Instructions to use OrnsteinThe3rd/xlm-roberta-base-finetuned-panx-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OrnsteinThe3rd/xlm-roberta-base-finetuned-panx-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OrnsteinThe3rd/xlm-roberta-base-finetuned-panx-ar")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OrnsteinThe3rd/xlm-roberta-base-finetuned-panx-ar") model = AutoModelForTokenClassification.from_pretrained("OrnsteinThe3rd/xlm-roberta-base-finetuned-panx-ar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base-finetuned-panx-ar
This model is a fine-tuned version of tner/xlm-roberta-base-panx-dataset-ar on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1973
- F1: 0.8776
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: 64
- eval_batch_size: 64
- 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 | F1 |
|---|---|---|---|---|
| 0.2329 | 1.0 | 188 | 0.1973 | 0.8776 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for OrnsteinThe3rd/xlm-roberta-base-finetuned-panx-ar
Base model
tner/xlm-roberta-base-panx-dataset-ar