Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
Transformers.js
English
bert
fill-mask
sentence-similarity
mteb
custom_code
text-embeddings-inference
Instructions to use michaelfeil/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 michaelfeil/jina-embeddings-v2-base-code with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaelfeil/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 michaelfeil/jina-embeddings-v2-base-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="michaelfeil/jina-embeddings-v2-base-code", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("michaelfeil/jina-embeddings-v2-base-code", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("michaelfeil/jina-embeddings-v2-base-code", trust_remote_code=True, device_map="auto") - Transformers.js
How to use michaelfeil/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', 'michaelfeil/jina-embeddings-v2-base-code'); - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "jinaai/jina-bert-v2-qk-post-norm", | |
| "architectures": [ | |
| "JinaBertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "attn_implementation": "torch", | |
| "auto_map": { | |
| "AutoConfig": "jinaai/jina-bert-v2-qk-post-norm--configuration_bert.JinaBertConfig", | |
| "AutoModel": "jinaai/jina-bert-v2-qk-post-norm--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" | |
| }, | |
| "classifier_dropout": null, | |
| "emb_pooler": "mean", | |
| "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": 1024, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "alibi", | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.35.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 61056 | |
| } | |