Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:4091
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use adriansanz/ST-tramits-VIL-001-5ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adriansanz/ST-tramits-VIL-001-5ep with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adriansanz/ST-tramits-VIL-001-5ep") sentences = [ "Aquest tràmit permet formalitzar la matrícula a les llars d’infants municipals, si l'infant ha estat admès al període de preinscripcions.", "Quin és el tràmit que es realitza abans de la matrícula?", "Quin és el propòsit de l'Ajuntament en aquest tràmit?", "Què es pot fer amb les exclusions indegudes al Cens Electoral?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |