nomi
Datasets
All datasets matching “nomi”cornstack-python-v1
CoRNStack Python Dataset
The CoRNStack Dataset, accepted to ICLR 2025, is a large-scale high quality training dataset specifically for code retrieval across multiple
programming languages. This dataset comprises of <query, positive, negative> triplets used to train nomic-embed-code,
CodeRankEmbed, and CodeRankLLM.
CoRNStack Dataset Curation
Starting with the deduplicated Stackv2, we create text-code pairs from function docstrings and respective code. We filtered out… See the full description on the dataset page: https://huggingface.co/datasets/nomic-ai/cornstack-python-v1.bert-128-grouped
Dataset Card for "bert-128-grouped"
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nomic-embed-unsupervised-dataWeakly Supervised Contrastive Training data for Text Embedding models used in Nomic Embed models
Training
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
We train our embedder using a multi-stage training pipeline. Starting from a long-context BERT model,
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora… See the full description on the dataset page: https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data.cohere-wiki-sbert
Dataset Card for "cohere-wiki-sbert"
More Information needed
cornstack-java-v1
CoRNStack Python Dataset
The CoRNStack Dataset, accepted to ICLR 2025, is a large-scale high quality training dataset specifically for code retrieval across multiple
programming languages. This dataset comprises of <query, positive, negative> triplets used to train nomic-embed-code,
CodeRankEmbed, and CodeRankLLM.
CoRNStack Dataset Curation
Starting with the deduplicated Stackv2, we create text-code pairs from function docstrings and respective code. We filtered out… See the full description on the dataset page: https://huggingface.co/datasets/nomic-ai/cornstack-java-v1.nomic-bert-2048-pretraining-data
Dataset Card for "bert-pretokenized-2048-wiki-2023"
More Information needed