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 training_args.bin from ltuzova/citation_intent_classification_roberta_dapt: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/ltuzova/citation_intent_classification_roberta_dapt/resolve/main/training_args.bin
- Command line
-
hf download hf://ltuzova/citation_intent_classification_roberta_dapt/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ltuzova/citation_intent_classification_roberta_dapt/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- a5b94a9bd5d29ceb7f0975c0a4050aa288be023fa1dd8b90a37cf4f5747d01ab
- Size of remote file:
- 4.03 kB
- SHA256:
- c94d1d5d1aecb4a32ccbe8ba829890317831fd745a25de921a3eba7d002cbd61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.