Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use srsawant34/ProTopic-niter3-bs64-e32-ntopics10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srsawant34/ProTopic-niter3-bs64-e32-ntopics10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srsawant34/ProTopic-niter3-bs64-e32-ntopics10") 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 srsawant34/ProTopic-niter3-bs64-e32-ntopics10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("srsawant34/ProTopic-niter3-bs64-e32-ntopics10") model = AutoModel.from_pretrained("srsawant34/ProTopic-niter3-bs64-e32-ntopics10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 762e0f63ad23ac6bb908ebf21e8e35571f79ee12f41bd3a893bfeee2642dccee
- Size of remote file:
- 4.79 kB
- SHA256:
- 59691d62067580cb96432d7835b9a22fa0cdbf9d683d1ce4d96a99344613e85b
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