Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:1084
loss:ContrastiveTensionLossInBatchNegatives
text-embeddings-inference
Instructions to use andreyunic23/beds_step4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use andreyunic23/beds_step4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("andreyunic23/beds_step4") sentences = [ "Heat and smoke detectors should trigger an alarm and extinguishing systems.", "Laboratory Operators must be trained to manipulate energetic materials correctly.", "Loss or damage to test environments.", "Heat and smoke detectors should trigger an alarm and extinguishing systems." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "sentence-transformers/all-mpnet-base-v2", | |
| "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": "4.45.2", | |
| "vocab_size": 30527 | |
| } | |