Feature Extraction
Transformers
English
retrieval
reasoning
embedding
BRIGHT
information-retrieval
Eval Results (legacy)
Instructions to use ForwardAILabs/MRE-T1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ForwardAILabs/MRE-T1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ForwardAILabs/MRE-T1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ForwardAILabs/MRE-T1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
fix: update Long comparison table with 7 verified single-model scores
Browse files
README.md
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@@ -121,6 +121,11 @@ MRE-T1 achieves state-of-the-art single-model performance on the [BRIGHT benchma
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| **MRE-T1** | **~4B** | **35.1** |
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| Google-Gecko-Text-Embedding-004 | — | 33.2 |
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| gte-Qwen1.5-7B-instruct | 7B | 27.8 |
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## Usage
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| **MRE-T1** | **~4B** | **35.1** |
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| Google-Gecko-Text-Embedding-004 | — | 33.2 |
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| gte-Qwen1.5-7B-instruct | 7B | 27.8 |
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| SFR-Embedding-Mistral | 7B | 26.0 |
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| e5-mistral-7b-instruct | 7B | 25.5 |
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| voyage-large-2-instruct | — | 24.6 |
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| Cohere-embed-english-v3.0 | — | 18.4 |
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| bge-large-en-v1.5 | 335M | 14.8 |
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## Usage
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