Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use Charul/use-case-classification-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Charul/use-case-classification-final with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Charul/use-case-classification-final") 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 Charul/use-case-classification-final with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Charul/use-case-classification-final") model = AutoModel.from_pretrained("Charul/use-case-classification-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6db2aef892abf9cc79b9f28628a2c82b4cfcf06310f0790ec66f7067ff43e062
- Size of remote file:
- 1.11 GB
- SHA256:
- 08eaf63cb3fd75d55d493acf54b8dda86e90ae2be6a457eb8d1c6463cf670d19
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