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--- |
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license: cc-by-sa-4.0 |
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base_model: ClassCat/roberta-small-basque |
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tags: |
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- generated_from_trainer |
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datasets: |
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- basque_glue |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: XLM-EusBERTa-sentiment-classification |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: basque_glue |
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type: basque_glue |
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config: bec |
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split: validation |
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args: bec |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6290322580645161 |
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- name: F1 |
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type: f1 |
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value: 0.6290834931512662 |
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- name: Precision |
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type: precision |
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value: 0.630304630215078 |
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- name: Recall |
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type: recall |
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value: 0.6290322580645161 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# XLM-EusBERTa-sentiment-classification |
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This model is a fine-tuned version of [ClassCat/roberta-small-basque](https://huggingface.co/ClassCat/roberta-small-basque) on the basque_glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.0012 |
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- Accuracy: 0.6290 |
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- F1: 0.6291 |
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- Precision: 0.6303 |
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- Recall: 0.6290 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 380 | 0.7366 | 0.6736 | 0.6589 | 0.6711 | 0.6736 | |
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| 0.7679 | 2.0 | 760 | 0.7654 | 0.6767 | 0.6692 | 0.6726 | 0.6767 | |
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| 0.4846 | 3.0 | 1140 | 0.9844 | 0.6621 | 0.6599 | 0.6681 | 0.6621 | |
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| 0.2952 | 4.0 | 1520 | 1.1162 | 0.6375 | 0.6371 | 0.6473 | 0.6375 | |
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| 0.2952 | 5.0 | 1900 | 1.4234 | 0.6329 | 0.6343 | 0.6425 | 0.6329 | |
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| 0.192 | 6.0 | 2280 | 1.8570 | 0.6413 | 0.6362 | 0.6424 | 0.6413 | |
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| 0.159 | 7.0 | 2660 | 2.1968 | 0.6152 | 0.6086 | 0.6152 | 0.6152 | |
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| 0.1265 | 8.0 | 3040 | 2.1853 | 0.6283 | 0.6267 | 0.6267 | 0.6283 | |
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| 0.1265 | 9.0 | 3420 | 2.1953 | 0.6467 | 0.6441 | 0.6435 | 0.6467 | |
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| 0.0807 | 10.0 | 3800 | 2.2806 | 0.6367 | 0.6381 | 0.6480 | 0.6367 | |
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| 0.0688 | 11.0 | 4180 | 2.7982 | 0.6175 | 0.6167 | 0.6356 | 0.6175 | |
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| 0.0675 | 12.0 | 4560 | 2.5182 | 0.6605 | 0.6587 | 0.6584 | 0.6605 | |
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| 0.0675 | 13.0 | 4940 | 2.6544 | 0.6413 | 0.6315 | 0.6391 | 0.6413 | |
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| 0.0451 | 14.0 | 5320 | 2.5889 | 0.6459 | 0.6427 | 0.6424 | 0.6459 | |
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| 0.0432 | 15.0 | 5700 | 2.8100 | 0.6290 | 0.6299 | 0.6359 | 0.6290 | |
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| 0.0297 | 16.0 | 6080 | 2.9983 | 0.6275 | 0.6262 | 0.6263 | 0.6275 | |
