---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: im-bin-tf-abstr
  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. -->

# im-bin-tf-abstr

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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2750
- Accuracy: 0.9261
- F1: 0.9259
- Precision: 0.9311
- Recall: 0.9207

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2484        | 1.0   | 30000  | 0.2765          | 0.9192   | 0.9209 | 0.9039    | 0.9386 |
| 0.2141        | 2.0   | 60000  | 0.2750          | 0.9261   | 0.9259 | 0.9311    | 0.9207 |
| 0.1991        | 3.0   | 90000  | 0.2952          | 0.9271   | 0.9275 | 0.9248    | 0.9303 |
| 0.1661        | 4.0   | 120000 | 0.3409          | 0.9274   | 0.9275 | 0.9284    | 0.9266 |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3