Instructions to use hellowYJY/xlm-roberta-base-finetuned-panx-de-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hellowYJY/xlm-roberta-base-finetuned-panx-de-fr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hellowYJY/xlm-roberta-base-finetuned-panx-de-fr")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hellowYJY/xlm-roberta-base-finetuned-panx-de-fr") model = AutoModelForTokenClassification.from_pretrained("hellowYJY/xlm-roberta-base-finetuned-panx-de-fr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base-finetuned-panx-de-fr
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: 0.1607
- F1: 0.8600
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: 24
- eval_batch_size: 24
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.2815 | 1.0 | 715 | 0.1884 | 0.8069 |
| 0.1477 | 2.0 | 1430 | 0.1573 | 0.8506 |
| 0.0954 | 3.0 | 2145 | 0.1607 | 0.8600 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for hellowYJY/xlm-roberta-base-finetuned-panx-de-fr
Base model
FacebookAI/xlm-roberta-base