Text Ranking
sentence-transformers
PyTorch
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Safetensors
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English
bert
text-classification
text-embeddings-inference
Instructions to use cross-encoder/ms-marco-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/ms-marco-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Transformers
How to use cross-encoder/ms-marco-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/ms-marco-MiniLM-L6-v2") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/ms-marco-MiniLM-L6-v2") - Notebooks
- Google Colab
- Kaggle
TMT: dynamic graph attention beats Mamba on WikiText-2 at 48% compute — open source
#16
by vigneshwar234 - opened
TemporalMesh Transformer — benchmarked on this dataset
TMT achieves 29.4 PPL on WikiText-2 (vs 31.8 Mamba, 42.1 vanilla) and 36.1 on WikiText-103 at only 48% relative compute — 120M params, 3 seeds.
Five innovations: Mesh Attention (O(S·k) dynamic kNN), Temporal Decay, Adaptive Exit Gate, Dual-Stream FFN, EMA Memory Anchors.
📄 Paper: https://zenodo.org/records/20287390
💻 Code: https://github.com/vignesh2027/TemporalMesh-Transformer
🎮 Demo: https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo