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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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This is all just for testing purposes. |
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## my-Jira-embedding-v3 |
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This is a sentence embedding model based on [jinai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3), fine-tuned for the task of embedding text related to Jira tickets. |
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This model is intended for use in tasks such as: |
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- Semantic search on Jira ticket descriptions and comments. |
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- Clustering of similar Jira tickets. |
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- Text similarity comparison for identifying duplicate or related issues. |
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## Key Features: |
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- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE. |
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- **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options: |
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- `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks |
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- `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks |
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- `separation`: Used for embeddings in clustering and re-ranking applications |
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- `classification`: Used for embeddings in classification tasks |
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- `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks |
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- **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application. |
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## Example: |
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```python |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True) |
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task = "retrieval.query" |
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embeddings = model.encode( |
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["What is the weather like in Berlin today?"], |
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task=task, |
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prompt_name=task, |
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) |
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``` |
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## Limitations |
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[Discuss any known limitations, e.g., performance on out-of-domain text, potential biases from the training data.] |
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## Training Data |
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This model was fine-tuned on a dataset of [Describe your dataset, e.g., a collection of anonymized Jira tickets]. |
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## How to Use |
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You can use this model with the `sentence-transformers` library: |
