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
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<p align="center">Publish at Findings of ACL 2023</p>
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<p align="center">Publish at Findings of ACL 2023</p>
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<p align="center"> <a href="https://arxiv.org/abs/2203.07648" target="_blank">Paper</a></p>
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