---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-ner-wnut-17
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: train
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.6239782016348774
    - name: Recall
      type: recall
      value: 0.42446709916589437
    - name: F1
      type: f1
      value: 0.5052399338113625
    - name: Accuracy
      type: accuracy
      value: 0.9504082766876149
---

<!-- 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-large-ner-wnut-17

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2851
- Precision: 0.6240
- Recall: 0.4245
- F1: 0.5052
- Accuracy: 0.9504

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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   | 213  | 0.2551          | 0.6727    | 0.3105 | 0.4249 | 0.9446   |
| No log        | 2.0   | 426  | 0.2521          | 0.6491    | 0.4235 | 0.5126 | 0.9510   |
| 0.1128        | 3.0   | 639  | 0.2851          | 0.6240    | 0.4245 | 0.5052 | 0.9504   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2