Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
mitre_ttps
security
adversarial-threat-annotation
text-embeddings-inference
Instructions to use QCRI/SentSecBert_10k_AllDataSplit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use QCRI/SentSecBert_10k_AllDataSplit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("QCRI/SentSecBert_10k_AllDataSplit") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 669 Bytes
014188d | 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 | {
"_name_or_path": "../../models/SentSecBert_10k/",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 514,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.28.1",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 52000
}
|