ai-forever/FRIDA
feature-extraction model by ai-forever
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Model card & documentation
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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 our article (RU). The model's results are presented on the MTEB and rusBEIR leaderboards. 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…
Read the full model card ↗ · Preview checked 2026-10-03T06:45:35.426Z
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Model overview
ai-forever/FRIDA is a feature-extraction model repository published by ai-forever on Hugging Face. The source reports the sentence-transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- feature-extraction
- Library
- sentence-transformers
- Recent downloads (30 days)
- 110,156
- Cumulative likes
- 150
- Hugging Face trending score
- 2
- Reported safetensors parameters
- 823,401,216
- Architecture
- T5EncoderModel
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2024-12-26T15:07:35.000Z
- Last modified
- 2026-08-11T16:34:54.000Z
Compatibility and lineage
Reported languages: ru, en
Reported base models: ai-forever/FRED-T5-1.7B
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-03T06:40:34.990Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.