geologist387/FRIDA-transformed

FRIDA transformed to onnx

Link to an original repository for this model. This onnx version has batching support

Transform FRIDA to onnx and tensorrt

This is a repository that contains FRIDA model in onnx (tensorrt upcoming) format. Python transformation scripts for this model are here too. onnx_to_trt.py is untested as of now

Model Card for FRIDA ONNX/TRT

FRIDA is a full-scale finetuned general text embedding model inspired by denoising architecture based on T5. The model is based on the encoder part of FRED-T5 model and continues research of text embedding models (ruMTEB, ru-en-RoSBERTa). It has been pre-trained on a Russian-English dataset and fine-tuned for improved performance on the target task.

For more model details please refer to this article (RU).

Usage

The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.

We use the following basic rules to choose a prefix:

• "search_query: " and "search_document: " prefixes are for answer or relevant paragraph retrieval
• "paraphrase: " prefix is for symmetric paraphrasing related tasks (STS, paraphrase mining, deduplication)
• "categorize: " prefix is for asymmetric matching of document title and body (e.g. news, scientific papers, social posts)
• "categorize_sentiment: " prefix is for any tasks that rely on sentiment features (e.g. hate, toxic, emotion)
• "categorize_topic: " prefix is intended for tasks where you need to group texts by topic
• "categorize_entailment: " prefix is for textual entailment task (NLI)

To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets.

Below are examples of texts encoding using the Transformers and SentenceTransformers libraries.

Authors

• SaluteDevices AI for B2C RnD Team.
• Artem Snegirev: HF profile, Github;
• Anna Maksimova HF profile;
• Aleksandr Abramov: HF profile, Github, Kaggle Competitions Master

Citation

@misc{TODO
}

Limitations

The model is designed to process texts in Russian, the quality in English is unknown. Maximum input text length is limited to 512 tokens.