transformers tensorboard safetensors roberta text-classification generated_from_trainer dataset:basque_glue base_model:ClassCat/roberta-small-basque base_model:finetune:ClassCat/roberta-small-basque license:cc-by-sa-4.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: ClassCat/roberta-small-basque
datasets:
- basque_glue
license: cc-by-sa-4.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: XLM-EusBERTa-topic-classification
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: basque_glue
      type: basque_glue
      config: bhtc
      split: validation
      args: bhtc
    metrics:
    - type: accuracy
      value: 0.6494345718901454
      name: Accuracy
    - type: f1
      value: 0.6432667195761544
      name: F1
    - type: precision
      value: 0.6447174737999963
      name: Precision
    - type: recall
      value: 0.6494345718901454
      name: Recall
---

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# XLM-EusBERTa-topic-classification

This model is a fine-tuned version of [ClassCat/roberta-small-basque](https://huggingface.co/ClassCat/roberta-small-basque) on the basque_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2158
- Accuracy: 0.6494
- F1: 0.6433
- Precision: 0.6447
- Recall: 0.6494

## 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: 0.0001
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2439        | 1.0   | 1074  | 1.1310          | 0.6581   | 0.6316 | 0.6139    | 0.6581 |
| 0.9539        | 2.0   | 2148  | 1.3019          | 0.6117   | 0.6034 | 0.6465    | 0.6117 |
| 0.579         | 3.0   | 3222  | 1.5533          | 0.6645   | 0.6524 | 0.6661    | 0.6645 |
| 0.3766        | 4.0   | 4296  | 2.3287          | 0.6381   | 0.6283 | 0.6590    | 0.6381 |
| 0.2641        | 5.0   | 5370  | 2.2805          | 0.6597   | 0.6515 | 0.6707    | 0.6597 |
| 0.1707        | 6.0   | 6444  | 2.6621          | 0.6397   | 0.6399 | 0.6581    | 0.6397 |
| 0.1537        | 7.0   | 7518  | 2.9116          | 0.6408   | 0.6336 | 0.6452    | 0.6408 |
| 0.0867        | 8.0   | 8592  | 3.1775          | 0.6344   | 0.6337 | 0.6531    | 0.6344 |
| 0.0779        | 9.0   | 9666  | 3.2514          | 0.6543   | 0.6471 | 0.6593    | 0.6543 |
| 0.0587        | 10.0  | 10740 | 3.3244          | 0.6457   | 0.6424 | 0.6488    | 0.6457 |
| 0.0322        | 11.0  | 11814 | 3.8090          | 0.6214   | 0.6244 | 0.6488    | 0.6214 |
| 0.0139        | 12.0  | 12888 | 3.8642          | 0.6247   | 0.6176 | 0.6424    | 0.6247 |
| 0.0256        | 13.0  | 13962 | 3.8734          | 0.6419   | 0.6327 | 0.6398    | 0.6419 |
| 0.0046        | 14.0  | 15036 | 4.0934          | 0.6365   | 0.6330 | 0.6463    | 0.6365 |
| 0.0036        | 15.0  | 16110 | 4.0890          | 0.6484   | 0.6416 | 0.6469    | 0.6484 |
| 0.0023        | 16.0  | 17184 | 4.0978          | 0.6505   | 0.6440 | 0.6470    | 0.6505 |
| 0.0008        | 17.0  | 18258 | 4.1709          | 0.6478   | 0.6418 | 0.6449    | 0.6478 |
| 0.0014        | 18.0  | 19332 | 4.1715          | 0.6505   | 0.6446 | 0.6458    | 0.6505 |
| 0.0007        | 19.0  | 20406 | 4.2158          | 0.6489   | 0.6427 | 0.6443    | 0.6489 |
| 0.0039        | 20.0  | 21480 | 4.2158          | 0.6494   | 0.6433 | 0.6447    | 0.6494 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
