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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clips/mfaq") model = AutoModel.from_pretrained("clips/mfaq", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from clips/mfaq: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/clips/mfaq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://clips/mfaq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/clips/mfaq/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 3c16a324f207d302958bd072e26e747cdbf38a4beee9d651e38b96a427393810
- Size of remote file:
- 1.11 GB
- SHA256:
- dd4269157ebf5f786ed8478fd1339aaa3fd582c24acebe1f56ca8a6c228df407
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