Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:121
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use qygoh/ilo-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use qygoh/ilo-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("qygoh/ilo-embedding-model") sentences = [ "Kasano a mausar ti online a panag-apply iti tulong dagiti Golden Citizens?", "Ania dagiti addang a mangaplikar iti tulong kadagiti umili babaen ti online system?", "Ania ti pamay-an a nalaklaka a mangasaba iti tulong kadagiti umili?", "Ania dagiti addang a mabalin nga aramiden tapno maaddaan iti status ti binulan a sueldo iti agdama a tawen?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 726 Bytes
0af5c22 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"_name_or_path": "BAAI/bge-m3",
"architectures": [
"XLMRobertaModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
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"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 8194,
"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": "4.47.1",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
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