---
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
```python
# 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

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]