Model Card for Model ID
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Shubhangi Nikam and Asmita Singh
- Funded by [optional]: NA
- Shared by [optional]: NA
- Model type: Summarization
- Language(s) (NLP): Python
- License: NA
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: https://huggingface.co/Shubhangi21/mt5-small-finetuned-xsum
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
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.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
XSum Dataset
XSum is a dataset for abstractive summarization. It contains BBC articles and corresponding single-sentence summaries written by experts.
Number of Samples:
Training: 20 samples
Validation: 20 samples
Testing: 20 samples
Preprocessing:
Documents were truncated to a maximum length of 512 tokens.
Summaries were truncated to a maximum length of 128 tokens.
[More Information Needed]
Training Procedure
Preprocessing
Tokenization was performed using the MT5Tokenizer (slow tokenizer).
Input text was tokenized with a maximum input length of 512 tokens.
Target summaries were tokenized with a maximum target length of 128 tokens.
Training Hyperparameters
Learning Rate: 2e-5
Batch Size: 4 (per device)
Weight Decay: 0.01
Number of Epochs: 3
Precision: Mixed Precision (fp16)
Optimizer: AdamW
Scheduler: Linear Scheduler
Save Strategy: Save the last 2 checkpoints during training.
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[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]
Summary
Model Examination [optional]
[More Information Needed]
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
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
