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 pytorch_model.bin from louisbetsch/new-classification-model: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/louisbetsch/new-classification-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://louisbetsch/new-classification-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/louisbetsch/new-classification-model/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 713871c1926980eea342631d7cbd5612d5fc1c76058edad8623dbf7af780cf9f
- Size of remote file:
- 1.11 GB
- SHA256:
- d8df0f9be9eb8d00ed52b223e173614f3216d4d96827fb352c32ad2b88baa522
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