---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: the_first_model_develop_wnut
  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: 0.5735512630014858
    - name: Recall
      type: recall
      value: 0.3577386468952734
    - name: F1
      type: f1
      value: 0.44063926940639264
    - name: Accuracy
      type: accuracy
      value: 0.9459621221837459
---

<!-- 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. -->

# the_first_model_develop_wnut

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3410
- Precision: 0.5736
- Recall: 0.3577
- F1: 0.4406
- Accuracy: 0.9460

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 107  | 0.2753          | 0.6224    | 0.2734 | 0.3799 | 0.9402   |
| No log        | 2.0   | 214  | 0.2722          | 0.5637    | 0.3197 | 0.4080 | 0.9447   |
| No log        | 3.0   | 321  | 0.3145          | 0.5789    | 0.3401 | 0.4285 | 0.9458   |
| No log        | 4.0   | 428  | 0.3199          | 0.5218    | 0.3550 | 0.4225 | 0.9449   |
| 0.0914        | 5.0   | 535  | 0.3410          | 0.5736    | 0.3577 | 0.4406 | 0.9460   |


### Framework versions

- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.0