metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-base-chn
results: []
xlm-roberta-base-chn
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1099
- Accuracy: 0.8201
- F1 Binary: 0.5729
- Precision: 0.4830
- Recall: 0.7040
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 39
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
---|---|---|---|---|---|---|---|
No log | 1.0 | 397 | 0.1365 | 0.8182 | 0.4844 | 0.4713 | 0.4982 |
0.1411 | 2.0 | 794 | 0.1133 | 0.8210 | 0.5375 | 0.4825 | 0.6066 |
0.111 | 3.0 | 1191 | 0.1364 | 0.8655 | 0.5929 | 0.6158 | 0.5717 |
0.0802 | 4.0 | 1588 | 0.1099 | 0.8201 | 0.5729 | 0.4830 | 0.7040 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0