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bert-small-finetuned-xglue-ner-longer20

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bert-small-finetuned-xglue-ner-longer20
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-small-finetuned-xglue-ner-longer20
  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: 0.5782747603833865
    - name: Recall
      type: recall
      value: 0.43301435406698563
    - name: F1
      type: f1
      value: 0.4952120383036936
    - name: Accuracy
      type: accuracy
      value: 0.92613831861452
---

<!-- 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-longer20

This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner-longer10](https://huggingface.co/muhtasham/bert-small-finetuned-xglue-ner-longer10) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5839
- Precision: 0.5783
- Recall: 0.4330
- F1: 0.4952
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 425  | 0.4882          | 0.5       | 0.4510 | 0.4742 | 0.9228   |
| 0.013         | 2.0   | 850  | 0.5039          | 0.5130    | 0.4246 | 0.4647 | 0.9242   |
| 0.0124        | 3.0   | 1275 | 0.5331          | 0.5506    | 0.4426 | 0.4907 | 0.9256   |
| 0.0159        | 4.0   | 1700 | 0.5403          | 0.5488    | 0.4234 | 0.4781 | 0.9249   |
| 0.0132        | 5.0   | 2125 | 0.5459          | 0.5714    | 0.4211 | 0.4848 | 0.9255   |
| 0.0117        | 6.0   | 2550 | 0.5522          | 0.5637    | 0.4342 | 0.4905 | 0.9253   |
| 0.0117        | 7.0   | 2975 | 0.5712          | 0.5778    | 0.4354 | 0.4966 | 0.9257   |
| 0.007         | 8.0   | 3400 | 0.5860          | 0.5828    | 0.4378 | 0.5    | 0.9259   |
| 0.0066        | 9.0   | 3825 | 0.5745          | 0.5703    | 0.4462 | 0.5007 | 0.9259   |
| 0.0049        | 10.0  | 4250 | 0.5839          | 0.5783    | 0.4330 | 0.4952 | 0.9261   |


### Framework versions

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