transformers pytorch bert token-classification generated_from_trainer base_model:yanekyuk/bert-keyword-extractor license:cc-by-nc-3.0 autotrain_compatible endpoints_compatible region:us
---
license: cc-by-nc-3.0
base_model: yanekyuk/bert-keyword-extractor
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-keyword-extractor_oknashar
  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. -->

# bert-keyword-extractor_oknashar

This model is a fine-tuned version of [yanekyuk/bert-keyword-extractor](https://huggingface.co/yanekyuk/bert-keyword-extractor) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2965
- Precision: 0.5862
- Recall: 0.6937
- F1: 0.6354
- Accuracy: 0.9555

## 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: 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.204         | 1.0   | 1357  | 0.1717          | 0.3345    | 0.3419 | 0.3382 | 0.9375   |
| 0.161         | 2.0   | 2714  | 0.1547          | 0.3949    | 0.4845 | 0.4352 | 0.9426   |
| 0.1333        | 3.0   | 4071  | 0.1506          | 0.4369    | 0.5342 | 0.4807 | 0.9481   |
| 0.1147        | 4.0   | 5428  | 0.1461          | 0.5121    | 0.5424 | 0.5268 | 0.9522   |
| 0.0925        | 5.0   | 6785  | 0.1514          | 0.4864    | 0.5868 | 0.5319 | 0.9506   |
| 0.0788        | 6.0   | 8142  | 0.1477          | 0.5173    | 0.6043 | 0.5574 | 0.9529   |
| 0.0676        | 7.0   | 9499  | 0.1765          | 0.5126    | 0.6277 | 0.5644 | 0.9543   |
| 0.0542        | 8.0   | 10856 | 0.1638          | 0.5066    | 0.6704 | 0.5771 | 0.9500   |
| 0.0453        | 9.0   | 12213 | 0.1746          | 0.5158    | 0.6598 | 0.5790 | 0.9517   |
| 0.0366        | 10.0  | 13570 | 0.1905          | 0.5876    | 0.6429 | 0.6140 | 0.9569   |
| 0.0327        | 11.0  | 14927 | 0.2215          | 0.5709    | 0.6593 | 0.6119 | 0.9557   |
| 0.0296        | 12.0  | 16284 | 0.2109          | 0.5648    | 0.6598 | 0.6086 | 0.9557   |
| 0.0269        | 13.0  | 17641 | 0.2226          | 0.5632    | 0.6745 | 0.6138 | 0.9543   |
| 0.0203        | 14.0  | 18998 | 0.2464          | 0.5682    | 0.6861 | 0.6217 | 0.9543   |
| 0.0163        | 15.0  | 20355 | 0.2738          | 0.5747    | 0.6791 | 0.6226 | 0.9541   |
| 0.016         | 16.0  | 21712 | 0.2805          | 0.5868    | 0.6832 | 0.6314 | 0.9557   |
| 0.0115        | 17.0  | 23069 | 0.2923          | 0.5798    | 0.6879 | 0.6292 | 0.9551   |
| 0.0107        | 18.0  | 24426 | 0.2923          | 0.5943    | 0.6926 | 0.6397 | 0.9558   |
| 0.0105        | 19.0  | 25783 | 0.2992          | 0.5890    | 0.6902 | 0.6356 | 0.9553   |
| 0.0087        | 20.0  | 27140 | 0.2965          | 0.5862    | 0.6937 | 0.6354 | 0.9555   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
