Zabbonat/DDI

Model Card for Model ID

The model is fine-tune on different case studies of companies using cloud services and earnings call transcripts from 2004 to 2007. The model is able to recognise the concept of data-driven innovation (OECD, 2015).

Model Details

Fine-tune of RoBERTa uncase

Model Sources

• Paper [optional]: [coming soon]

Uses

The model is able to recognise the concept of data-driven innovation (OECD, 2015).

• NoDDI : No Data-Driven Innovation
• DDI: Data-Driven Innovation

Example Pipeline

# Use a pipeline as a high-level helper
from transformers import pipeline
ddi = pipeline("text-classification", model="Zabbonat/DDI")
ddi('And another important point i would like to highlight, we selected google cloud as a technology partner to speed up the implementation of digital innovation')

[{'label': 'DDI', 'score': 0.99}]

Evaluation

• Accuracy: 0.78
• Precision 0.84
• Recall: 0.78
• F1-Score: 0.77

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]