nyu-mll/glue
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How to use curtsmith/distilbert-base-uncased-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="curtsmith/distilbert-base-uncased-finetuned-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("curtsmith/distilbert-base-uncased-finetuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("curtsmith/distilbert-base-uncased-finetuned-cola", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased 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 | Matthews Correlation |
|---|---|---|---|---|
| 0.5227 | 1.0 | 535 | 0.5222 | 0.4210 |
| 0.3466 | 2.0 | 1070 | 0.5048 | 0.4832 |
| 0.2335 | 3.0 | 1605 | 0.5641 | 0.5173 |
| 0.1812 | 4.0 | 2140 | 0.7638 | 0.5200 |
| 0.1334 | 5.0 | 2675 | 0.8123 | 0.5364 |
Base model
distilbert/distilbert-base-uncased