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---
license: cc-by-sa-4.0
base_model: ClassCat/roberta-small-basque
tags:
- generated_from_trainer
datasets:
- basque_glue
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: XLM-EusBERTa-topic-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: basque_glue
type: basque_glue
config: bhtc
split: validation
args: bhtc
metrics:
- name: Accuracy
type: accuracy
value: 0.6494345718901454
- name: F1
type: f1
value: 0.6432667195761544
- name: Precision
type: precision
value: 0.6447174737999963
- name: Recall
type: recall
value: 0.6494345718901454
---
<!-- 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. -->
# 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
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