Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use IR-Cocktail/bert-mini-mean-v3-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use IR-Cocktail/bert-mini-mean-v3-msmarco with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("IR-Cocktail/bert-mini-mean-v3-msmarco") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use IR-Cocktail/bert-mini-mean-v3-msmarco with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("IR-Cocktail/bert-mini-mean-v3-msmarco") model = AutoModel.from_pretrained("IR-Cocktail/bert-mini-mean-v3-msmarco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d13ae5119a100ec328bd49d17cb5c608733b6b480dc5c26f3a601d6e06d17c8e
- Size of remote file:
- 44.7 MB
- SHA256:
- 72380931d85bc64f597380d7f1210bbe03044417c5f7257e0ec0f586b6c0f65e
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