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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-sentiment-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: basque_glue
type: basque_glue
config: bec
split: validation
args: bec
metrics:
- name: Accuracy
type: accuracy
value: 0.6290322580645161
- name: F1
type: f1
value: 0.6290834931512662
- name: Precision
type: precision
value: 0.630304630215078
- name: Recall
type: recall
value: 0.6290322580645161
---
<!-- 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-sentiment-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: 4.0012
- Accuracy: 0.6290
- F1: 0.6291
- Precision: 0.6303
- Recall: 0.6290
## 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: 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 380 | 0.7366 | 0.6736 | 0.6589 | 0.6711 | 0.6736 |
| 0.7679 | 2.0 | 760 | 0.7654 | 0.6767 | 0.6692 | 0.6726 | 0.6767 |
| 0.4846 | 3.0 | 1140 | 0.9844 | 0.6621 | 0.6599 | 0.6681 | 0.6621 |
| 0.2952 | 4.0 | 1520 | 1.1162 | 0.6375 | 0.6371 | 0.6473 | 0.6375 |
| 0.2952 | 5.0 | 1900 | 1.4234 | 0.6329 | 0.6343 | 0.6425 | 0.6329 |
| 0.192 | 6.0 | 2280 | 1.8570 | 0.6413 | 0.6362 | 0.6424 | 0.6413 |
| 0.159 | 7.0 | 2660 | 2.1968 | 0.6152 | 0.6086 | 0.6152 | 0.6152 |
| 0.1265 | 8.0 | 3040 | 2.1853 | 0.6283 | 0.6267 | 0.6267 | 0.6283 |
| 0.1265 | 9.0 | 3420 | 2.1953 | 0.6467 | 0.6441 | 0.6435 | 0.6467 |
| 0.0807 | 10.0 | 3800 | 2.2806 | 0.6367 | 0.6381 | 0.6480 | 0.6367 |
| 0.0688 | 11.0 | 4180 | 2.7982 | 0.6175 | 0.6167 | 0.6356 | 0.6175 |
| 0.0675 | 12.0 | 4560 | 2.5182 | 0.6605 | 0.6587 | 0.6584 | 0.6605 |
| 0.0675 | 13.0 | 4940 | 2.6544 | 0.6413 | 0.6315 | 0.6391 | 0.6413 |
| 0.0451 | 14.0 | 5320 | 2.5889 | 0.6459 | 0.6427 | 0.6424 | 0.6459 |
| 0.0432 | 15.0 | 5700 | 2.8100 | 0.6290 | 0.6299 | 0.6359 | 0.6290 |
| 0.0297 | 16.0 | 6080 | 2.9983 | 0.6275 | 0.6262 | 0.6263 | 0.6275 |
| 0.0297 | 17.0 | 6460 | 2.7803 | 0.6313 | 0.6289 | 0.6311 | 0.6313 |
| 0.0369 | 18.0 | 6840 | 2.9602 | 0.6283 | 0.6287 | 0.6353 | 0.6283 |
| 0.0289 | 19.0 | 7220 | 2.9911 | 0.6298 | 0.6309 | 0.6356 | 0.6298 |
| 0.0251 | 20.0 | 7600 | 2.8634 | 0.6344 | 0.6350 | 0.6364 | 0.6344 |
| 0.0251 | 21.0 | 7980 | 2.7171 | 0.6406 | 0.6378 | 0.6375 | 0.6406 |
| 0.0332 | 22.0 | 8360 | 3.0386 | 0.6275 | 0.6215 | 0.6245 | 0.6275 |
| 0.0212 | 23.0 | 8740 | 2.9876 | 0.6313 | 0.6319 | 0.6344 | 0.6313 |
| 0.0218 | 24.0 | 9120 | 2.9776 | 0.6283 | 0.6267 | 0.6348 | 0.6283 |
| 0.0189 | 25.0 | 9500 | 2.9596 | 0.6329 | 0.6340 | 0.6381 | 0.6329 |
| 0.0189 | 26.0 | 9880 | 3.0420 | 0.6329 | 0.6324 | 0.6380 | 0.6329 |
| 0.0172 | 27.0 | 10260 | 3.3335 | 0.6336 | 0.6348 | 0.6369 | 0.6336 |
| 0.0054 | 28.0 | 10640 | 3.2843 | 0.6429 | 0.6442 | 0.6466 | 0.6429 |
| 0.0065 | 29.0 | 11020 | 3.4868 | 0.6275 | 0.6291 | 0.6399 | 0.6275 |
| 0.0065 | 30.0 | 11400 | 3.8241 | 0.6175 | 0.6174 | 0.6209 | 0.6175 |
| 0.0108 | 31.0 | 11780 | 3.5833 | 0.6260 | 0.6275 | 0.6317 | 0.6260 |
| 0.0127 | 32.0 | 12160 | 3.5452 | 0.6183 | 0.6203 | 0.6283 | 0.6183 |
| 0.0092 | 33.0 | 12540 | 3.8349 | 0.6167 | 0.6167 | 0.6389 | 0.6167 |
| 0.0092 | 34.0 | 12920 | 3.6464 | 0.6244 | 0.6260 | 0.6313 | 0.6244 |
| 0.0069 | 35.0 | 13300 | 3.7538 | 0.6352 | 0.6352 | 0.6359 | 0.6352 |
| 0.0028 | 36.0 | 13680 | 3.8862 | 0.6221 | 0.6243 | 0.6350 | 0.6221 |
| 0.0001 | 37.0 | 14060 | 3.9846 | 0.6229 | 0.6206 | 0.6252 | 0.6229 |
| 0.0001 | 38.0 | 14440 | 3.7743 | 0.6275 | 0.6287 | 0.6309 | 0.6275 |
| 0.0057 | 39.0 | 14820 | 3.9002 | 0.6290 | 0.6300 | 0.6319 | 0.6290 |
| 0.0004 | 40.0 | 15200 | 3.9651 | 0.6306 | 0.6315 | 0.6333 | 0.6306 |
| 0.0032 | 41.0 | 15580 | 4.0279 | 0.6206 | 0.6213 | 0.6365 | 0.6206 |
| 0.0032 | 42.0 | 15960 | 3.8244 | 0.6344 | 0.6342 | 0.6344 | 0.6344 |
| 0.0033 | 43.0 | 16340 | 3.9036 | 0.6198 | 0.6205 | 0.6237 | 0.6198 |
| 0.003 | 44.0 | 16720 | 4.0028 | 0.6198 | 0.6214 | 0.6263 | 0.6198 |
| 0.0005 | 45.0 | 17100 | 3.9621 | 0.6306 | 0.6315 | 0.6361 | 0.6306 |
| 0.0005 | 46.0 | 17480 | 3.9682 | 0.6306 | 0.6297 | 0.6298 | 0.6306 |
| 0.0003 | 47.0 | 17860 | 4.0103 | 0.6321 | 0.6310 | 0.6305 | 0.6321 |
| 0.0003 | 48.0 | 18240 | 3.9968 | 0.6321 | 0.6316 | 0.6317 | 0.6321 |
| 0.003 | 49.0 | 18620 | 3.9835 | 0.6298 | 0.6297 | 0.6304 | 0.6298 |
| 0.0005 | 50.0 | 19000 | 4.0012 | 0.6290 | 0.6291 | 0.6303 | 0.6290 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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