Instructions to use 2O24dpower2024/xlm-roberta-base-finetuned-panx-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 2O24dpower2024/xlm-roberta-base-finetuned-panx-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="2O24dpower2024/xlm-roberta-base-finetuned-panx-all")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("2O24dpower2024/xlm-roberta-base-finetuned-panx-all") model = AutoModelForTokenClassification.from_pretrained("2O24dpower2024/xlm-roberta-base-finetuned-panx-all") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base-finetuned-panx-all
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: 0.1742
- F1: 0.8541
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.3026 | 1.0 | 835 | 0.1851 | 0.8182 |
| 0.1575 | 2.0 | 1670 | 0.1712 | 0.8413 |
| 0.1031 | 3.0 | 2505 | 0.1742 | 0.8541 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
- 7
Model tree for 2O24dpower2024/xlm-roberta-base-finetuned-panx-all
Base model
FacebookAI/xlm-roberta-base