---
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