Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use clips/mfaq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clips/mfaq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/mfaq") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use clips/mfaq with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clips/mfaq") model = AutoModel.from_pretrained("clips/mfaq") - Inference
- Notebooks
- Google Colab
- Kaggle
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datasets:
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- clips/mfaq
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# MFAQ
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datasets:
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- clips/mfaq
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widget:
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source_sentence: "<Q>How many models can I host on HuggingFace?"
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sentences:
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- "<A>All plans come with unlimited private models and datasets."
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- "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem."
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- "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."
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# MFAQ
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