update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- ncbi_disease
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: ncbi_disease
+      type: ncbi_disease
+      args: ncbi_disease
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8288508557457213
+    - name: Recall
+      type: recall
+      value: 0.8614993646759848
+    - name: F1
+      type: f1
+      value: 0.8448598130841122
+    - name: Accuracy
+      type: accuracy
+      value: 0.9861487755016897
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner
+
+This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the ncbi_disease dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0629
+- Precision: 0.8289
+- Recall: 0.8615
+- F1: 0.8449
+- Accuracy: 0.9861
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0554        | 1.0   | 1359 | 0.0659          | 0.7814    | 0.8132 | 0.7970 | 0.9825   |
+| 0.0297        | 2.0   | 2718 | 0.0445          | 0.8284    | 0.8895 | 0.8578 | 0.9876   |
+| 0.0075        | 3.0   | 4077 | 0.0629          | 0.8289    | 0.8615 | 0.8449 | 0.9861   |
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.10.2+cu102
+- Datasets 2.3.2
+- Tokenizers 0.12.1