Instructions to use AAA01101312/xlm-roberta-base-finetuned-panx-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AAA01101312/xlm-roberta-base-finetuned-panx-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AAA01101312/xlm-roberta-base-finetuned-panx-en")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AAA01101312/xlm-roberta-base-finetuned-panx-en") model = AutoModelForTokenClassification.from_pretrained("AAA01101312/xlm-roberta-base-finetuned-panx-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base-finetuned-panx-en
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: 0.1745
- F1: 0.8555
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 |
|---|---|---|---|---|
| 0.235 | 1.0 | 835 | 0.2044 | 0.8053 |
| 0.1574 | 2.0 | 1670 | 0.1664 | 0.8442 |
| 0.0968 | 3.0 | 2505 | 0.1745 | 0.8555 |
Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for AAA01101312/xlm-roberta-base-finetuned-panx-en
Base model
FacebookAI/xlm-roberta-base