---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: bert-uncased-keyword-extractor
  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-uncased-keyword-extractor

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1247
- Precision: 0.8547
- Recall: 0.8825
- Accuracy: 0.9741
- F1: 0.8684

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.165         | 1.0   | 1875  | 0.1202          | 0.7109    | 0.7766 | 0.9505   | 0.7423 |
| 0.1211        | 2.0   | 3750  | 0.1011          | 0.7801    | 0.8186 | 0.9621   | 0.7989 |
| 0.0847        | 3.0   | 5625  | 0.0945          | 0.8292    | 0.8044 | 0.9667   | 0.8166 |
| 0.0614        | 4.0   | 7500  | 0.0927          | 0.8409    | 0.8524 | 0.9711   | 0.8466 |
| 0.0442        | 5.0   | 9375  | 0.1057          | 0.8330    | 0.8738 | 0.9712   | 0.8529 |
| 0.0325        | 6.0   | 11250 | 0.1103          | 0.8585    | 0.8743 | 0.9738   | 0.8663 |
| 0.0253        | 7.0   | 13125 | 0.1204          | 0.8453    | 0.8825 | 0.9735   | 0.8635 |
| 0.0203        | 8.0   | 15000 | 0.1247          | 0.8547    | 0.8825 | 0.9741   | 0.8684 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1