Instructions to use DeepaPeri/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 DeepaPeri/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="DeepaPeri/xlm-roberta-base-finetuned-panx-all")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DeepaPeri/xlm-roberta-base-finetuned-panx-all") model = AutoModelForTokenClassification.from_pretrained("DeepaPeri/xlm-roberta-base-finetuned-panx-all", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of DeepaPeri/xlm-roberta-base-finetuned-panx-hi-5-epochs on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2851
- F1: 0.9009
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.3035 | 1.0 | 281 | 0.2922 | 0.8457 |
| 0.1659 | 2.0 | 562 | 0.2768 | 0.8798 |
| 0.1108 | 3.0 | 843 | 0.2583 | 0.8707 |
| 0.0745 | 4.0 | 1124 | 0.2656 | 0.9043 |
| 0.0443 | 5.0 | 1405 | 0.2851 | 0.9009 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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