---
base_model: google-bert/bert-base-uncased
datasets:
- wnut_17
library_name: peft
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: bert-base-uncased-wnut_17
  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-wnut_17

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2870
- Precision: 0.4802
- Recall: 0.2132
- F1: 0.2953
- Accuracy: 0.9366

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.4305          | 1.0       | 0.0    | 0.0    | 0.9256   |
| No log        | 2.0   | 426  | 0.3568          | 0.0       | 0.0    | 0.0    | 0.9256   |
| 0.496         | 3.0   | 639  | 0.3379          | 0.3495    | 0.0334 | 0.0609 | 0.9277   |
| 0.496         | 4.0   | 852  | 0.3166          | 0.3824    | 0.1205 | 0.1832 | 0.9321   |
| 0.1935        | 5.0   | 1065 | 0.3034          | 0.3907    | 0.1705 | 0.2374 | 0.9343   |
| 0.1935        | 6.0   | 1278 | 0.2956          | 0.4313    | 0.1863 | 0.2602 | 0.9353   |
| 0.1935        | 7.0   | 1491 | 0.2941          | 0.4700    | 0.1891 | 0.2697 | 0.9357   |
| 0.1717        | 8.0   | 1704 | 0.2960          | 0.4874    | 0.1965 | 0.2801 | 0.9363   |
| 0.1717        | 9.0   | 1917 | 0.2882          | 0.4797    | 0.2076 | 0.2898 | 0.9364   |
| 0.1594        | 10.0  | 2130 | 0.2870          | 0.4802    | 0.2132 | 0.2953 | 0.9366   |


### Framework versions

- PEFT 0.12.1.dev0
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1