Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use ramnathv/clips-mfaq-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ramnathv/clips-mfaq-test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ramnathv/clips-mfaq-test") sentences = [ "<Q>How many models can I host on HuggingFace?", "<A>All plans come with unlimited private models and datasets.", "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem.", "<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." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use ramnathv/clips-mfaq-test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ramnathv/clips-mfaq-test") model = AutoModel.from_pretrained("ramnathv/clips-mfaq-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ramnathv/clips-mfaq-test: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/ramnathv/clips-mfaq-test/resolve/main/tokenizer.json
- Command line
-
hf download hf://ramnathv/clips-mfaq-test/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ramnathv/clips-mfaq-test/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- a478c006fce0750de138ca28cd28dd4b9f02222d15406ed1adb516ac56677ad1
- Size of remote file:
- 17.1 MB
- SHA256:
- c202c2904979914d10811ca26aaf9672c41820a3d25cb432040fe1405be30ffb
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