Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
Transformers.js
English
bert
fill-mask
sentence-similarity
mteb
custom_code
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use jinaai/jina-embeddings-v2-base-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jinaai/jina-embeddings-v2-base-code with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v2-base-code", 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] - Transformers
How to use jinaai/jina-embeddings-v2-base-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinaai/jina-embeddings-v2-base-code", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-code", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("jinaai/jina-embeddings-v2-base-code", trust_remote_code=True, device_map="auto") - Transformers.js
How to use jinaai/jina-embeddings-v2-base-code with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'jinaai/jina-embeddings-v2-base-code'); - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +13 -14
config.json
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{
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"_name_or_path": "jinaai/jina-bert-
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"architectures": [
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"JinaBertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.0,
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"attn_implementation": "torch",
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"auto_map": {
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"AutoConfig": "jinaai/jina-bert-
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"
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"
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"AutoModelForSequenceClassification": "jinaai/jina-bert-
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},
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"classifier_dropout": null,
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"emb_pooler": "mean",
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"feed_forward_type": "geglu",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 8192,
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"model_max_length": 8192,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "alibi",
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"torch_dtype": "
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"transformers_version": "4.
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size":
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{
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"_name_or_path": "jinaai/jina-bert-implementation",
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"model_max_length": 8192,
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"architectures": [
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"JinaBertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.0,
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"auto_map": {
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"AutoConfig": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig",
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"AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
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"AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
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"AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
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},
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 8192,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "alibi",
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"torch_dtype": "float32",
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"transformers_version": "4.26.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30528,
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"feed_forward_type": "geglu",
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"emb_pooler": "mean"
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}
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