Should not be used as a text summarizer for very long paragraphs.
Bias, Risks, and Limitations
Max input token size of 1024
Max output token size of 24
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.
from transformers import pipeline
text = """Text that needs to be summarized"""
summarizer = pipeline("summarization", model="path-to-model")
summary = summarizer(text)[0]["summary_text"]
print (summary)
Training Details
Training Data
The training data is internally curated and canot be exposed.
Training Procedure
None
Preprocessing [optional]
None
Training Hyperparameters
Training regime: [More Information Needed]
None
Speeds, Sizes, Times [optional]
The training was done using GPU T4x 2. The task took 4:09:47 to complete. The dataset size of 10,000 examples was used for training the generative model.
Evaluation
The quality of summarization was tested on 5000 research journals created over last 20 years.
Testing Data, Factors & Metrics
Test Data Size: 5000 examples
Testing Data
The testing data is internally generated and curated.
Factors
[More Information Needed]
Metrics
The model was evaluated on Rouge Metrics below are the baseline results achieved
