Sentence Similarity
sentence-transformers
Safetensors
English
nomic_bert
legal
legal-retrieval
bns
indian-law
semantic-search
information-retrieval
custom_code
text-embeddings-inference
Instructions to use Ayushi054/CourtInsight-BNS-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ayushi054/CourtInsight-BNS-Encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ayushi054/CourtInsight-BNS-Encoder", trust_remote_code=True) 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
Remove unnecessary training checkpoint files
Browse files
checkpoint-1320/tokenizer_config.json
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"backend": "tokenizers",
|
| 3 |
-
"clean_up_tokenization_spaces": true,
|
| 4 |
-
"cls_token": "[CLS]",
|
| 5 |
-
"do_lower_case": true,
|
| 6 |
-
"is_local": false,
|
| 7 |
-
"local_files_only": false,
|
| 8 |
-
"mask_token": "[MASK]",
|
| 9 |
-
"model_max_length": 512,
|
| 10 |
-
"pad_token": "[PAD]",
|
| 11 |
-
"sep_token": "[SEP]",
|
| 12 |
-
"strip_accents": null,
|
| 13 |
-
"tokenize_chinese_chars": true,
|
| 14 |
-
"tokenizer_class": "BertTokenizer",
|
| 15 |
-
"unk_token": "[UNK]"
|
| 16 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|