---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nlpcw_bert-base-uncased-abbr
  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. -->

# nlpcw_bert-base-uncased-abbr

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: 0.2675
- Precision: 0.9390
- Recall: 0.9349
- F1: 0.9369
- Accuracy: 0.9317

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6325        | 1.0   | 67   | 0.2629          | 0.9036    | 0.9090 | 0.9063 | 0.9043   |
| 0.3169        | 2.0   | 134  | 0.2297          | 0.9309    | 0.9137 | 0.9223 | 0.9182   |
| 0.1994        | 3.0   | 201  | 0.2282          | 0.9310    | 0.9193 | 0.9251 | 0.9223   |
| 0.17          | 4.0   | 268  | 0.2193          | 0.9366    | 0.9286 | 0.9326 | 0.9278   |
| 0.1457        | 5.0   | 335  | 0.2350          | 0.9395    | 0.9373 | 0.9384 | 0.9331   |
| 0.1086        | 6.0   | 402  | 0.2435          | 0.9418    | 0.9340 | 0.9379 | 0.9331   |
| 0.0908        | 7.0   | 469  | 0.2537          | 0.9357    | 0.9283 | 0.9319 | 0.9270   |
| 0.0791        | 8.0   | 536  | 0.2675          | 0.9390    | 0.9349 | 0.9369 | 0.9317   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1