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xlm-roberta-large-finetuned-combined-DS
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9169
- Accuracy: 0.6587
- Precision: 0.6417
- Recall: 0.6445
- F1: 0.6396
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|---|---|---|
1.0116 | 0.5 | 711 | 0.9454 | 0.5892 | 0.6556 | 0.5190 | 0.4582 |
0.8678 | 1.0 | 1422 | 0.9676 | 0.6503 | 0.6383 | 0.6076 | 0.6103 |
0.7644 | 1.5 | 2133 | 0.8672 | 0.6355 | 0.6142 | 0.6206 | 0.6166 |
0.8198 | 2.0 | 2844 | 0.8319 | 0.6713 | 0.6460 | 0.6448 | 0.6453 |
0.6665 | 2.5 | 3555 | 0.8342 | 0.6538 | 0.6359 | 0.6414 | 0.6349 |
0.6473 | 3.0 | 4266 | 0.9169 | 0.6587 | 0.6417 | 0.6445 | 0.6396 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1