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 tokenizer_config.json from clips/mfaq: direct link, hf CLI and curl.
- Browser
- Download file 464 Bytes
-
https://huggingface.co/clips/mfaq/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://clips/mfaq/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/clips/mfaq/resolve/main/tokenizer_config.json
464 Bytes
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "additional_special_tokens": ["<Q>", "<A>", "<link>"], "model_max_length": 128, "special_tokens_map_file": null, "name_or_path": "output/1024/checkpoint-3000", "tokenizer_class": "XLMRobertaTokenizer"} |