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bert-small-finetuned-xglue-ner

Token Classification
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bert-small-finetuned-xglue-ner
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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
  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: 0.5931899641577061
    - name: Recall
      type: recall
      value: 0.39593301435406697
    - name: F1
      type: f1
      value: 0.4748923959827833
    - name: Accuracy
      type: accuracy
      value: 0.9251634361738732
---

<!-- 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

This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3663
- Precision: 0.5932
- Recall: 0.3959
- F1: 0.4749
- Accuracy: 0.9252

## 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  | 0.3590          | 0.6185    | 0.3433 | 0.4415 | 0.9220   |
| 0.2242        | 2.0   | 850  | 0.3638          | 0.6226    | 0.3947 | 0.4832 | 0.9245   |
| 0.1219        | 3.0   | 1275 | 0.3663          | 0.5932    | 0.3959 | 0.4749 | 0.9252   |


### Framework versions

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