Feature Extraction
sentence-transformers
Safetensors
ONNX
code
bert
code-retrieval
issue-localization
custom_code
text-embeddings-inference
Instructions to use codeusmorbid/jina-v2-code-ft2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codeusmorbid/jina-v2-code-ft2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codeusmorbid/jina-v2-code-ft2", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 1,120 Bytes
2acb16c | 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 30 31 32 33 34 35 36 37 38 | {
"architectures": [
"JinaBertModel"
],
"attention_probs_dropout_prob": 0.0,
"attn_implementation": null,
"auto_map": {
"AutoConfig": "configuration_bert.JinaBertConfig",
"AutoModel": "modeling_bert.JinaBertModel",
"AutoModelForMaskedLM": "jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertForMaskedLM",
"AutoModelForSequenceClassification": "jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertForSequenceClassification"
},
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"emb_pooler": "mean",
"eos_token_id": 2,
"feed_forward_type": "geglu",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 8192,
"model_max_length": 8192,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "alibi",
"transformers_version": "4.57.6",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 61056
}
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