Datasets:
Improve metadata and add links to paper and code
#1
by
nielsr HF Staff - opened
README.md
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---
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dataset_info:
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features:
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- name: timestamp
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data_files:
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- split: train
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path: data/train-*
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language:
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- en
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tags:
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- Blockchain
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- DLT
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- Cryptocurrencies
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- Bitcoin
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- Ethereum
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- Crypto
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pretty_name: Distributed Ledger Technology (DLT) / Blockchain Tweets
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size_categories:
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- 10M<n<100M
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---
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# DLT-Tweets
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## Dataset Description
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### Dataset Summary
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DLT-Tweets is a large-scale corpus of social media posts related to Distributed Ledger Technology (DLT). This dataset is part of the larger DLT-Corpus collection, designed to support NLP research, social computing studies, and public discourse analysis in the DLT domain.
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The dataset contains **22.03 million social media documents** with **1,120 million tokens** (1.12 billion tokens), spanning posts from **2013 to mid-2023**. All documents are in English and have been processed to protect user privacy.
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---
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language:
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- en
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license: cc-by-nc-4.0
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size_categories:
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- 10M<n<100M
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task_categories:
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- text-generation
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pretty_name: Distributed Ledger Technology (DLT) / Blockchain Tweets
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dataset_info:
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features:
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- name: timestamp
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data_files:
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- split: train
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path: data/train-*
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tags:
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- Blockchain
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- DLT
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- Cryptocurrencies
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- Bitcoin
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- Ethereum
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- Crypto
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---
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# DLT-Tweets
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<p align="center">
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<a href="https://huggingface.co/papers/2602.22045"><b>[Paper]</b></a> •
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<a href="https://github.com/dlt-science/DLT-Corpus"><b>[Code]</b></a>
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</p>
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## Dataset Description
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### Dataset Summary
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DLT-Tweets is a large-scale corpus of social media posts related to Distributed Ledger Technology (DLT). This dataset is part of the larger DLT-Corpus collection, designed to support NLP research, social computing studies, and public discourse analysis in the DLT domain. It was introduced in the paper [DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain](https://huggingface.co/papers/2602.22045).
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The dataset contains **22.03 million social media documents** with **1,120 million tokens** (1.12 billion tokens), spanning posts from **2013 to mid-2023**. All documents are in English and have been processed to protect user privacy.
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