---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- szeged_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test-train-model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: szeged_ner
      type: szeged_ner
      config: business
      split: validation
      args: business
    metrics:
    - name: Precision
      type: precision
      value: 0.9325044404973357
    - name: Recall
      type: recall
      value: 0.9308510638297872
    - name: F1
      type: f1
      value: 0.9316770186335402
    - name: Accuracy
      type: accuracy
      value: 0.9925327242378986
---

<!-- 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. -->

# test-train-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: 0.0319
- Precision: 0.9325
- Recall: 0.9309
- F1: 0.9317
- Accuracy: 0.9925

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2029        | 1.0   | 511  | 0.0493          | 0.8734    | 0.8564 | 0.8648 | 0.9873   |
| 0.0756        | 2.0   | 1022 | 0.0381          | 0.8930    | 0.9025 | 0.8977 | 0.9897   |
| 0.0489        | 3.0   | 1533 | 0.0327          | 0.925     | 0.9184 | 0.9217 | 0.9921   |
| 0.0339        | 4.0   | 2044 | 0.0323          | 0.9385    | 0.9202 | 0.9293 | 0.9926   |
| 0.0258        | 5.0   | 2555 | 0.0319          | 0.9325    | 0.9309 | 0.9317 | 0.9925   |


### Framework versions

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