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

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: 0.1296
- Precision: 0.8439
- Recall: 0.8722
- Accuracy: 0.9727
- F1: 0.8578
- Ent/precision: 0.8723
- Ent/accuracy: 0.9077
- Ent/f1: 0.8896
- Con/precision: 0.8010
- Con/accuracy: 0.8196
- Con/f1: 0.8102

## 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     | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.1849        | 1.0   | 1875  | 0.1323          | 0.7039    | 0.7428 | 0.9488   | 0.7228 | 0.7522        | 0.8166       | 0.7831 | 0.6268        | 0.6332       | 0.6300 |
| 0.1357        | 2.0   | 3750  | 0.1132          | 0.7581    | 0.8024 | 0.9592   | 0.7796 | 0.7948        | 0.8785       | 0.8346 | 0.6971        | 0.6895       | 0.6933 |
| 0.0965        | 3.0   | 5625  | 0.1033          | 0.8086    | 0.7980 | 0.9646   | 0.8032 | 0.8410        | 0.8592       | 0.8500 | 0.7560        | 0.7071       | 0.7307 |
| 0.0713        | 4.0   | 7500  | 0.1040          | 0.8181    | 0.8435 | 0.9683   | 0.8306 | 0.8526        | 0.8906       | 0.8712 | 0.7652        | 0.7736       | 0.7694 |
| 0.0525        | 5.0   | 9375  | 0.1126          | 0.8150    | 0.8633 | 0.9689   | 0.8385 | 0.8495        | 0.9064       | 0.8770 | 0.7629        | 0.7993       | 0.7807 |
| 0.0386        | 6.0   | 11250 | 0.1183          | 0.8374    | 0.8678 | 0.9719   | 0.8523 | 0.8709        | 0.9020       | 0.8862 | 0.7877        | 0.8170       | 0.8021 |
| 0.03          | 7.0   | 13125 | 0.1237          | 0.8369    | 0.8707 | 0.9723   | 0.8535 | 0.8657        | 0.9079       | 0.8863 | 0.7934        | 0.8155       | 0.8043 |
| 0.0244        | 8.0   | 15000 | 0.1296          | 0.8439    | 0.8722 | 0.9727   | 0.8578 | 0.8723        | 0.9077       | 0.8896 | 0.8010        | 0.8196       | 0.8102 |


### Framework versions

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