update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- jnlpba
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: biobert-base-cased-v1.2-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: jnlpba
+      type: jnlpba
+      args: jnlpba
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8948080842655547
+    - name: Recall
+      type: recall
+      value: 0.9282417121275703
+    - name: F1
+      type: f1
+      value: 0.9112183219652858
+    - name: Accuracy
+      type: accuracy
+      value: 0.9601644367242017
+---
+
+<!-- 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-finetuned-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 jnlpba dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1265
+- Precision: 0.8948
+- Recall: 0.9282
+- F1: 0.9112
+- Accuracy: 0.9602
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2278        | 1.0   | 1858 | 0.1826          | 0.8415    | 0.8815 | 0.8610 | 0.9384   |
+| 0.151         | 2.0   | 3716 | 0.1443          | 0.8756    | 0.9162 | 0.8955 | 0.9530   |
+| 0.1157        | 3.0   | 5574 | 0.1265          | 0.8948    | 0.9282 | 0.9112 | 0.9602   |
+
+
+### Framework versions
+
+- Transformers 4.12.0.dev0
+- Pytorch 1.9.1+cu102
+- Datasets 1.12.1
+- Tokenizers 0.10.3