---
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: 0.5552523874488404
    - name: Recall
      type: recall
      value: 0.37720111214087115
    - name: F1
      type: f1
      value: 0.44922737306843263
    - name: Accuracy
      type: accuracy
      value: 0.9469454063528707
---

<!-- 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: 0.2942
- Precision: 0.5553
- Recall: 0.3772
- F1: 0.4492
- Accuracy: 0.9469

## 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  | 0.2666          | 0.6024    | 0.2808 | 0.3831 | 0.9405   |
| No log        | 2.0   | 426  | 0.2605          | 0.5708    | 0.3364 | 0.4233 | 0.9456   |
| 0.1299        | 3.0   | 639  | 0.2827          | 0.5658    | 0.3346 | 0.4205 | 0.9452   |
| 0.1299        | 4.0   | 852  | 0.2836          | 0.5503    | 0.3753 | 0.4463 | 0.9469   |
| 0.051         | 5.0   | 1065 | 0.2942          | 0.5553    | 0.3772 | 0.4492 | 0.9469   |


### Framework versions

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