---
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-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: validation
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.5509868421052632
    - name: Recall
      type: recall
      value: 0.4007177033492823
    - name: F1
      type: f1
      value: 0.46398891966759004
    - name: Accuracy
      type: accuracy
      value: 0.9174203906877174
---

<!-- 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-finetuned-ner

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: 0.3691
- Precision: 0.5510
- Recall: 0.4007
- F1: 0.4640
- Accuracy: 0.9174

## 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: 32
- eval_batch_size: 32
- 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 107  | 0.4278          | 0.5169    | 0.2380 | 0.3260 | 0.9021   |
| No log        | 2.0   | 214  | 0.3786          | 0.6056    | 0.3600 | 0.4516 | 0.9135   |
| No log        | 3.0   | 321  | 0.3691          | 0.5510    | 0.4007 | 0.4640 | 0.9174   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0