update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: PubMedELECTRA-LitCovid-1.4
+  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. -->
+
+# PubMedELECTRA-LitCovid-1.4
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedELECTRA-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedELECTRA-base-uncased-abstract) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5898
+- Hamming loss: 0.0967
+- F1 micro: 0.5691
+- F1 macro: 0.4329
+- F1 weighted: 0.6693
+- F1 samples: 0.5791
+- Precision micro: 0.4198
+- Precision macro: 0.3211
+- Precision weighted: 0.5820
+- Precision samples: 0.4666
+- Recall micro: 0.8834
+- Recall macro: 0.8456
+- Recall weighted: 0.8834
+- Recall samples: 0.8983
+- Roc Auc: 0.8941
+- Accuracy: 0.0504
+
+## 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
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.7482        | 1.0   | 1151 | 0.7061          | 0.1518       | 0.4528   | 0.3484   | 0.6073      | 0.4584     | 0.3063          | 0.2528          | 0.5187             | 0.3313            | 0.8684       | 0.8422       | 0.8684          | 0.8869         | 0.8575  | 0.0023   |
+| 0.5987        | 2.0   | 2303 | 0.6241          | 0.1287       | 0.4983   | 0.3783   | 0.6327      | 0.5120     | 0.3469          | 0.2766          | 0.5412             | 0.3888            | 0.8840       | 0.8571       | 0.8840          | 0.8996         | 0.8771  | 0.0193   |
+| 0.5194        | 3.0   | 3454 | 0.5960          | 0.1079       | 0.5399   | 0.4108   | 0.6584      | 0.5500     | 0.3903          | 0.3056          | 0.5764             | 0.4339            | 0.8752       | 0.8513       | 0.8752          | 0.8921         | 0.8843  | 0.0351   |
+| 0.4471        | 4.0   | 4606 | 0.5900          | 0.0982       | 0.5653   | 0.4286   | 0.6681      | 0.5747     | 0.4157          | 0.3179          | 0.5810             | 0.4609            | 0.8830       | 0.8468       | 0.8830          | 0.8983         | 0.8931  | 0.0460   |
+| 0.422         | 5.0   | 5755 | 0.5898          | 0.0967       | 0.5691   | 0.4329   | 0.6693      | 0.5791     | 0.4198          | 0.3211          | 0.5820             | 0.4666            | 0.8834       | 0.8456       | 0.8834          | 0.8983         | 0.8941  | 0.0504   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.13.3