Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Cutting3dg3/MMiniLM_TID_d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Cutting3dg3/MMiniLM_TID_d with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Cutting3dg3/MMiniLM_TID_d") 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 Cutting3dg3/MMiniLM_TID_d with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Cutting3dg3/MMiniLM_TID_d") model = AutoModel.from_pretrained("Cutting3dg3/MMiniLM_TID_d", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Cutting3dg3/MMiniLM_TID_d: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Cutting3dg3/MMiniLM_TID_d/resolve/main/tokenizer.json
- Command line
-
hf download hf://Cutting3dg3/MMiniLM_TID_d/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Cutting3dg3/MMiniLM_TID_d/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- c9abfbcdb4ffc00d355a5aceeb3df3cad5ed9f079223b9434115e96b6423ffb1
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.