Instructions to use Dzeniks/alberta_fact_checking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dzeniks/alberta_fact_checking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dzeniks/alberta_fact_checking")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dzeniks/alberta_fact_checking") model = AutoModelForSequenceClassification.from_pretrained("Dzeniks/alberta_fact_checking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -43,4 +43,4 @@ print(f"Label: {label}")
|
|
| 43 |
|
| 44 |
## Disclaimer
|
| 45 |
|
| 46 |
-
While the
|
|
|
|
| 43 |
|
| 44 |
## Disclaimer
|
| 45 |
|
| 46 |
+
While the alberta_fact_checking Model has been trained on a relatively large dataset and can provide accurate results, it may not always provide correct results. Users should always exercise caution when making decisions based on the output of any machine learning model.
|