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-v1.2.4
+  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-v1.2.4
+
+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: 0.2160
+- F1 micro: 0.8926
+- F1 macro: 0.3237
+- F1 weighted: 0.9016
+- F1 samples: 0.9024
+- Precision micro: 0.8426
+- Precision macro: 0.2736
+- Precision weighted: 0.8627
+- Precision samples: 0.8871
+- Recall micro: 0.9490
+- Recall macro: 0.4834
+- Recall weighted: 0.9490
+- Recall samples: 0.9544
+- Roc Auc: 0.9697
+- Accuracy: 0.7353
+
+## 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 | 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.4454        | 1.0   | 2248 | 0.3019          | 0.8637   | 0.2988   | 0.8757      | 0.8789     | 0.7937          | 0.2500          | 0.8205             | 0.8518            | 0.9471       | 0.4390       | 0.9471          | 0.9528         | 0.9669  | 0.6618   |
+| 0.2453        | 2.0   | 4496 | 0.2696          | 0.8852   | 0.3387   | 0.8917      | 0.8947     | 0.8231          | 0.2862          | 0.8377             | 0.8701            | 0.9574       | 0.4723       | 0.9574          | 0.9602         | 0.9731  | 0.7056   |
+| 0.1271        | 3.0   | 6744 | 0.2160          | 0.8926   | 0.3237   | 0.9016      | 0.9024     | 0.8426          | 0.2736          | 0.8627             | 0.8871            | 0.9490       | 0.4834       | 0.9490          | 0.9544         | 0.9697  | 0.7353   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3