Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
Generated from Trainer
dataset_size:9741
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Matjac5/MNLP_M2_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matjac5/MNLP_M2_rag_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/MNLP_M2_rag_model") sentences = [ "What's one characteristic that separates a person from a stuffed dummy?", "accidental", "experience pain", "wrong place" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 720 Bytes
a16981f | 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 30 31 | {
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"Qwen3Model"
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"attention_dropout": 0.0,
"bos_token_id": 151643,
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"head_dim": 128,
"hidden_act": "silu",
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"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 32768,
"max_window_layers": 28,
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"num_hidden_layers": 28,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
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"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"torch_dtype": "float32",
"transformers_version": "4.52.2",
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}
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