tags:
- text-2-text-generation
- bart
Model Card for brio-cnndm-uncased
Model Details
Model Description
Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary. This assumption may lead to performance degradation during inference, where the model needs to compare several system-generated (candidate) summaries that have deviated from the reference summary. To address this problem, we propose a novel training paradigm which assumes a non-deterministic distribution so that different candidate summaries are assigned probability mass according to their quality.
Developed by: Yale LILY Lab
Shared by [Optional]: Yale LILY Lab
Model type: Text2Text Generation
Language(s) (NLP): More information needed
License: More information needed
Parent Model: BART
Resources for more information:
Uses
Direct Use
This model can be used for the task of Text2Text Generation
Downstream Use [Optional]
Further analysis also shows that our model can estimate probabilities of candidate summaries that are more correlated with their level of quality.
Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Data
The model creators note in the associated paper:
CNNDM4: is a large scale news dataset. Nallapati et al: we treat the news articles as the source documents and the associated highlights as the summaries. XSum5: is a highly abstractive dataset of articles from the British Broadcasting Corporation (BBC). NYT6: contains articles from the New York Times and the associated summaries
Training Procedure
Preprocessing
The model creators note in the associated paper:
We follow Kedzie et al. (2018) for data preprocessing and splitting, and use the associated archival abstracts as the summaries
Speeds, Sizes, Times
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
More information needed
Factors
More information needed
Metrics
More information needed
Results
CNNDM
| ROUGE-1 | ROUGE-2 | ROUGE-L | |
|---|---|---|---|
| BART | 44.16 | 21.28 | 40.90 |
| Ours | 47.78 | 23.55 | 44.57 |
Model Examination
The model creators note in the associated paper:
We attribute BRIO-Ctr’s superior performance to its use of the same model architecture (BART) for both candidate generation and scoring, while SimCLS uses RoBERTa as the evaluation model. As a result, BRIO-Ctr maximizes the parameter sharing between the two stages, and preserves the power of the Seq2Seq model pre-trained on the same dataset.
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
The model creators note in the associated paper:
Formulate summarization as a sequence-to-sequence (Seq2Seq) problem
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed.
Citation
BibTeX:
@misc{mesh-transformer-jax,
@misc{https://doi.org/10.48550/arxiv.2203.16804,
doi = {10.48550/ARXIV.2203.16804},
url = {https://arxiv.org/abs/2203.16804},
author = {Liu, Yixin and Liu, Pengfei and Radev, Dragomir and Neubig, Graham},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {BRIO: Bringing Order to Abstractive Summarization},
Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
Yale LILY Lab in collaboration with Ezi Ozoani and the Hugging Face team
Model Card Contact
More information needed
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/brio-cnndm-uncased")
model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/brio-cnndm-uncased")