Sentence Similarity
sentence-transformers
ONNX
English
code
bert
code-search
embeddings
LoRA
E5
cqs
Eval Results
text-embeddings-inference
Instructions to use jamie8johnson/e5-base-v2-code-search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jamie8johnson/e5-base-v2-code-search with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jamie8johnson/e5-base-v2-code-search") 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
feat: LoRA v7 — update tokenizer_config.json
Browse files- tokenizer_config.json +0 -7
tokenizer_config.json
CHANGED
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@@ -46,18 +46,11 @@
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"do_lower_case": true,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"max_length": 512,
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"model_max_length": 512,
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"pad_to_multiple_of": null,
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"pad_token": "[PAD]",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"sep_token": "[SEP]",
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"stride": 0,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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}
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"do_lower_case": true,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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