initial commit

README.md

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+This model repository presents "TinySapBERT", a tiny-sized SapBERT model trained using [official SapBERT code and instructions (Liu et al., NAACL 2021)](https://github.com/cambridgeltl/sapbert). 
+We used our [TinyPubMedBERT](https://huggingface.co/dmis-lab/TinyPubMedBERT-v1.0), a tiny-sized LM, as an initial starting point to train using the SapBERT scheme.
+<br>
+cf) TinyPubMedBERT is a distillated [PubMedBERT (Gu et al., 2021)](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract), open-sourced along with the release of the KAZU (Korea University and AstraZeneca) framework.
+
+* For details, please visit [KAZU framework](https://github.com/AstraZeneca/KAZU) or see our paper entitled **Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework**, (EMNLP 2022 industry track).
+
+
+### Citation info
+Joint-first authorship of **Richard Jackson** (AstraZeneca) and **WonJin Yoon** (Korea University).
+<br>Please cite: (Full citation info will be announced soon)
+```
+@inproceedings{YoonAndJackson2022BiomedicalNER,
+  title={Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework},
+  author={Wonjin Yoon, Richard Jackson, Elliot Ford, Vladimir Poroshin, Jaewoo Kang},
+  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+  year={2022}
+}
+```
+
+The model used resources of [SapBERT paper](https://aclanthology.org/2021.naacl-main.334.pdf). We appreciate the authors for making the resources publicly available!
+```
+Liu, Fangyu, et al. "Self-Alignment Pretraining for Biomedical Entity Representations." 
+Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
+```
+
+### Contact Information
+For help or issues using the codes or model (NER module of KAZU) in this repository, please contact WonJin Yoon (wonjin.info (at) gmail.com) or submit a GitHub issue.

config.json

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+{
+  "_name_or_path": "/hdd2/wonjin/SapBERT-data/ckpt/TinyPubMedBERT-epoch3-41k",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "cell": {},
+  "classifier_dropout": null,
+  "emb_size": 312,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 312,
+  "initializer_range": 0.02,
+  "intermediate_size": 1200,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "pre_trained": "",
+  "structure": [],
+  "torch_dtype": "float32",
+  "training": "",
+  "transformers_version": "4.12.5",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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special_tokens_map.json

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tokenizer_config.json

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