---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: test
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.55
    - name: Recall
      type: recall
      value: 0.37720111214087115
    - name: F1
      type: f1
      value: 0.44749862561847165
    - name: Accuracy
      type: accuracy
      value: 0.9481063520560827
---

<!-- 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. -->

# my_awesome_wnut_model

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3958
- Precision: 0.55
- Recall: 0.3772
- F1: 0.4475
- Accuracy: 0.9481

## 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: 16
- eval_batch_size: 16
- 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   | 213  | 0.2562          | 0.5704    | 0.2929 | 0.3870 | 0.9417   |
| No log        | 2.0   | 426  | 0.2776          | 0.5462    | 0.3179 | 0.4019 | 0.9436   |
| 0.1469        | 3.0   | 639  | 0.2834          | 0.5453    | 0.3624 | 0.4354 | 0.9475   |
| 0.1469        | 4.0   | 852  | 0.3004          | 0.5669    | 0.3652 | 0.4442 | 0.9480   |
| 0.0325        | 5.0   | 1065 | 0.3360          | 0.5858    | 0.3735 | 0.4561 | 0.9482   |
| 0.0325        | 6.0   | 1278 | 0.3471          | 0.5149    | 0.3855 | 0.4409 | 0.9474   |
| 0.0325        | 7.0   | 1491 | 0.3883          | 0.5552    | 0.3633 | 0.4392 | 0.9474   |
| 0.0117        | 8.0   | 1704 | 0.3881          | 0.5602    | 0.3707 | 0.4462 | 0.9477   |
| 0.0117        | 9.0   | 1917 | 0.4008          | 0.5582    | 0.3689 | 0.4442 | 0.9478   |
| 0.0051        | 10.0  | 2130 | 0.3958          | 0.55      | 0.3772 | 0.4475 | 0.9481   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0