---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_ner_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: 0.5759096612296111
    - name: Recall
      type: recall
      value: 0.42539388322520855
    - name: F1
      type: f1
      value: 0.4893390191897655
    - name: Accuracy
      type: accuracy
      value: 0.9489119746911205
---

<!-- 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_ner_model

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: 0.3321
- Precision: 0.5759
- Recall: 0.4254
- F1: 0.4893
- Accuracy: 0.9489

## 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  | 0.2766          | 0.5657    | 0.2873 | 0.3811 | 0.9399   |
| No log        | 2.0   | 426  | 0.2634          | 0.6267    | 0.3438 | 0.4440 | 0.9447   |
| 0.1828        | 3.0   | 639  | 0.3173          | 0.6354    | 0.2697 | 0.3787 | 0.9432   |
| 0.1828        | 4.0   | 852  | 0.3102          | 0.6018    | 0.3670 | 0.4560 | 0.9470   |
| 0.0475        | 5.0   | 1065 | 0.3047          | 0.5914    | 0.3957 | 0.4742 | 0.9478   |
| 0.0475        | 6.0   | 1278 | 0.3226          | 0.5927    | 0.4059 | 0.4818 | 0.9481   |
| 0.0475        | 7.0   | 1491 | 0.3109          | 0.5709    | 0.4291 | 0.4899 | 0.9486   |
| 0.0212        | 8.0   | 1704 | 0.3609          | 0.6200    | 0.3855 | 0.4754 | 0.9474   |
| 0.0212        | 9.0   | 1917 | 0.3236          | 0.5587    | 0.4365 | 0.4901 | 0.9486   |
| 0.0117        | 10.0  | 2130 | 0.3321          | 0.5759    | 0.4254 | 0.4893 | 0.9489   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1