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-v1.2.2
+  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-v1.2.2
+
+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: 0.2409
+- F1 micro: 0.9209
+- F1 macro: 0.8813
+- F1 weighted: 0.9216
+- F1 samples: 0.9216
+- Precision micro: 0.8926
+- Precision macro: 0.8430
+- Precision weighted: 0.8949
+- Precision samples: 0.9138
+- Recall micro: 0.9510
+- Recall macro: 0.9272
+- Recall weighted: 0.9510
+- Recall samples: 0.9564
+- Roc Auc: 0.9622
+- Accuracy: 0.7805
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.2394        | 1.0   | 2183 | 0.2237          | 0.9040   | 0.8670   | 0.9056      | 0.9069     | 0.8548          | 0.8161          | 0.8601             | 0.8857            | 0.9592       | 0.9364       | 0.9592          | 0.9624         | 0.9607  | 0.7319   |
+| 0.1798        | 2.0   | 4366 | 0.2275          | 0.9171   | 0.8758   | 0.9182      | 0.9191     | 0.8855          | 0.8336          | 0.8888             | 0.9097            | 0.9510       | 0.9288       | 0.9510          | 0.9571         | 0.9612  | 0.7705   |
+| 0.1408        | 3.0   | 6549 | 0.2409          | 0.9209   | 0.8813   | 0.9216      | 0.9216     | 0.8926          | 0.8430          | 0.8949             | 0.9138            | 0.9510       | 0.9272       | 0.9510          | 0.9564         | 0.9622  | 0.7805   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3