update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: Bioformer-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. -->
+
+# Bioformer-LitCovid-v1.2.3
+
+This model is a fine-tuned version of [bioformers/bioformer-litcovid](https://huggingface.co/bioformers/bioformer-litcovid) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9317
+- F1 micro: 0.7843
+- F1 macro: 0.2816
+- F1 weighted: 0.8523
+- F1 samples: 0.8647
+- Precision micro: 0.6737
+- Precision macro: 0.2255
+- Precision weighted: 0.7914
+- Precision samples: 0.8417
+- Recall micro: 0.9384
+- Recall macro: 0.7715
+- Recall weighted: 0.9384
+- Recall samples: 0.9468
+- Roc Auc: 0.9568
+- Accuracy: 0.6515
+
+## 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: 32
+- eval_batch_size: 32
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.8024        | 1.0   | 1136 | 0.6487          | 0.6577   | 0.2288   | 0.7528      | 0.7600     | 0.5118          | 0.1747          | 0.6503             | 0.7007            | 0.9198       | 0.8185       | 0.9198          | 0.9253         | 0.9361  | 0.3919   |
+| 0.639         | 2.0   | 2272 | 0.8280          | 0.7187   | 0.2482   | 0.8039      | 0.8088     | 0.5833          | 0.1935          | 0.7200             | 0.7602            | 0.9361       | 0.7535       | 0.9361          | 0.9441         | 0.9499  | 0.4986   |
+| 0.5167        | 3.0   | 3408 | 0.8318          | 0.7589   | 0.2686   | 0.8342      | 0.8469     | 0.6372          | 0.2127          | 0.7628             | 0.8153            | 0.9382       | 0.7903       | 0.9382          | 0.9462         | 0.9546  | 0.6008   |
+| 0.3641        | 4.0   | 4544 | 0.9231          | 0.7793   | 0.2788   | 0.8472      | 0.8578     | 0.6644          | 0.2220          | 0.7815             | 0.8290            | 0.9422       | 0.7678       | 0.9422          | 0.9497         | 0.9582  | 0.6311   |
+| 0.3754        | 5.0   | 5680 | 0.9317          | 0.7843   | 0.2816   | 0.8523      | 0.8647     | 0.6737          | 0.2255          | 0.7914             | 0.8417            | 0.9384       | 0.7715       | 0.9384          | 0.9468         | 0.9568  | 0.6515   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3