metadata
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large
results: []
xlm-roberta-large
This model is a fine-tuned version of xlm-roberta-large on the None dataset.
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: 8
- 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
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Date | Loc | Org | Per | Price | Product | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 100 | 0.0445 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 0.8984375, 'recall': 0.9274193548387096, 'f1': 0.9126984126984127, 'number': 124} | {'precision': 0.8448275862068966, 'recall': 0.8305084745762712, 'f1': 0.8376068376068375, 'number': 59} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 70} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79} | {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13} | 0.9406 | 0.9479 | 0.9442 | 0.9859 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0