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| 0.0297 | 17.0 | 6460 | 2.7803 | 0.6313 | 0.6289 | 0.6311 | 0.6313 | |
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| 0.0369 | 18.0 | 6840 | 2.9602 | 0.6283 | 0.6287 | 0.6353 | 0.6283 | |
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| 0.0289 | 19.0 | 7220 | 2.9911 | 0.6298 | 0.6309 | 0.6356 | 0.6298 | |
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| 0.0251 | 20.0 | 7600 | 2.8634 | 0.6344 | 0.6350 | 0.6364 | 0.6344 | |
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| 0.0251 | 21.0 | 7980 | 2.7171 | 0.6406 | 0.6378 | 0.6375 | 0.6406 | |
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| 0.0332 | 22.0 | 8360 | 3.0386 | 0.6275 | 0.6215 | 0.6245 | 0.6275 | |
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| 0.0212 | 23.0 | 8740 | 2.9876 | 0.6313 | 0.6319 | 0.6344 | 0.6313 | |
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| 0.0218 | 24.0 | 9120 | 2.9776 | 0.6283 | 0.6267 | 0.6348 | 0.6283 | |
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| 0.0189 | 25.0 | 9500 | 2.9596 | 0.6329 | 0.6340 | 0.6381 | 0.6329 | |
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| 0.0189 | 26.0 | 9880 | 3.0420 | 0.6329 | 0.6324 | 0.6380 | 0.6329 | |
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| 0.0172 | 27.0 | 10260 | 3.3335 | 0.6336 | 0.6348 | 0.6369 | 0.6336 | |
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| 0.0054 | 28.0 | 10640 | 3.2843 | 0.6429 | 0.6442 | 0.6466 | 0.6429 | |
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| 0.0065 | 29.0 | 11020 | 3.4868 | 0.6275 | 0.6291 | 0.6399 | 0.6275 | |
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| 0.0065 | 30.0 | 11400 | 3.8241 | 0.6175 | 0.6174 | 0.6209 | 0.6175 | |
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| 0.0108 | 31.0 | 11780 | 3.5833 | 0.6260 | 0.6275 | 0.6317 | 0.6260 | |
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| 0.0127 | 32.0 | 12160 | 3.5452 | 0.6183 | 0.6203 | 0.6283 | 0.6183 | |
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| 0.0092 | 33.0 | 12540 | 3.8349 | 0.6167 | 0.6167 | 0.6389 | 0.6167 | |
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| 0.0092 | 34.0 | 12920 | 3.6464 | 0.6244 | 0.6260 | 0.6313 | 0.6244 | |
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| 0.0069 | 35.0 | 13300 | 3.7538 | 0.6352 | 0.6352 | 0.6359 | 0.6352 | |
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| 0.0028 | 36.0 | 13680 | 3.8862 | 0.6221 | 0.6243 | 0.6350 | 0.6221 | |
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| 0.0001 | 37.0 | 14060 | 3.9846 | 0.6229 | 0.6206 | 0.6252 | 0.6229 | |
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| 0.0001 | 38.0 | 14440 | 3.7743 | 0.6275 | 0.6287 | 0.6309 | 0.6275 | |
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| 0.0057 | 39.0 | 14820 | 3.9002 | 0.6290 | 0.6300 | 0.6319 | 0.6290 | |
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| 0.0004 | 40.0 | 15200 | 3.9651 | 0.6306 | 0.6315 | 0.6333 | 0.6306 | |
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| 0.0032 | 41.0 | 15580 | 4.0279 | 0.6206 | 0.6213 | 0.6365 | 0.6206 | |
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| 0.0032 | 42.0 | 15960 | 3.8244 | 0.6344 | 0.6342 | 0.6344 | 0.6344 | |
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| 0.0033 | 43.0 | 16340 | 3.9036 | 0.6198 | 0.6205 | 0.6237 | 0.6198 | |
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| 0.003 | 44.0 | 16720 | 4.0028 | 0.6198 | 0.6214 | 0.6263 | 0.6198 | |
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| 0.0005 | 45.0 | 17100 | 3.9621 | 0.6306 | 0.6315 | 0.6361 | 0.6306 | |
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| 0.0005 | 46.0 | 17480 | 3.9682 | 0.6306 | 0.6297 | 0.6298 | 0.6306 | |
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| 0.0003 | 47.0 | 17860 | 4.0103 | 0.6321 | 0.6310 | 0.6305 | 0.6321 | |
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| 0.0003 | 48.0 | 18240 | 3.9968 | 0.6321 | 0.6316 | 0.6317 | 0.6321 | |
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| 0.003 | 49.0 | 18620 | 3.9835 | 0.6298 | 0.6297 | 0.6304 | 0.6298 | |
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| 0.0005 | 50.0 | 19000 | 4.0012 | 0.6290 | 0.6291 | 0.6303 | 0.6290 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
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