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") - 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 | { | |
| "model_type": "model2vec", | |
| "architectures": [ | |
| "StaticModel" | |
| ], | |
| "tokenizer_name": "intfloat/multilingual-e5-large", | |
| "apply_pca": 512, | |
| "sif_coefficient": 0.0001, | |
| "hidden_dim": 512, | |
| "seq_length": 1000000, | |
| "normalize": true, | |
| "pooling": "mean", | |
| "embedding_dtype": "float32" | |
| } |