update model card README.md

README.md

@@ -0,0 +1,80 @@
+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: Bioformer-LitCovid-v1.4h
+  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-v1.4h
+
+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: 0.5733
+- Hamming loss: 0.0842
+- F1 micro: 0.6047
+- F1 macro: 0.4622
+- F1 weighted: 0.6887
+- F1 samples: 0.6127
+- Precision micro: 0.4576
+- Precision macro: 0.3466
+- Precision weighted: 0.5990
+- Precision samples: 0.5038
+- Recall micro: 0.8912
+- Recall macro: 0.8446
+- Recall weighted: 0.8912
+- Recall samples: 0.9055
+- Roc Auc: 0.9044
+- Accuracy: 0.0708
+
+## 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: 5.451682398151845e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.08129918921555689
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Hamming 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.9164        | 1.0   | 576  | 0.6810          | 0.1510       | 0.4505   | 0.3468   | 0.6199      | 0.4653     | 0.3057          | 0.2568          | 0.5483             | 0.3450            | 0.8564       | 0.8656       | 0.8564          | 0.8750         | 0.8524  | 0.0078   |
+| 0.6032        | 2.0   | 1152 | 0.5983          | 0.1154       | 0.5273   | 0.4002   | 0.6493      | 0.5373     | 0.3746          | 0.2939          | 0.5587             | 0.4139            | 0.8902       | 0.8651       | 0.8902          | 0.9050         | 0.8872  | 0.0263   |
+| 0.4965        | 3.0   | 1728 | 0.5752          | 0.0975       | 0.5704   | 0.4372   | 0.6709      | 0.5795     | 0.4185          | 0.3237          | 0.5797             | 0.4617            | 0.8952       | 0.8536       | 0.8952          | 0.9089         | 0.8991  | 0.0479   |
+| 0.4354        | 4.0   | 2304 | 0.5655          | 0.0863       | 0.5978   | 0.4554   | 0.6872      | 0.6050     | 0.4508          | 0.3406          | 0.6021             | 0.4948            | 0.8870       | 0.8503       | 0.8870          | 0.9024         | 0.9014  | 0.0636   |
+| 0.3874        | 5.0   | 2880 | 0.5733          | 0.0842       | 0.6047   | 0.4622   | 0.6887      | 0.6127     | 0.4576          | 0.3466          | 0.5990             | 0.5038            | 0.8912       | 0.8446       | 0.8912          | 0.9055         | 0.9044  | 0.0708   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3