xlm_r_base-finetuned_after_mrp-v2-different-deluge-11
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4623
- Precision 0: 0.8620
- Precision 1: 0.8034
- Recall 0: 0.8667
- Recall 1: 0.7970
- F1 0: 0.8643
- F1 1: 0.8002
- Precision Weighted: 0.8382
- Recall Weighted: 0.8384
- F1 Weighted: 0.8383
- F1 Macro: 0.8323
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: 2e-05
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision 0 | Precision 1 | Recall 0 | Recall 1 | F1 0 | F1 1 | Precision Weighted | Recall Weighted | F1 Weighted | F1 Macro |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.5617 | 1.0 | 469 | 0.4641 | 0.7967 | 0.8424 | 0.9158 | 0.6581 | 0.8521 | 0.7389 | 0.8153 | 0.8112 | 0.8062 | 0.7955 |
0.3872 | 2.0 | 938 | 0.4605 | 0.8990 | 0.6649 | 0.6949 | 0.8857 | 0.7839 | 0.7596 | 0.8039 | 0.7724 | 0.7740 | 0.7718 |
0.3888 | 3.0 | 1407 | 0.3794 | 0.8290 | 0.8341 | 0.9010 | 0.7281 | 0.8635 | 0.7775 | 0.8311 | 0.8308 | 0.8286 | 0.8205 |
0.2986 | 4.0 | 1876 | 0.3995 | 0.8507 | 0.8186 | 0.8828 | 0.7734 | 0.8665 | 0.7953 | 0.8377 | 0.8384 | 0.8376 | 0.8309 |
0.2518 | 5.0 | 2345 | 0.4623 | 0.8620 | 0.8034 | 0.8667 | 0.7970 | 0.8643 | 0.8002 | 0.8382 | 0.8384 | 0.8383 | 0.8323 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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