Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ltuzova/citation_intent_classification_roberta_dapt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltuzova/citation_intent_classification_roberta_dapt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ltuzova/citation_intent_classification_roberta_dapt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ltuzova/citation_intent_classification_roberta_dapt") model = AutoModelForSequenceClassification.from_pretrained("ltuzova/citation_intent_classification_roberta_dapt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ltuzova/citation_intent_classification_roberta_dapt: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/ltuzova/citation_intent_classification_roberta_dapt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ltuzova/citation_intent_classification_roberta_dapt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ltuzova/citation_intent_classification_roberta_dapt/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- b7237b3b5eb6f20f4d1d1cbca749c8603c89e376733f8bd7a1da1b33114af9cf
- Size of remote file:
- 499 MB
- SHA256:
- 6168d3ea6ea415ac5d4b8f9268699d58544bd98aa79aeabeb94e0511fee359c9
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