Sentence Similarity
sentence-transformers
Safetensors
multilingual
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:1765
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use RamsesDIIP/me5-large-construction-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RamsesDIIP/me5-large-construction-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RamsesDIIP/me5-large-construction-v2") sentences = [ "Pavimento de piedra calcárea nacional serrada y sin pulir, precio alto, de 40 mm de espesor con arista viva en los cuatro bordes 1251 a 2500 cm2, colocada a pique de maceta con mortero cemento 1:6", "Bordillo de hormigón recto con canaleta, de una sola capa, dimensiones 40x35 cm, instalado sobre una base de hormigón no estructural de 25 a 30 cm de altura y sellado con mortero.", "Pavimento de piedra caliza nacional, sin pulir y con un grosor de 40 mm, con bordes afilados, en un rango de 1251 a 2500 cm2, instalado en macetas utilizando mortero de cemento en una proporción de 1:6, a un precio elevado.", "Pavimento de cerámica esmaltada de importación, precio bajo, de 10 mm de espesor con bordes redondeados en los cuatro lados 500 a 1000 cm2, instalada en superficie plana con adhesivo flexible." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "intfloat/multilingual-e5-large", | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |