Sentence Similarity
sentence-transformers
Safetensors
Transformers
bidirlm_omni
mteb
embedding
bidirectional
custom_code
Instructions to use BidirLM/BidirLM-Omni-2.5B-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BidirLM/BidirLM-Omni-2.5B-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BidirLM/BidirLM-Omni-2.5B-Embedding", trust_remote_code=True) 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] - Transformers
How to use BidirLM/BidirLM-Omni-2.5B-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BidirLM/BidirLM-Omni-2.5B-Embedding", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 709 Bytes
4d8a7d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"auto_map": {
"AutoProcessor": "processing_bidirlm_omni.BidirLMOmniProcessor"
},
"chunk_length": 30,
"dither": 0.0,
"feature_extractor_type": "WhisperFeatureExtractor",
"feature_size": 128,
"hop_length": 160,
"image_mean": [0.5, 0.5, 0.5],
"image_processor_type": "Qwen2VLImageProcessorFast",
"image_std": [0.5, 0.5, 0.5],
"merge_size": 2,
"n_fft": 400,
"n_samples": 480000,
"nb_max_frames": 3000,
"padding_side": "right",
"padding_value": 0.0,
"patch_size": 16,
"processor_class": "BidirLMOmniProcessor",
"return_attention_mask": true,
"sampling_rate": 16000,
"size": {
"longest_edge": 1048576,
"shortest_edge": 65536
},
"temporal_patch_size": 2
}
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