---
base_model: google-bert/bert-base-uncased
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-intent-booking
  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-base-uncased-intent-booking

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1797
- Accuracy: 0.1937
- F1: 0.1715
- Precision: 0.3099
- Recall: 0.1937

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 64
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.3533        | 1.0   | 65   | 2.3007          | 0.0946   | 0.0338 | 0.0281    | 0.0946 |
| 2.2868        | 2.0   | 130  | 2.1968          | 0.1351   | 0.0632 | 0.0472    | 0.1351 |
| 2.2299        | 3.0   | 195  | 2.1730          | 0.1847   | 0.1151 | 0.1020    | 0.1847 |
| 2.1909        | 4.0   | 260  | 2.1719          | 0.1937   | 0.1687 | 0.3112    | 0.1937 |
| 2.1657        | 5.0   | 325  | 2.1376          | 0.2027   | 0.1567 | 0.2069    | 0.2027 |
| 2.1437        | 6.0   | 390  | 2.1459          | 0.1757   | 0.1461 | 0.1909    | 0.1757 |
| 2.1342        | 7.0   | 455  | 2.1581          | 0.1667   | 0.1418 | 0.2867    | 0.1667 |
| 2.1025        | 8.0   | 520  | 2.1425          | 0.1892   | 0.1504 | 0.2449    | 0.1892 |
| 2.0749        | 9.0   | 585  | 2.1277          | 0.1847   | 0.1641 | 0.3096    | 0.1847 |
| 2.0482        | 10.0  | 650  | 2.1283          | 0.2117   | 0.1895 | 0.3519    | 0.2117 |
| 2.0654        | 11.0  | 715  | 2.1253          | 0.2117   | 0.1886 | 0.3004    | 0.2117 |
| 2.0443        | 12.0  | 780  | 2.1200          | 0.1937   | 0.1770 | 0.2982    | 0.1937 |
| 2.0345        | 13.0  | 845  | 2.1252          | 0.1937   | 0.1791 | 0.3098    | 0.1937 |
| 2.0148        | 14.0  | 910  | 2.1113          | 0.1982   | 0.1783 | 0.2804    | 0.1982 |
| 2.0112        | 15.0  | 975  | 2.1372          | 0.1892   | 0.1702 | 0.2746    | 0.1892 |
| 2.0022        | 16.0  | 1040 | 2.1254          | 0.1892   | 0.1696 | 0.2710    | 0.1892 |
| 1.9913        | 17.0  | 1105 | 2.1221          | 0.1892   | 0.1696 | 0.2710    | 0.1892 |
| 1.9827        | 18.0  | 1170 | 2.1090          | 0.1982   | 0.1758 | 0.2910    | 0.1982 |
| 1.9871        | 19.0  | 1235 | 2.1111          | 0.1982   | 0.1789 | 0.2756    | 0.1982 |
| 1.9824        | 20.0  | 1300 | 2.1132          | 0.1892   | 0.1705 | 0.2665    | 0.1892 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1