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
| { | |
| "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 | |
| } | |