---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert_wnut_model
  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: 0.5291073738680466
    - name: Recall
      type: recall
      value: 0.3790546802594995
    - name: F1
      type: f1
      value: 0.44168466522678185
    - name: Accuracy
      type: accuracy
      value: 0.9476788920235958
---

<!-- 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_wnut_model

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3346
- Precision: 0.5291
- Recall: 0.3791
- F1: 0.4417
- Accuracy: 0.9477

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.2607          | 0.5443    | 0.2901 | 0.3785 | 0.9411   |
| No log        | 2.0   | 426  | 0.2689          | 0.5474    | 0.3318 | 0.4132 | 0.9453   |
| 0.1554        | 3.0   | 639  | 0.2896          | 0.5253    | 0.3753 | 0.4378 | 0.9475   |
| 0.1554        | 4.0   | 852  | 0.3009          | 0.5079    | 0.3865 | 0.4389 | 0.9474   |
| 0.0349        | 5.0   | 1065 | 0.3195          | 0.5109    | 0.3920 | 0.4436 | 0.9486   |
| 0.0349        | 6.0   | 1278 | 0.3346          | 0.5291    | 0.3791 | 0.4417 | 0.9477   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1