---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner
  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: 0.5527638190954773
    - name: Recall
      type: recall
      value: 0.4077849860982391
    - name: F1
      type: f1
      value: 0.46933333333333327
    - name: Accuracy
      type: accuracy
      value: 0.9475439271514685
---

<!-- 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. -->

# ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3030
- Precision: 0.5528
- Recall: 0.4078
- F1: 0.4693
- Accuracy: 0.9475

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.2577          | 0.5099    | 0.3568 | 0.4198 | 0.9437   |
| No log        | 2.0   | 426  | 0.2675          | 0.5406    | 0.3948 | 0.4563 | 0.9463   |
| 0.0737        | 3.0   | 639  | 0.3040          | 0.5737    | 0.3716 | 0.4511 | 0.9465   |
| 0.0737        | 4.0   | 852  | 0.3042          | 0.5514    | 0.3976 | 0.4620 | 0.9474   |
| 0.0307        | 5.0   | 1065 | 0.3030          | 0.5528    | 0.4078 | 0.4693 | 0.9475   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3