---
license: apache-2.0
base_model: Twitter/twhin-bert-base
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fine-tuned-bert-extractive-summarization
  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. -->

# fine-tuned-bert-extractive-summarization

This model is a fine-tuned version of [Twitter/twhin-bert-base](https://huggingface.co/Twitter/twhin-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5566
- Accuracy: 0.6995
- Precision: 0.6947
- Recall: 0.6995
- F1: 0.6961

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5748        | 1.0   | 7107  | 0.5609          | 0.6916   | 0.6858    | 0.6916 | 0.6873 |
| 0.5552        | 2.0   | 14215 | 0.5659          | 0.6839   | 0.6931    | 0.6839 | 0.6870 |
| 0.5364        | 3.0   | 21321 | 0.5566          | 0.6995   | 0.6947    | 0.6995 | 0.6961 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2