---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-small-finetuned-xglue-ner-longer6
  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: 0.5620094191522763
    - name: Recall
      type: recall
      value: 0.42822966507177035
    - name: F1
      type: f1
      value: 0.48608282416836385
    - name: Accuracy
      type: accuracy
      value: 0.9260809725885996
---

<!-- 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-small-finetuned-xglue-ner-longer6

This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner](https://huggingface.co/muhtasham/bert-small-finetuned-xglue-ner) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4087
- Precision: 0.5620
- Recall: 0.4282
- F1: 0.4861
- Accuracy: 0.9261

## 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  | 0.3753          | 0.5646    | 0.4127 | 0.4768 | 0.9250   |
| 0.0824        | 2.0   | 850  | 0.3942          | 0.5484    | 0.4270 | 0.4802 | 0.9259   |
| 0.0641        | 3.0   | 1275 | 0.4087          | 0.5620    | 0.4282 | 0.4861 | 0.9261   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1