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") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- 1_Pooling
- checkpoint-1148
- checkpoint-1435
- checkpoint-1722
- checkpoint-2009
- checkpoint-2296
- checkpoint-287
- checkpoint-574
- checkpoint-8323
- checkpoint-861
- checkpoint-8610
- checkpoint-8897
- checkpoint-9184
- runs
- 1.52 kB
- 3.69 kB
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- 90.9 MB xet
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- 1.24 kB
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