Instructions to use Zabbonat/DDI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zabbonat/DDI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zabbonat/DDI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zabbonat/DDI") model = AutoModelForSequenceClassification.from_pretrained("Zabbonat/DDI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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] | |