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-v1.2.3
+  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-v1.2.3
+
+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: 0.9691
+- F1 micro: 0.8266
+- F1 macro: 0.3107
+- F1 weighted: 0.8821
+- F1 samples: 0.8868
+- Precision micro: 0.7335
+- Precision macro: 0.2518
+- Precision weighted: 0.8347
+- Precision samples: 0.8699
+- Recall micro: 0.9468
+- Recall macro: 0.7764
+- Recall weighted: 0.9468
+- Recall samples: 0.9538
+- Roc Auc: 0.9640
+- Accuracy: 0.7104
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 1.1059        | 1.0   | 2272 | 0.7606          | 0.7517   | 0.2617   | 0.8428      | 0.8566     | 0.6257          | 0.2096          | 0.7778             | 0.8302            | 0.9412       | 0.7947       | 0.9412          | 0.9501         | 0.9553  | 0.6325   |
+| 0.6408        | 2.0   | 4544 | 0.8639          | 0.8057   | 0.2965   | 0.8751      | 0.8786     | 0.7057          | 0.2399          | 0.8315             | 0.8626            | 0.9389       | 0.8070       | 0.9389          | 0.9484         | 0.9588  | 0.6961   |
+| 0.6275        | 3.0   | 6816 | 0.9691          | 0.8266   | 0.3107   | 0.8821      | 0.8868     | 0.7335          | 0.2518          | 0.8347             | 0.8699            | 0.9468       | 0.7764       | 0.9468          | 0.9538         | 0.9640  | 0.7104   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3