---
library_name: transformers
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: 0.576214405360134
    - name: Recall
      type: recall
      value: 0.31881371640407785
    - name: F1
      type: f1
      value: 0.41050119331742246
    - name: Accuracy
      type: accuracy
      value: 0.94258475482023
---

<!-- 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: 0.2722
- Precision: 0.5762
- Recall: 0.3188
- F1: 0.4105
- Accuracy: 0.9426

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.2801          | 0.5214    | 0.2373 | 0.3261 | 0.9384   |
| No log        | 2.0   | 426  | 0.2722          | 0.5762    | 0.3188 | 0.4105 | 0.9426   |


### Framework versions

- Transformers 4.45.0
- Pytorch 2.4.1+cpu
- Datasets 3.0.0
- Tokenizers 0.20.0