Visual Document Retrieval
Transformers
Safetensors
ColPali
sentence-transformers
multilingual
vidore
multimodal-embedding
multilingual-embedding
Text-to-Visual Document (T→VD) retrieval
feature-extraction
sentence-similarity
mteb
Instructions to use KrishnaIndukuri/IRMSEmbeddingsV4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KrishnaIndukuri/IRMSEmbeddingsV4 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KrishnaIndukuri/IRMSEmbeddingsV4", device_map="auto") - ColPali
How to use KrishnaIndukuri/IRMSEmbeddingsV4 with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- sentence-transformers
How to use KrishnaIndukuri/IRMSEmbeddingsV4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KrishnaIndukuri/IRMSEmbeddingsV4") 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
- Xet hash:
- 8e4bf50ae3ff62b9cd3691303065620e8fe5c1029b945a3805e6b90939e3d0e3
- Size of remote file:
- 1.05 kB
- SHA256:
- 94174d7176c52a7192f96fc34eb2cf23c7c2059d63cdbfadca1586ba89731fb7
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