Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence") model = AutoModelForSequenceClassification.from_pretrained("DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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- Tokenizers 0.12.1
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### Cite
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```bibtex
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```
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- Tokenizers 0.12.1
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### Cite
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Should use our models in your work, please consider citing us as:
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```bibtex
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@article{BERTOLINI2024406,
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title = {DReAMy: a library for the automatic analysis and annotation of dream reports with multilingual large language models},
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journal = {Sleep Medicine},
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volume = {115},
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pages = {406-407},
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year = {2024},
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note = {Abstracts from the 17th World Sleep Congress},
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issn = {1389-9457},
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doi = {https://doi.org/10.1016/j.sleep.2023.11.1092},
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url = {https://www.sciencedirect.com/science/article/pii/S1389945723015186},
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author = {L. Bertolini and A. Michalak and J. Weeds}
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}
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```
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