---
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-optimized
  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: 0.5794655414908579
    - name: Recall
      type: recall
      value: 0.3818350324374421
    - name: F1
      type: f1
      value: 0.46033519553072627
    - name: Accuracy
      type: accuracy
      value: 0.9485338120885697
---

<!-- 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-optimized

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: 0.2901
- Precision: 0.5795
- Recall: 0.3818
- F1: 0.4603
- Accuracy: 0.9485

## 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: 2.631245451057452e-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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.2365          | 0.5265    | 0.4235 | 0.4694 | 0.9478   |
| No log        | 2.0   | 426  | 0.2692          | 0.5710    | 0.3689 | 0.4482 | 0.9480   |
| 0.2086        | 3.0   | 639  | 0.2901          | 0.5795    | 0.3818 | 0.4603 | 0.9485   |


### Framework versions

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