---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- szeged_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: szeged_ner
      type: szeged_ner
      config: business
      split: test
      args: business
    metrics:
    - name: Precision
      type: precision
      value: 0.8253343823760818
    - name: Recall
      type: recall
      value: 0.856326530612245
    - name: F1
      type: f1
      value: 0.8405448717948719
    - name: Accuracy
      type: accuracy
      value: 0.9829550592277783
---

<!-- 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_awesome_wnut_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the szeged_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0590
- Precision: 0.8253
- Recall: 0.8563
- F1: 0.8405
- Accuracy: 0.9830

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2068        | 1.0   | 511  | 0.0724          | 0.8008    | 0.8237 | 0.8121 | 0.9797   |
| 0.0835        | 2.0   | 1022 | 0.0590          | 0.8253    | 0.8563 | 0.8405 | 0.9830   |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3