Instructions to use rasgaard/m2v-dfm-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use rasgaard/m2v-dfm-large with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("rasgaard/m2v-dfm-large") - sentence-transformers
How to use rasgaard/m2v-dfm-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rasgaard/m2v-dfm-large") 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
File size: 338 Bytes
4884e69 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "model2vec",
"architectures": [
"StaticModel"
],
"tokenizer_name": "KennethEnevoldsen/dfm-sentence-encoder-large",
"apply_pca": 256,
"sif_coefficient": 0.0001,
"hidden_dim": 256,
"seq_length": 1000000,
"normalize": true,
"pooling": "mean",
"embedding_dtype": "float32"
} |