Feature Extraction
Transformers
PyTorch
English
roberta
social media
contrastive learning
text-embeddings-inference
Instructions to use UBC-NLP/InfoDCL-hashtag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/InfoDCL-hashtag with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="UBC-NLP/InfoDCL-hashtag")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/InfoDCL-hashtag") model = AutoModel.from_pretrained("UBC-NLP/InfoDCL-hashtag", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a981a8410c7218fa7747a9d07c2d8b9fd9f17981b672323bbbac10d43a92a70
- Size of remote file:
- 2.48 kB
- SHA256:
- ffb13b5d36b1eb7a1f37d6679405145078cf189ff6315a9cb364750b1ab2c3e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.