Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
bert
political-nlp
domain-adaptation
political-debates
text-embeddings-inference
Instructions to use ddore14/Sentence-RooseBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ddore14/Sentence-RooseBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ddore14/Sentence-RooseBERT") 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: 603 Bytes
6113166 | 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 | {
"architectures": [
"MPNetForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"dtype": "float16",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "mpnet",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"relative_attention_num_buckets": 32,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"use_cache": false,
"vocab_size": 30527
}
|