Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:86807
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use deedcon/bi-encoder-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use deedcon/bi-encoder-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("deedcon/bi-encoder-v2") sentences = [ "[CLS] [KNOWLEDGE] NLTK [CTX] Programmierung einer pre-processing CI/CD Pipeline zur automatisierten Verarbeitung von Newsartikeln von Tamil zu Englisch, in Python unter Verwendung von NLTK und SpaCy\n [SEP]", "[CLS] [KNOWLEDGE] SPOC [CTX] Single Point of Contact (SPOC) für die Business Units (Schnittstellenfunktion zu anderen Teilprojekten und Teams)\n [SEP]", "[CLS] [KNOWLEDGE] DIN 50001 [CTX] Mitarbeit zur Einführung eines Energiemanagementsystems nach DIN 50001\n [SEP]", "[CLS] [KNOWLEDGE] Risikomanagement [CTX] - Risikomanagement\n [SEP]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 666 Bytes
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"_name_or_path": "models/bi-encoder_v2/final",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.49.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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