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-mnli-snli-scinli-scitail-mednli-stsb-ncbi
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: ncbi_disease
+      type: ncbi_disease
+      config: ncbi_disease
+      split: test
+      args: ncbi_disease
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8604187437686939
+    - name: Recall
+      type: recall
+      value: 0.8989583333333333
+    - name: F1
+      type: f1
+      value: 0.879266428935303
+    - name: Accuracy
+      type: accuracy
+      value: 0.9870188186308527
+---
+
+<!-- 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-mnli-snli-scinli-scitail-mednli-stsb-ncbi
+
+This model is a fine-tuned version of [pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb](https://huggingface.co/pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb) on the ncbi_disease dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0814
+- Precision: 0.8604
+- Recall: 0.8990
+- F1: 0.8793
+- Accuracy: 0.9870
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 340  | 0.0481          | 0.8308    | 0.8438 | 0.8372 | 0.9840   |
+| 0.0715        | 2.0   | 680  | 0.0497          | 0.8337    | 0.8771 | 0.8548 | 0.9857   |
+| 0.0152        | 3.0   | 1020 | 0.0588          | 0.8596    | 0.8802 | 0.8698 | 0.9858   |
+| 0.0152        | 4.0   | 1360 | 0.0589          | 0.8589    | 0.8875 | 0.8730 | 0.9873   |
+| 0.0059        | 5.0   | 1700 | 0.0693          | 0.8412    | 0.8938 | 0.8667 | 0.9852   |
+| 0.003         | 6.0   | 2040 | 0.0770          | 0.8701    | 0.9    | 0.8848 | 0.9863   |
+| 0.003         | 7.0   | 2380 | 0.0787          | 0.861     | 0.8969 | 0.8786 | 0.9863   |
+| 0.0014        | 8.0   | 2720 | 0.0760          | 0.8655    | 0.8979 | 0.8814 | 0.9872   |
+| 0.0007        | 9.0   | 3060 | 0.0817          | 0.8589    | 0.8938 | 0.8760 | 0.9865   |
+| 0.0007        | 10.0  | 3400 | 0.0814          | 0.8604    | 0.8990 | 0.8793 | 0.9870   |
+
+
+### Framework versions
+
+- Transformers 4.29.1
+- Pytorch 2.0.1+cpu
+- Datasets 2.12.0
+- Tokenizers 0.13.3