Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use prodm93/lb_large_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use prodm93/lb_large_base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prodm93/lb_large_base") 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] - Transformers
How to use prodm93/lb_large_base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("prodm93/lb_large_base") model = AutoModel.from_pretrained("prodm93/lb_large_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 742 Bytes
5ba9942 | 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": "pile-of-law/legalbert-large-1.7M-2",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"directionality": "bidi",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"tokenizer_class": "BertTokenizer",
"torch_dtype": "float32",
"transformers_version": "4.24.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 32000
}
|