Instructions to use DeepPavlov/roberta-large-winogrande with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepPavlov/roberta-large-winogrande with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DeepPavlov/roberta-large-winogrande")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/roberta-large-winogrande") model = AutoModelForSequenceClassification.from_pretrained("DeepPavlov/roberta-large-winogrande") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,7 +13,7 @@ widget:
|
|
| 13 |
# RoBERTa Large model fine-tuned on Winogrande
|
| 14 |
|
| 15 |
This model was fine-tuned on Winogrande dataset (XL size) in sequence classification task format, meaning that original pairs of sentences
|
| 16 |
-
with corresponding options filled in
|
| 17 |
|
| 18 |
## Model description
|
| 19 |
|
|
|
|
| 13 |
# RoBERTa Large model fine-tuned on Winogrande
|
| 14 |
|
| 15 |
This model was fine-tuned on Winogrande dataset (XL size) in sequence classification task format, meaning that original pairs of sentences
|
| 16 |
+
with corresponding options filled in were separated, shuffled and classified independently of each other.
|
| 17 |
|
| 18 |
## Model description
|
| 19 |
|