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xnli_m_bert_only_sw
This model is a fine-tuned version of bert-base-multilingual-cased on the xnli dataset. It achieves the following results on the evaluation set:
- Loss: 1.5193
- Accuracy: 0.6289
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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8323 | 1.0 | 3068 | 0.8640 | 0.6217 |
| 0.7614 | 2.0 | 6136 | 0.7812 | 0.6598 |
| 0.6875 | 3.0 | 9204 | 0.8466 | 0.6394 |
| 0.6065 | 4.0 | 12272 | 0.8354 | 0.6538 |
| 0.5219 | 5.0 | 15340 | 0.8810 | 0.6550 |
| 0.4317 | 6.0 | 18408 | 0.9880 | 0.6554 |
| 0.3532 | 7.0 | 21476 | 1.1403 | 0.6390 |
| 0.2893 | 8.0 | 24544 | 1.1935 | 0.6390 |
| 0.2351 | 9.0 | 27612 | 1.3805 | 0.6390 |
| 0.1928 | 10.0 | 30680 | 1.5193 | 0.6289 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
