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
Download tokenizer_config.json from prodm93/lb_large_base: direct link, hf CLI and curl.
- Browser
- Download file 446 Bytes
-
https://huggingface.co/prodm93/lb_large_base/resolve/refs%2Fpr%2F1/tokenizer_config.json
- Command line
-
hf download hf://prodm93/lb_large_base@refs/pr/1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/prodm93/lb_large_base/resolve/refs%2Fpr%2F1/tokenizer_config.json
446 Bytes
| { | |
| "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]" | |
| } | |