nyu-mll/glue
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How to use gokuls/distilroberta-sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/distilroberta-sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/distilroberta-sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/distilroberta-sst2")This model is a fine-tuned version of distilroberta-base 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 | Accuracy |
|---|---|---|---|---|
| 0.2928 | 1.0 | 4210 | 0.3499 | 0.8876 |
| 0.1908 | 2.0 | 8420 | 0.3451 | 0.9083 |
| 0.1489 | 3.0 | 12630 | 0.3440 | 0.9048 |
| 0.119 | 4.0 | 16840 | 0.4963 | 0.8911 |
| 0.0974 | 5.0 | 21050 | 0.4645 | 0.8888 |