---
library_name: transformers
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-wnut17-final
  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: 0.5603799185888738
    - name: Recall
      type: recall
      value: 0.3827618164967563
    - name: F1
      type: f1
      value: 0.45484581497797355
    - name: Accuracy
      type: accuracy
      value: 0.9482345900658289
---

<!-- 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-wnut17-final

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3245
- Precision: 0.5604
- Recall: 0.3828
- F1: 0.4548
- Accuracy: 0.9482

## 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: 3.4590617775212224e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.2392          | 0.5203    | 0.4041 | 0.4549 | 0.9462   |
| No log        | 2.0   | 426  | 0.2932          | 0.5818    | 0.3494 | 0.4366 | 0.9459   |
| 0.1758        | 3.0   | 639  | 0.3100          | 0.5768    | 0.3828 | 0.4602 | 0.9478   |
| 0.1758        | 4.0   | 852  | 0.3245          | 0.5604    | 0.3828 | 0.4548 | 0.9482   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0