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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
adding TensorFlow
Browse files- config.json +1 -1
- tf_model.h5 +3 -0
config.json
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{
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"_name_or_path": "
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"architectures": [
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"XLMRobertaModel"
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],
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{
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"_name_or_path": "clips/mfaq",
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"architectures": [
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"XLMRobertaModel"
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],
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:840cd21152b9d1a5a435331634f529d77500a36bfbc19c8902f8c5a3e485d9de
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size 1112450672
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