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crabz
/
distil-slovakbert-upos

Token Classification
Transformers
PyTorch
roberta
Generated from Trainer
Eval Results
Model card Files Community
distil-slovakbert-upos
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---
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
inference: false
model-index:
- name: distil-slovakbert-upos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies sk_snk
      type: universal_dependencies
      args: sk_snk
    metrics:
    - name: Precision
      type: precision
      value: 0.9771104035797263
    - name: Recall
      type: recall
      value: 0.9785418821096173
    - name: F1
      type: f1
      value: 0.9778256189451022
    - name: Accuracy
      type: accuracy
      value: 0.9800851200513933
---

<!-- 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. -->

# distil-slovakbert-upos

This model is a fine-tuned version of [crabz/distil-slovakbert](https://huggingface.co/crabz/distil-slovakbert) on the universal_dependencies sk_snk dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Precision: 0.9771
- Recall: 0.9785
- F1: 0.9778
- Accuracy: 0.9801

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 266  | 0.2168          | 0.9570    | 0.9554 | 0.9562 | 0.9610   |
| 0.3935        | 2.0   | 532  | 0.1416          | 0.9723    | 0.9736 | 0.9730 | 0.9740   |
| 0.3935        | 3.0   | 798  | 0.1236          | 0.9722    | 0.9735 | 0.9728 | 0.9747   |
| 0.0664        | 4.0   | 1064 | 0.1195          | 0.9722    | 0.9741 | 0.9732 | 0.9766   |
| 0.0664        | 5.0   | 1330 | 0.1160          | 0.9764    | 0.9772 | 0.9768 | 0.9789   |
| 0.0377        | 6.0   | 1596 | 0.1194          | 0.9763    | 0.9776 | 0.9770 | 0.9790   |
| 0.0377        | 7.0   | 1862 | 0.1188          | 0.9740    | 0.9755 | 0.9748 | 0.9777   |
| 0.024         | 8.0   | 2128 | 0.1188          | 0.9762    | 0.9777 | 0.9769 | 0.9793   |
| 0.024         | 9.0   | 2394 | 0.1207          | 0.9774    | 0.9789 | 0.9781 | 0.9802   |
| 0.0184        | 10.0  | 2660 | 0.1207          | 0.9771    | 0.9785 | 0.9778 | 0.9801   |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.11.0