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README.md

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+---
+language: en
+tags:
+  - veterinary
+  - pets
+  - vetbert
+  - BERT
+widget:
+  - text: >-
+      Hx: 7 yo canine with history of vomiting intermittently since yesterday.
+      No other concerns. Still eating and drinking [MASK]. cPL negative.
+    example_title: normally
+---
+
+# VetBERT Disease Syndrome Classifier
+
+This is a finetuned version of the VetBERT model, designed to classify the disease syndrome within a veterinary clinical note.
+
+<!-- Provide a quick summary of what the model is/does. -->
+This pretrained model is designed for performing NLP tasks related to veterinary clinical notes. The [Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes](https://aclanthology.org/2020.bionlp-1.17) (Hur et al., BioNLP 2020) paper introduced VetBERT model: an initialized Bert Model with ClinicalBERT (Bio+Clinical BERT) and further pretrained on the [VetCompass Australia](https://www.vetcompass.com.au/) corpus for performing tasks specific to veterinary medicine.
+
+## Pretraining Data
+
+The VetBERT model was initialized from [Bio_ClinicalBERT model](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT), which was initialized from BERT. The VetBERT model was trained on over 15 million veterinary clincal Records and 1.3 Billion tokens.
+
+## Pretraining Hyperparameters
+
+During the pretraining phase for VetBERT, we used a batch size of 32, a maximum sequence length of 512, and a learning rate of 5 · 10−5. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15 and max predictions per sequence = 20).
+
+## VetBERT Finetuning
+
+VetBERT was further finetuned on a set of 5002 annotated clinical notes to classifiy the disease syndrome associated with the clinical notes as outlined in the paper:  [Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes](https://aclanthology.org/2020.bionlp-1.17)
+
+## How to use the model
+
+Load the model via the transformers library:
+
+```
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("havocy28/VetBERTDx")
+model = AutoModel.from_pretrained("havocy28/VetBERTDx")
+```
+
+## Citation
+
+Please cite this article: Brian Hur, Timothy Baldwin, Karin Verspoor, Laura Hardefeldt, and James Gilkerson. 2020. [Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes](https://aclanthology.org/2020.bionlp-1.17). In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 156–166, Online. Association for Computational Linguistics. 
+

config.json

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-{"_name_or_path": "./data/model", "architectures": ["BertForSequenceClassification"], "attention_probs_dropout_prob": 0.1, "classifier_dropout": null, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "id2label": {"0": "abscess", "1": "adverse reaction to drug", "2": "appetite disorder", "3": "blank", "4": "blood test", "5": "claw/nail disorder", "6": "dental disorder", "7": "disorder not diagnosed", "8": "ear disorder", "9": "endocrine system disorder", "10": "enteropathy", "11": "female reproductive disorder", "12": "fever (undiagnosed)", "13": "foreign body", "14": "heart disorder", "15": "intoxication", "16": "kidney disorder", "17": "lethargy", "18": "liver disorder", "19": "lower respiratory tract disorder", "20": "lymph node disorder", "21": "male reproductive disorder", "22": "mass", "23": "musculoskeletal disorder", "24": "neoplasia", "25": "neurological disorder", "26": "ophthalmological disorder", "27": "oral cavity disorder", "28": "other species", "29": "palliative", "30": "pancreatic disorder", "31": "post operative complication", "32": "prophylaxis", "33": "repeat", "34": "skin disorder", "35": "traumatic injury", "36": "unknown", "37": "upper respiratory tract disorder", "38": "urinary tract disorder"}, "initializer_range": 0.02, "intermediate_size": 3072, "label2id": {"abscess": "0", "adverse reaction to drug": "1", "appetite disorder": "2", "blank": "3", "blood test": "4", "claw/nail disorder": "5", "dental disorder": "6", "disorder not diagnosed": "7", "ear disorder": "8", "endocrine system disorder": "9", "enteropathy": "10", "female reproductive disorder": "11", "fever (undiagnosed)": "12", "foreign body": "13", "heart disorder": "14", "intoxication": "15", "kidney disorder": "16", "lethargy": "17", "liver disorder": "18", "lower respiratory tract disorder": "19", "lymph node disorder": "20", "male reproductive disorder": "21", "mass": "22", "musculoskeletal disorder": "23", "neoplasia": "24", "neurological disorder": "25", "ophthalmological disorder": "26", "oral cavity disorder": "27", "other species": "28", "palliative": "29", "pancreatic disorder": "30", "post operative complication": "31", "prophylaxis": "32", "repeat": "33", "skin disorder": "34", "traumatic injury": "35", "unknown": "36", "upper respiratory tract disorder": "37", "urinary tract disorder": "38"}, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "position_embedding_type": "absolute", "problem_type": "single_label_classification", "torch_dtype": "float32", "transformers_version": "4.20.1", "type_vocab_size": 2, "use_cache": true, "vocab_size": 28996}
+{
+  "architectures": [
+    "BertForPreTraining"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

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