nyu-mll/glue
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How to use GCopoulos/robertaboolq-finetuned-answer-polarity-2e6 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="GCopoulos/robertaboolq-finetuned-answer-polarity-2e6") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GCopoulos/robertaboolq-finetuned-answer-polarity-2e6")
model = AutoModelForSequenceClassification.from_pretrained("GCopoulos/robertaboolq-finetuned-answer-polarity-2e6")This model is a fine-tuned version of shahrukhx01/roberta-base-boolq on the glue 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 |
|---|---|---|---|---|
| No log | 1.0 | 221 | 0.6677 | 0.7870 |
| 0.8099 | 2.0 | 442 | 0.4562 | 0.8663 |
| 0.3336 | 3.0 | 663 | 0.4096 | 0.8550 |
| 0.3336 | 4.0 | 884 | 0.3876 | 0.8739 |
| 0.221 | 5.0 | 1105 | 0.4030 | 0.8729 |
| 0.1842 | 6.0 | 1326 | 0.3926 | 0.8718 |