update model card README.md

README.md

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+---
+license: mit
+base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: mult_tf
+ 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. -->
+
+# mult_tf
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5180
+- Accuracy: 0.8364
+- F1: 0.8358
+- Precision: 0.8355
+- Recall: 0.8364
+- Roc Auc: 0.9896
+
+## 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: 1e-05
+- train_batch_size: 640
+- eval_batch_size: 1280
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
+| No log | 1.0 | 357 | 0.5694 | 0.8249 | 0.8243 | 0.8245 | 0.8249 | 0.9875 |
+| 0.5397 | 2.0 | 714 | 0.5324 | 0.8324 | 0.8312 | 0.8313 | 0.8324 | 0.9890 |
+| 0.523 | 3.0 | 1071 | 0.5193 | 0.8354 | 0.8348 | 0.8346 | 0.8354 | 0.9895 |
+| 0.523 | 4.0 | 1428 | 0.5180 | 0.8364 | 0.8358 | 0.8355 | 0.8364 | 0.9896 |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3