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