pkavumba/balanced-copa
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How to use Ariffiq99/Bert_Stacked_model_100 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="Ariffiq99/Bert_Stacked_model_100") # Load model directly
from transformers import AutoTokenizer, AutoModelForMultipleChoice
tokenizer = AutoTokenizer.from_pretrained("Ariffiq99/Bert_Stacked_model_100")
model = AutoModelForMultipleChoice.from_pretrained("Ariffiq99/Bert_Stacked_model_100")This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 1.249 | 1.0 | 1576 | 1.1862 | 0.5172 |
| 1.1963 | 2.0 | 3152 | 1.1461 | 0.5407 |
| 1.1495 | 3.0 | 4728 | 1.1241 | 0.5570 |
| 1.1192 | 4.0 | 6304 | 1.1172 | 0.5634 |
| 1.1025 | 5.0 | 7880 | 1.1094 | 0.5669 |
Base model
google-bert/bert-base-uncased