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--- |
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license: apache-2.0 |
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language: |
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- en |
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- fa |
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- ar |
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inference: true |
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base_model: |
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- jinaai/jina-embeddings-v3 |
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pipeline_tag: feature-extraction |
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tags: |
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- Embedding |
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library_name: transformers |
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--- |
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# Jina-embedding-v3 |
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## Overview |
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This repository uses the original [jinai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) embedding model, without modification or fine-tuning. |
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A versatile sentence embedding model suitable for a wide range of natural language processing tasks |
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you can find the specific tasks in the *Tasks* section |
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*The only difference is that you can use HF inference from this page, which is unavailable in the official page of jinaai.* |
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--- |
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## Inference Access |
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You can use this model for inference directly from this page using Hugging Face’s serverless inference API. |
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This is not possible via the official Jina AI model page. Here’s an example of how to get sentence embeddings using the Hugging Face Inference API: |
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you can use the function below for inference (it applies mean-pooling and normalization too and returns embeddings ready to store in your vector databases) |
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```python |
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from huggingface_hub import InferenceClient |
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import numpy as np |
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def get_HF_embeddings(text, api_key, model, mean_pool=True, l2_normalize=True): |
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""" |
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Fetches embeddings from HuggingFace serverless Inference API for a single string or a list of strings. |
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Args: |
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text (str or list): Input text or list of texts. |
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api_key (str): HuggingFace API key. |
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model (str): Model repo. |
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mean_pool (bool): If True, mean-pool the output. |
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l2_normalize (bool): If True, L2 normalize the output. |
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Returns: |
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np.ndarray: Embedding(s) as numpy array(s). |
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""" |
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client = InferenceClient(api_key=api_key) |
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if isinstance(text, str): |
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texts = [text] |
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single_input = True |
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else: |
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texts = text |
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single_input = False |
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result = client.feature_extraction( |
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text=texts, |
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model=model |
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) |
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if mean_pool: |
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embeddings = [np.mean(r, axis=0) for r in result] |
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if l2_normalize: |
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embeddings = [ |
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e / np.linalg.norm(e) if np.linalg.norm(e) > 0 else e for e in embeddings] |
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else: |
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embeddings = [r for r in result] |
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if single_input: |
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return embeddings[0] |
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return np.array(embeddings) |
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``` |
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--- |
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## Tasks |
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- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE positional encoding. |
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- **Task-Specific Embeddings:** Choose the `task` parameter for different application needs: |
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- `retrieval.query` – For query embeddings in asymmetric retrieval |
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- `retrieval.passage` – For passage embeddings in asymmetric retrieval |
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- `separation` – For clustering and re-ranking |
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- `classification` – For classification tasks |
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- `text-matching` – For symmetric similarity tasks (e.g., STS) |
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- **Matryoshka Embeddings:** Flexible embedding sizes (32, 64, 128, 256, 512, 768, 1024 dimensions). |
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--- |
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## How to Use |
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You can use this model with either the `transformers` or the `sentence-transformers` library. |
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For **full feature support** (using task-specific LoRA heads and flexible embedding sizes), it is recommended to use the `sentence-transformers` library. |
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### Using sentence-transformers (Recommended) |
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```python |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer("Sajjad313/jina-embedding-v3", trust_remote_code=True) |
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# Task-specific usage: Retrieval query embedding |
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query_embedding = model.encode( |
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["What is the weather like in Berlin today?"], |
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task="retrieval.query" |
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) |
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``` |
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### Using transformers |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3") |
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model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3") |
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inputs = tokenizer( |
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"What is the weather like in Berlin today?", |
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return_tensors="pt", padding=True, truncation=True |
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) |
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outputs = model(**inputs) |
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embedding = outputs.last_hidden_state[:, 0] # CLS token embedding |
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``` |
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> Note: Using the `transformers` library gives you basic access to the model’s output, but for full task-specific capabilities, use `sentence-transformers`. |
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--- |
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