---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-tiny-finetuned-wnut17-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: 0.0
    - name: Recall
      type: recall
      value: 0.0
    - name: F1
      type: f1
      value: 0.0
    - name: Accuracy
      type: accuracy
      value: 0.8960890010322284
---

<!-- 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-wnut17-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 wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6054
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8961

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log        | 1.0   | 27   | 1.1060          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 2.0   | 54   | 0.9075          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 3.0   | 81   | 0.7978          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 4.0   | 108  | 0.7333          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 5.0   | 135  | 0.6929          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 6.0   | 162  | 0.6661          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 7.0   | 189  | 0.6477          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 8.0   | 216  | 0.6346          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 9.0   | 243  | 0.6251          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 10.0  | 270  | 0.6182          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 11.0  | 297  | 0.6132          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 12.0  | 324  | 0.6097          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 13.0  | 351  | 0.6073          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 14.0  | 378  | 0.6059          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 15.0  | 405  | 0.6054          | 0.0       | 0.0    | 0.0 | 0.8961   |


### Framework versions

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