T5 Large for Text Aggregation
Model description
This is a T5 Large fine-tuned for crowdsourced text aggregation tasks. The model takes multiple performers' responses and yields a single aggregated response. This approach was introduced for the first time during VLDB 2021 Crowd Science Challenge and originally implemented at the second-place competitor's GitHub. The paper describing this model was presented at the 2nd Crowd Science Workshop.
How to use
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig
mname = "toloka/t5-large-for-text-aggregation"
tokenizer = AutoTokenizer.from_pretrained(mname)
model = AutoModelForSeq2SeqLM.from_pretrained(mname)
input = "samplee text | sampl text | sample textt"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # sample text
Training data
Pretrained weights were taken from the original T5 Large model by Google. For more details on the T5 architecture and training procedure see https://arxiv.org/abs/1910.10683
Model was fine-tuned on train-clean, dev-clean and dev-other parts of the CrowdSpeech dataset that was introduced in [our paper](https://openreview.net/forum?id=3_hgF1NAXU7&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DNeurIPS.cc%2F2021%2FTrack%2FDatasets_and_Benchmarks%2FRound1%2FAuthors%23your-submissions).
Training procedure
The model was fine-tuned for eight epochs directly following the HuggingFace summarization training example.
Eval results
| Dataset | Split | WER |
|---|---|---|
| CrowdSpeech | test-clean | 4.99 |
| CrowdSpeech | test-other | 10.61 |
BibTeX entry and citation info
@inproceedings{Pletenev:21,
author = {Pletenev, Sergey},
title = {{Noisy Text Sequences Aggregation as a Summarization Subtask}},
year = {2021},
booktitle = {Proceedings of the 2nd Crowd Science Workshop: Trust, Ethics, and Excellence in Crowdsourced Data Management at Scale},
pages = {15--20},
address = {Copenhagen, Denmark},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-2932/short2.pdf},
language = {english},
}
@misc{pavlichenko2021vox,
title={Vox Populi, Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription},
author={Nikita Pavlichenko and Ivan Stelmakh and Dmitry Ustalov},
year={2021},
eprint={2107.01091},
archivePrefix={arXiv},
primaryClass={cs.SD}
}