Sentence Similarity
Transformers
PyTorch
ONNX
sentence-transformers
Hindi
bert
feature-extraction
miniMiracle
passage-retrieval
knowledge-distillation
middle-training
text-embeddings-inference
Instructions to use prithivida/miniDense_hindi_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivida/miniDense_hindi_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("prithivida/miniDense_hindi_v1") model = AutoModel.from_pretrained("prithivida/miniDense_hindi_v1", device_map="auto") - sentence-transformers
How to use prithivida/miniDense_hindi_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prithivida/miniDense_hindi_v1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from prithivida/miniDense_hindi_v1: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/prithivida/miniDense_hindi_v1/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://prithivida/miniDense_hindi_v1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/prithivida/miniDense_hindi_v1/resolve/main/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |