Sentence Similarity
sentence-transformers
Safetensors
clip
feature-extraction
Generated from Trainer
dataset_size:12
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use machinev/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use machinev/model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("machinev/model") sentences = [ "the main power cable is connected with LPT ", "the main power cable is connected with LPT ", "the main power cable is connected with LPT ", "/content/sample_data/images/LPT (2).jpeg" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 770 Bytes
1371f59 | 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 | {
"_name_or_path": "/root/.cache/huggingface/hub/models--sentence-transformers--clip-ViT-L-14/snapshots/3b12140ad0f9750045e404f187cfccd04bcaf250/0_CLIPModel",
"architectures": [
"CLIPModel"
],
"initializer_factor": 1.0,
"logit_scale_init_value": 2.6592,
"model_type": "clip",
"projection_dim": 768,
"text_config": {
"dropout": 0.0,
"hidden_size": 768,
"intermediate_size": 3072,
"model_type": "clip_text_model",
"num_attention_heads": 12
},
"torch_dtype": "float32",
"transformers_version": "4.48.3",
"vision_config": {
"dropout": 0.0,
"hidden_size": 1024,
"intermediate_size": 4096,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"patch_size": 14
}
}
|