tags:
- text-2-text-generation
- t5
Model Card for t5_sentence_paraphraser
Model Details
Model Description
Using this model you can generate paraphrases of any given question.
- Developed by: Ramsri Goutham Golla
- Shared by [Optional]: Ramsri Goutham Golla
- Model type: Text2Text Generation
- Language(s) (NLP): More information needed
- License: More information needed
- Parent Model: All T5 Checkpoints
- Resources for more information:
Uses
Direct Use
This model can be used for the task of Text2Text Generation.
Downstream Use [Optional]
More information needed.
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 developers also write in a blog post that the model:
Quora Question Pairs dataset to collect all the questions marked as duplicates and prepared training and validation sets. Questions that are duplicates serve our purpose of getting paraphrase pairs.
Training Procedure
The developers also write in a blog post that the model:
I trained T5 with the original sentence as input and paraphrased (duplicate sentence from Quora Question pairs) sentence as output.
Preprocessing
More information needed
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
More information needed
Model Examination
More information needed
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: p2.xlarge
- Hours used: ~20 hrs
- Cloud Provider: AWS ec2
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
More information needed
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed.
Citation
BibTeX: More information needed
APA:
More information needed
Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
Ramsri Goutham Golla 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("ramsrigouthamg/t5_sentence_paraphraser")
model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5_sentence_paraphraser")
See the blog post and this Colab Notebook for more examples.