xnli_m_bert_only_en_single_gpu
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: 1.0082
- Accuracy: 0.8076
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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3328 | 1.0 | 3068 | 0.5433 | 0.8036 |
| 0.259 | 2.0 | 6136 | 0.5708 | 0.8008 |
| 0.2023 | 3.0 | 9204 | 0.6475 | 0.8048 |
| 0.1362 | 4.0 | 12272 | 0.7661 | 0.7972 |
| 0.0945 | 5.0 | 15340 | 0.8333 | 0.8008 |
| 0.0665 | 6.0 | 18408 | 0.9312 | 0.8092 |
| 0.0463 | 7.0 | 21476 | 1.0082 | 0.8076 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
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