Sentence Similarity
sentence-transformers
Safetensors
Slovak
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:137745
loss:CosineSimilarityLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use borsimnet/e5-sk-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use borsimnet/e5-sk-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("borsimnet/e5-sk-large") sentences = [ "Mor a epidémia sa očividne vymkli spod kontroly .", "Choroba bola nekontrolovateľná a ohrozovala všetok život .", "Tieto vylúčenia sú určené na iné cieľové skupiny ako obchodné štvrte .", "Autobus National Trust Tour odchádza každý deň o 9:00 z National Trust Information Centre ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "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", | |
| "transformers_version": "4.57.3", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 60002, | |
| "vocabtrimmer": { | |
| "mining_config": { | |
| "dataset": "ivykopal/fineweb2-slovak", | |
| "dataset_column": "text", | |
| "dataset_name": null, | |
| "dataset_split": "test", | |
| "language": "sk", | |
| "min_frequency": 1, | |
| "target_vocab_size": 60000 | |
| }, | |
| "stats": { | |
| "compression_rate_embedding": 24.00060799513604, | |
| "compression_rate_full": 65.25034383870307, | |
| "parameter_size_embedding/raw": 256002048, | |
| "parameter_size_embedding/trimmed": 61442048, | |
| "parameter_size_full/raw": 559890432, | |
| "parameter_size_full/trimmed": 365330432, | |
| "vocab_size/raw": 250002, | |
| "vocab_size/trimmed": 60002 | |
| } | |
| } | |
| } | |