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

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1310
- Precision: 0.8522
- Recall: 0.8868
- Accuracy: 0.9732
- F1: 0.8692
- Ent/precision: 0.8874
- Ent/accuracy: 0.9246
- Ent/f1: 0.9056
- Con/precision: 0.8011
- Con/accuracy: 0.8320
- Con/f1: 0.8163

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.1744        | 1.0   | 1875  | 0.1261          | 0.7176    | 0.7710 | 0.9494   | 0.7433 | 0.7586        | 0.8503       | 0.8018 | 0.6514        | 0.6561       | 0.6537 |
| 0.1261        | 2.0   | 3750  | 0.1041          | 0.7742    | 0.8057 | 0.9600   | 0.7896 | 0.8083        | 0.8816       | 0.8433 | 0.7185        | 0.6957       | 0.7070 |
| 0.0878        | 3.0   | 5625  | 0.0979          | 0.8176    | 0.8140 | 0.9655   | 0.8158 | 0.8518        | 0.8789       | 0.8651 | 0.7634        | 0.7199       | 0.7410 |
| 0.0625        | 4.0   | 7500  | 0.0976          | 0.8228    | 0.8643 | 0.9696   | 0.8430 | 0.8515        | 0.9182       | 0.8836 | 0.7784        | 0.7862       | 0.7823 |
| 0.0456        | 5.0   | 9375  | 0.1047          | 0.8304    | 0.8758 | 0.9704   | 0.8525 | 0.8758        | 0.9189       | 0.8968 | 0.7655        | 0.8133       | 0.7887 |
| 0.0342        | 6.0   | 11250 | 0.1207          | 0.8363    | 0.8887 | 0.9719   | 0.8617 | 0.8719        | 0.9274       | 0.8988 | 0.7846        | 0.8327       | 0.8080 |
| 0.0256        | 7.0   | 13125 | 0.1241          | 0.848     | 0.8892 | 0.9731   | 0.8681 | 0.8791        | 0.9299       | 0.9038 | 0.8019        | 0.8302       | 0.8158 |
| 0.0205        | 8.0   | 15000 | 0.1310          | 0.8522    | 0.8868 | 0.9732   | 0.8692 | 0.8874        | 0.9246       | 0.9056 | 0.8011        | 0.8320       | 0.8163 |


### Framework versions

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