Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use louisbetsch/new-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use louisbetsch/new-classification-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("louisbetsch/new-classification-model") 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 louisbetsch/new-classification-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("louisbetsch/new-classification-model") model = AutoModel.from_pretrained("louisbetsch/new-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from louisbetsch/new-classification-model: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/louisbetsch/new-classification-model/resolve/main/tokenizer.json
- Command line
-
hf download hf://louisbetsch/new-classification-model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/louisbetsch/new-classification-model/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 145c6051ee3e5283d98829a94bda6b83ca2cc07ea00a4593c15b8d504a563bca
- Size of remote file:
- 17.1 MB
- SHA256:
- b6ec84c665500787d777fac7dd8a8d40813f0db9fa7f4087f40b6ea60865cc0d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.