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hun_wnut_modell

Token Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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hun_wnut_modell
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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- szeged_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hun_wnut_modell
  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: 0.8590342679127726
    - name: Recall
      type: recall
      value: 0.9004081632653061
    - name: F1
      type: f1
      value: 0.8792347548824233
    - name: Accuracy
      type: accuracy
      value: 0.9881996563884619
---

<!-- 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. -->

# hun_wnut_modell

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: 0.0419
- Precision: 0.8590
- Recall: 0.9004
- F1: 0.8792
- Accuracy: 0.9882

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2035        | 1.0   | 511  | 0.0665          | 0.8124    | 0.8343 | 0.8232 | 0.9813   |
| 0.075         | 2.0   | 1022 | 0.0501          | 0.8280    | 0.8841 | 0.8551 | 0.9847   |
| 0.0498        | 3.0   | 1533 | 0.0444          | 0.8452    | 0.8914 | 0.8677 | 0.9866   |
| 0.0354        | 4.0   | 2044 | 0.0417          | 0.8661    | 0.8980 | 0.8818 | 0.9885   |
| 0.0275        | 5.0   | 2555 | 0.0419          | 0.8590    | 0.9004 | 0.8792 | 0.9882   |


### Framework versions

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