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Adds support for Stella_en_v5 embedding model - 1.5B variant (#2551)
* Stella_en_1.5B_v5 * Separated creation. This is a critical step for numerical accuracy and would be documented in the readme * EmbedDim would require clone and copy * WIP: example * Examples added * a litte more in README
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# candle-stella-en-v5: Implementation of [stella_en_1.5B_v5](https://huggingface.co/dunzhang/stella_en_1.5B_v5) embedding model | ||
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As of 7th Oct 2024, *Stella_en_1.5B_v5* is one of the top ranking model on `retrieval` and `reranking` tasks in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. | ||
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[Model card](https://huggingface.co/dunzhang/stella_en_1.5B_v5) on the HuggingFace Hub. | ||
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## Running the example | ||
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Stella_en_1.5B_v5 is used to generate text embeddings embeddings for a prompt. The model weights | ||
are downloaded from the hub on the first run. | ||
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```bash | ||
$ cargo run --example stella-en-v5 --release -- --query "What are safetensors?" | ||
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> [[ 0.3905, -0.0130, 0.2072, ..., -0.1100, -0.0086, 0.6002]] | ||
> Tensor[[1, 1024], f32] | ||
``` | ||
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Stella_en_1.5B_v5 is trained by [MRL](https://arxiv.org/abs/2205.13147) enabling multiple embedding dimensions. | ||
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The following reproduces the example in the [model card](https://huggingface.co/dunzhang/stella_en_1.5B_v5) for a retrieval task (s2p). The sample queries and docs are hardcoded in the example. | ||
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```bash | ||
$ cargo run --example stella-en-v5 --release --features <metal | cuda> | ||
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> | ||
> Score: 0.8178786 | ||
> Query: What are some ways to reduce stress? | ||
> Answer: There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending | ||
> time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent | ||
> stress from building up. | ||
> | ||
> | ||
> Score: 0.7853528 | ||
> Query: What are the benefits of drinking green tea? | ||
> Answer: Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage | ||
> caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types > | ||
> of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties. | ||
> | ||
``` | ||
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## Supported options: | ||
- `Stella_en_15B_v5` supports 256, 768, 1024, 2048, 4096, 6144 and 8192 embedding dimensions (though the model card mentions 512, I couldn't find weights for the same). In the example run this is supported with `--embed-dim` option. E.g. `... --embed-dim 4096`. Defaults to `1024`. | ||
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- As per the [model card](https://huggingface.co/dunzhang/stella_en_1.5B_v5), the model has been primarily trained on `s2s` (similarity) and `s2p` (retrieval) tasks. These require a slightly different `query` preprocessing (a different prompt template for each). In this example this is enabled though `--task` option. |
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