---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- trl
- sft
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-wnut_17-full
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: test
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.6373056994818653
    - name: Recall
      type: recall
      value: 0.34198331788693237
    - name: F1
      type: f1
      value: 0.44511459589867314
    - name: Accuracy
      type: accuracy
      value: 0.9476294301226967
---

<!-- 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-full

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: 0.4356
- Precision: 0.6373
- Recall: 0.3420
- F1: 0.4451
- Accuracy: 0.9476

## 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  | 0.2799          | 0.6045    | 0.3216 | 0.4198 | 0.9457   |
| No log        | 2.0   | 426  | 0.3236          | 0.5728    | 0.3392 | 0.4261 | 0.9463   |
| 0.0468        | 3.0   | 639  | 0.3751          | 0.5924    | 0.3448 | 0.4359 | 0.9472   |
| 0.0468        | 4.0   | 852  | 0.3713          | 0.5733    | 0.3661 | 0.4468 | 0.9470   |
| 0.0105        | 5.0   | 1065 | 0.3827          | 0.5741    | 0.3735 | 0.4526 | 0.9479   |
| 0.0105        | 6.0   | 1278 | 0.4356          | 0.6373    | 0.3420 | 0.4451 | 0.9476   |


### Framework versions

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