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: 446 Bytes
5ba9942 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"handle_chinese_chars": true,
"mask_token": "[MASK]",
"model_max_len": 512,
"name_or_path": "pile-of-law/legalbert-large-1.7M-2",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": null,
"strip_accents": true,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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