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:
- ebe4acb7acf5a8461d9102625b52ea61609f959597b8b8233c93290f93cca741
- Size of remote file:
- 694 MB
- SHA256:
- a08beb5351a02058c030638f4cc0b66603b31706dd26a46c41e6544cf328163e
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