRA-Base-SQuAD in our paper is 84.4 (F1) since we use ELECTRA open-source code with TF checkpoint, which uses Layer-Wise decay. 
+
+
+Evaluation results on SQuAD2.0 Dev Dataset
+```
+  eval_HasAns_exact      = 79.2679
+  eval_HasAns_f1         = 86.5416
+  eval_HasAns_total      =    5928
+  eval_NoAns_exact       = 75.8789
+  eval_NoAns_f1          = 75.8789
+  eval_NoAns_total       =    5945
+  eval_best_exact        =  77.571
+  eval_best_exact_thresh =     0.0
+  eval_best_f1           = 81.2026
+  eval_best_f1_thresh    =     0.0
+  eval_exact             =  77.571
+  eval_f1                = 81.2026
+  eval_samples           =   11979
+  eval_total             =   11873
+
+```
+
+
+- First make sure to install all libraries on Google Colab and make sure GPU is enabled
+
+```python
+!git  clone https://github.com/huggingface/transformers
+
+!pip3 install -e transformers
+
+!pip3 install sentencepiece
+
+!pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt
+
+```
+
+- Training script
+
+```python
+python3 transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Base-Discriminator \
+--dataset_name squad_v2 \
+--do_train \
+--do_eval \
+--dataloader_num_workers 20 \
+--preprocessing_num_workers 20 \
+--version_2_with_negative \
+--num_train_epochs 3 \
+--learning_rate 4e-5 \
+--max_seq_length 512 \
+--doc_stride 128 \
+--per_device_train_batch_size 8 \
+--gradient_accumulation_steps 3 \
+--per_device_eval_batch_size 128 \
+--fp16 \
+--fp16_opt_level O1 \
+--logging_steps 50 \
+--save_steps 5000 \
+--overwrite_output_dir \
+--output_dir out
+```
+
+
+- Reproduce results without training ( only eval):
+
+```python
+python transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Base-SQuAD2 \
+--do_eval \
+--version_2_with_negative \
+--per_device_eval_batch_size 8 \
+--dataset_name squad_v2 \
+--overwrite_output_dir \
+--fp16 \
+--output_dir out
+```
+
+- You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
+
+- Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints. 
+
+
+# Acknowledgment
+
+We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
+
+# Citation
+
+
+```bibtex
+@inproceedings{alrowili-shanker-2021-biom,
+title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
+author = "Alrowili, Sultan and
+Shanker, Vijay",
+booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
+month = jun,
+year = "2021",
+address = "Online",
+publisher = "Association for Computational Linguistics",
+url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
+pages = "221--227",
+abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
+}
+```

config.json

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+{
+  "_name_or_path": "sultan/BioM-ELECTRA-Base-Discriminator",
+  "architectures": [
+    "ElectraForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "embedding_size": 768,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.9.0.dev0",
+  "type_vocab_size": 2,
+  "vocab_size": 28895
+}

pytorch_model.bin

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