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bert-small-finetuned-wnut17-ner-longer10

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bert-small-finetuned-wnut17-ner-longer10
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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-wnut17-ner-longer10
  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: 0.5546995377503852
    - name: Recall
      type: recall
      value: 0.430622009569378
    - name: F1
      type: f1
      value: 0.48484848484848486
    - name: Accuracy
      type: accuracy
      value: 0.9250487441220323
---

<!-- 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-wnut17-ner-longer10

This model is a fine-tuned version of [muhtasham/bert-small-finetuned-wnut17-ner-longer6](https://huggingface.co/muhtasham/bert-small-finetuned-wnut17-ner-longer6) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4693
- Precision: 0.5547
- Recall: 0.4306
- F1: 0.4848
- Accuracy: 0.9250

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 425  | 0.4815          | 0.5759    | 0.3947 | 0.4684 | 0.9255   |
| 0.0402        | 2.0   | 850  | 0.4467          | 0.5397    | 0.4390 | 0.4842 | 0.9247   |
| 0.0324        | 3.0   | 1275 | 0.4646          | 0.5332    | 0.4318 | 0.4772 | 0.9244   |
| 0.0315        | 4.0   | 1700 | 0.4693          | 0.5547    | 0.4306 | 0.4848 | 0.9250   |


### Framework versions

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