Model2Vec
Safetensors
sentence-transformers
German
multilingual
embeddings
static-embeddings
german
education
Instructions to use JanSchachtschabel/m2v-e5-large-edu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use JanSchachtschabel/m2v-e5-large-edu with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("JanSchachtschabel/m2v-e5-large-edu") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use JanSchachtschabel/m2v-e5-large-edu with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JanSchachtschabel/m2v-e5-large-edu") 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] - Notebooks
- Google Colab
- Kaggle
m2v-e5-large-edu: torch-free multilingual-e5-large static model + education keyword vocab (mean pooling, honest eval)
911164e verified Download tokenizer.json from JanSchachtschabel/m2v-e5-large-edu: direct link, hf CLI and curl.
- Browser
- Download file 15.2 MB
-
https://huggingface.co/JanSchachtschabel/m2v-e5-large-edu/resolve/main/tokenizer.json
- Command line
-
hf download hf://JanSchachtschabel/m2v-e5-large-edu/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/JanSchachtschabel/m2v-e5-large-edu/resolve/main/tokenizer.json
15.2 MB
- Xet hash:
- d7ef00eee9c62071b811d3ab81cec2f7bc339c02b62aaded382d46bccff918cc
- Size of remote file:
- 15.2 MB
- SHA256:
- aed6898868c666a674372b1f2e684621e9b22a2893346427e745b413a18a0cfe
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