Instructions to use pmpc/xlm-roberta-base2longformer-8192 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pmpc/xlm-roberta-base2longformer-8192 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pmpc/xlm-roberta-base2longformer-8192")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pmpc/xlm-roberta-base2longformer-8192") model = AutoModel.from_pretrained("pmpc/xlm-roberta-base2longformer-8192") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-longformer-base-16384
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.27.4
- TensorFlow 2.11.0
- Datasets 2.1.0
- Tokenizers 0.13.2
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