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") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Checkpoints of Models Pre-Trained with InfoDCL
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* InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji
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* InfoDCL-RoBERTa trained with
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## Model Performance
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## Checkpoints of Models Pre-Trained with InfoDCL
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* InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji
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* InfoDCL-RoBERTa trained with TweetHashtag-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag
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## Model Performance
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