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
+  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
+
+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: 0.0950
+- F1 micro: 0.9201
+- F1 macro: 0.8831
+- F1 weighted: 0.9202
+- F1 samples: 0.9200
+- Precision micro: 0.9141
+- Precision macro: 0.8790
+- Precision weighted: 0.9144
+- Precision samples: 0.9283
+- Recall micro: 0.9263
+- Recall macro: 0.8877
+- Recall weighted: 0.9263
+- Recall samples: 0.9372
+- Roc Auc: 0.9529
+- Accuracy: 0.7848
+
+## 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: 5
+
+### 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.1013        | 1.0   | 2211  | 0.0899          | 0.9159   | 0.8789   | 0.9164      | 0.9149     | 0.9074          | 0.8824          | 0.9092             | 0.9213            | 0.9245       | 0.8808       | 0.9245          | 0.9355         | 0.9511  | 0.7729   |
+| 0.0749        | 2.0   | 4422  | 0.0847          | 0.9205   | 0.8854   | 0.9205      | 0.9203     | 0.9138          | 0.8843          | 0.9144             | 0.9264            | 0.9274       | 0.8882       | 0.9274          | 0.9390         | 0.9534  | 0.7857   |
+| 0.0583        | 3.0   | 6633  | 0.0871          | 0.9212   | 0.8851   | 0.9212      | 0.9206     | 0.9145          | 0.8913          | 0.9151             | 0.9269            | 0.9280       | 0.8808       | 0.9280          | 0.9390         | 0.9537  | 0.7883   |
+| 0.0433        | 4.0   | 8844  | 0.0924          | 0.9201   | 0.8849   | 0.9203      | 0.9202     | 0.9094          | 0.8766          | 0.9099             | 0.9246            | 0.9312       | 0.8947       | 0.9312          | 0.9416         | 0.9546  | 0.7834   |
+| 0.0315        | 5.0   | 11055 | 0.0950          | 0.9201   | 0.8831   | 0.9202      | 0.9200     | 0.9141          | 0.8790          | 0.9144             | 0.9283            | 0.9263       | 0.8877       | 0.9263          | 0.9372         | 0.9529  | 0.7848   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3