---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 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: 0.5632040050062578
    - name: Recall
      type: recall
      value: 0.4170528266913809
    - name: F1
      type: f1
      value: 0.47923322683706066
    - name: Accuracy
      type: accuracy
      value: 0.9478859390363815
---

<!-- 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_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: 0.3832
- Precision: 0.5632
- Recall: 0.4171
- F1: 0.4792
- Accuracy: 0.9479

## 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: 8
- eval_batch_size: 8
- 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   | 425  | 0.2828          | 0.6021    | 0.3800 | 0.4659 | 0.9466   |
| 0.074         | 2.0   | 850  | 0.2955          | 0.5825    | 0.3892 | 0.4667 | 0.9474   |
| 0.0457        | 3.0   | 1275 | 0.3072          | 0.5857    | 0.4180 | 0.4878 | 0.9492   |
| 0.0234        | 4.0   | 1700 | 0.3430          | 0.5911    | 0.4059 | 0.4813 | 0.9481   |
| 0.0144        | 5.0   | 2125 | 0.3468          | 0.5406    | 0.4198 | 0.4726 | 0.9476   |
| 0.0107        | 6.0   | 2550 | 0.3742          | 0.5541    | 0.4032 | 0.4667 | 0.9470   |
| 0.0107        | 7.0   | 2975 | 0.3779          | 0.5861    | 0.4133 | 0.4848 | 0.9483   |
| 0.0081        | 8.0   | 3400 | 0.3802          | 0.5537    | 0.4013 | 0.4653 | 0.9477   |
| 0.0059        | 9.0   | 3825 | 0.3750          | 0.5511    | 0.4198 | 0.4766 | 0.9478   |
| 0.0033        | 10.0  | 4250 | 0.3832          | 0.5632    | 0.4171 | 0.4792 | 0.9479   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1