Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:50
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use akshitguptafintek24/exxon-semantic-search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use akshitguptafintek24/exxon-semantic-search with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("akshitguptafintek24/exxon-semantic-search") sentences = [ "Freepoint Commodity services venture", "DUPLI OF 823707 BITUBULK SRL VESSEL", "Freepoint Commodities LLC", "AUGUSTA ENERGY DMCC" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 710 Bytes
7379c1a | 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 | {
"_name_or_path": "C:\\Pycharm\\Projects\\nlp\\src\\models\\nlp\\notebooks\\semanticSearch\\exxon\\models\\all-mpnet-base-v2\\final",
"architectures": [
"MPNetModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "mpnet",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"relative_attention_num_buckets": 32,
"torch_dtype": "float32",
"transformers_version": "4.44.2",
"vocab_size": 30527
}
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