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README (6).md

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+---
+tags:
+- medical
+---
+# ClinicalBERT
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model card describes the ClinicalBERT model, which was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed.
+We then utilized a large-scale corpus of EHRs from over 3 million patient records to fine tune the base language model.
+
+## Pretraining Data
+
+The ClinicalBERT model was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed.
+<!-- For more details, see here.  -->
+
+## Model Pretraining
+
+### Pretraining Procedures
+The ClinicalBERT was initialized from BERT. Then the training followed the principle of masked language model, in which given a piece of text, we randomly replace some tokens by MASKs, 
+special tokens for masking, and then require the model to predict the original tokens via contextual text. 
+
+### Pretraining Hyperparameters
+
+We used a batch size of 32, a maximum sequence length of 256, and a learning rate of 5e-5 for pre-training our models. 
+
+## How to use the model
+
+Load the model via the transformers library:
+```python
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("medicalai/ClinicalBERT")
+model = AutoModel.from_pretrained("medicalai/ClinicalBERT")
+```
+
+## Citation
+
+Please cite this article:  Wang, G., Liu, X., Ying, Z. et al. Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial. Nat Med (2023). https://doi.org/10.1038/s41591-023-02552-9

config (1).json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "vocab_size": 119547
+}

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