---
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: 0.6274509803921569
    - name: Recall
      type: recall
      value: 0.49760765550239233
    - name: F1
      type: f1
      value: 0.5550366911274184
    - name: Accuracy
      type: accuracy
      value: 0.9333784769246797
---

<!-- 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 was trained from scratch on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4590
- Precision: 0.6275
- Recall: 0.4976
- F1: 0.5550
- Accuracy: 0.9334

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 425  | 0.4576          | 0.6556    | 0.4713 | 0.5484 | 0.9321   |
| 0.0403        | 2.0   | 850  | 0.4647          | 0.6293    | 0.4629 | 0.5334 | 0.9311   |
| 0.0227        | 3.0   | 1275 | 0.4590          | 0.6275    | 0.4976 | 0.5550 | 0.9334   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2