Chime-SMS-2M: training data credits

Luke Steuber trained this model on English text from the five sources below. The text was
filtered, normalized and deduplicated, and the model holds weights and a vocabulary, not a copy
of the text. The source authors have not endorsed this model. The sources are provided as-is,
without warranties, under their own licenses.

1. NUS SMS Corpus, English portion, release 2015.03.09.
   Tao Chen and Min-Yen Kan, "Creating a Live, Public Short Message Service Corpus: The NUS SMS
   Corpus", Language Resources and Evaluation 47(2), 2013.
   https://doi.org/10.25540/WVM0-4RNX
   Release mirror: kite1988/nus-sms-corpus, commit bf0a365170d324e61e4025edf363d400f1edd1a6.
   English messages only. Placeholder tags from the corpus's own anonymization (such as <EMAIL>)
   were dropped from the text.
   License: Creative Commons Attribution 4.0 International (CC BY 4.0).
   https://creativecommons.org/licenses/by/4.0/

2. Taskmaster-1 written self-dialogs, TM-1-2019/self-dialogs.json.
   Byrne et al., "Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset", EMNLP 2019.
   Copyright Google LLC.
   https://github.com/google-research-datasets/Taskmaster
   Commit d92cb6af3005f1dc09c39e75e7daf4a04905e00b.
   User utterances only.
   License: Creative Commons Attribution 4.0 International (CC BY 4.0).
   https://creativecommons.org/licenses/by/4.0/

3. OpenAssistant Conversations 2 (OpenAssistant/oasst2).
   Köpf et al., "OpenAssistant Conversations: Democratizing Large Language Model Alignment",
   NeurIPS 2023 Datasets and Benchmarks, arXiv:2304.07327.
   https://huggingface.co/datasets/OpenAssistant/oasst2
   Revision 179dd21fc55192153d94adb0e0ce8f69e222bf75.
   English prompts written by people; assistant turns were not used.
   License: Apache License 2.0. A copy is in Apache-2.0.txt.
   https://www.apache.org/licenses/LICENSE-2.0

4. Taskmaster-3 (TicketTalk) self-dialogs, TM-3-2020/data.
   Byrne et al., "TicketTalk: Toward human-level performance with end-to-end, transaction-based
   dialog systems", ACL 2021. Copyright Google LLC.
   https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020
   Commit d92cb6af3005f1dc09c39e75e7daf4a04905e00b.
   Customer utterances only.
   License: Creative Commons Attribution 4.0 International (CC BY 4.0).
   https://creativecommons.org/licenses/by/4.0/

5. SODA (allenai/soda).
   Kim et al., "SODA: Million-scale Dialogue Distillation with Social Commonsense
   Contextualization", arXiv:2212.10465.
   https://huggingface.co/datasets/allenai/soda
   Revision fdc848ab0183208ea7808206c91c724414d0a071.
   Both speakers' turns. These dialogues were generated by GPT-3.5 (InstructGPT).
   License: Creative Commons Attribution 4.0 International (CC BY 4.0).
   https://creativecommons.org/licenses/by/4.0/

Vocabulary selection also used a word-frequency list derived from hermitdave/FrequencyWords
(OpenSubtitles 2018 English), https://github.com/hermitdave/FrequencyWords, licensed under
Creative Commons Attribution-ShareAlike 4.0 International. It shaped which words are in the
vocabulary; it is not training text. The emoji tokens come from a word-to-emoji list written
for LocalType.