metadata
language:
- en
widget:
- text: >-
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
- text: >-
We have successfully negotiated favorable terms with our suppliers,
improving our cost structure
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]