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 model_head.pkl from louisbetsch/new-classification-model: direct link, hf CLI and curl.
- Browser
- Download file 6.93 kB
-
https://huggingface.co/louisbetsch/new-classification-model/resolve/main/model_head.pkl
- Command line
-
hf download hf://louisbetsch/new-classification-model/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/louisbetsch/new-classification-model/resolve/main/model_head.pkl
6.93 kB
- Xet hash:
- 01939b71d2a08ea21bdc6b93240f75c07108e78d752ba972e50fedd3dbcfc09b
- Size of remote file:
- 6.93 kB
- SHA256:
- 499d45d562a1c0e0b248f3129232c269cfb147059cafe0260b83284f49077318
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