nyu-mll/glue
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How to use Bhumika/roberta-base-finetuned-sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Bhumika/roberta-base-finetuned-sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Bhumika/roberta-base-finetuned-sst2")
model = AutoModelForSequenceClassification.from_pretrained("Bhumika/roberta-base-finetuned-sst2")This model was trained from scratch 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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.1106 | 1.0 | 4210 | 0.9255 | 0.3326 |
| 0.1497 | 2.0 | 8420 | 0.9369 | 0.2858 |
| 0.1028 | 3.0 | 12630 | 0.3128 | 0.9335 |
| 0.0872 | 4.0 | 16840 | 0.3000 | 0.9450 |
| 0.0571 | 5.0 | 21050 | 0.3378 | 0.9427 |