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bert-tiny-finetuned-xglue-ner

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bert-tiny-finetuned-xglue-ner
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xglue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-tiny-finetuned-xglue-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xglue
      type: xglue
      config: ner
      split: train
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.630759453447728
    - name: Recall
      type: recall
      value: 0.6681252103668799
    - name: F1
      type: f1
      value: 0.6489048708728343
    - name: Accuracy
      type: accuracy
      value: 0.9274310133922189
---

<!-- 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-tiny-finetuned-xglue-ner

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2489
- Precision: 0.6308
- Recall: 0.6681
- F1: 0.6489
- Accuracy: 0.9274

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4082        | 1.0   | 1756 | 0.3326          | 0.5600    | 0.5798 | 0.5697 | 0.9118   |
| 0.2974        | 2.0   | 3512 | 0.2635          | 0.6143    | 0.6562 | 0.6346 | 0.9248   |
| 0.2741        | 3.0   | 5268 | 0.2489          | 0.6308    | 0.6681 | 0.6489 | 0.9274   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1