update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: BioLinkBERT-LitCovid-v1.2.1
+  results: []
+---
+
+<!-- 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. -->
+
+# BioLinkBERT-LitCovid-v1.2.1
+
+This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2205
+- F1 micro: 0.9016
+- F1 macro: 0.8505
+- F1 weighted: 0.9044
+- F1 samples: 0.9056
+- Precision micro: 0.8545
+- Precision macro: 0.7857
+- Precision weighted: 0.8625
+- Precision samples: 0.8862
+- Recall micro: 0.9540
+- Recall macro: 0.9431
+- Recall weighted: 0.9540
+- Recall samples: 0.9610
+- Roc Auc: 0.9578
+- Accuracy: 0.7211
+
+## 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: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1 micro | F1 macro | F1 weighted | F1 samples | Precision micro | Precision macro | Precision weighted | Precision samples | Recall micro | Recall macro | Recall weighted | Recall samples | Roc Auc | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.2839        | 1.0   | 2211 | 0.2205          | 0.9016   | 0.8505   | 0.9044      | 0.9056     | 0.8545          | 0.7857          | 0.8625             | 0.8862            | 0.9540       | 0.9431       | 0.9540          | 0.9610         | 0.9578  | 0.7211   |
+| 0.1926        | 2.0   | 4422 | 0.2477          | 0.9134   | 0.8734   | 0.9147      | 0.9159     | 0.8770          | 0.8309          | 0.8808             | 0.9026            | 0.9529       | 0.9283       | 0.9529          | 0.9590         | 0.9607  | 0.7554   |
+| 0.1341        | 3.0   | 6633 | 0.2667          | 0.9155   | 0.8749   | 0.9164      | 0.9170     | 0.8823          | 0.8328          | 0.8851             | 0.9059            | 0.9513       | 0.9251       | 0.9513          | 0.9569         | 0.9606  | 0.7642   |
+| 0.1161        | 4.0   | 8844 | 0.2864          | 0.9188   | 0.8783   | 0.9195      | 0.9202     | 0.8938          | 0.8451          | 0.8958             | 0.9150            | 0.9452       | 0.9173       | 0.9452          | 0.9525         | 0.9593  | 0.7758   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3