nyu-mll/glue
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How to use ShirinP/nli-distilroberta-base-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="ShirinP/nli-distilroberta-base-finetuned-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ShirinP/nli-distilroberta-base-finetuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("ShirinP/nli-distilroberta-base-finetuned-cola", device_map="auto")This model is a fine-tuned version of cross-encoder/nli-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 | Matthews Correlation |
|---|---|---|---|---|
| 0.5646 | 1.0 | 535 | 0.6462 | 0.3422 |
| 0.4267 | 2.0 | 1070 | 0.5672 | 0.4422 |
| 0.3354 | 3.0 | 1605 | 0.6441 | 0.4698 |
| 0.2723 | 4.0 | 2140 | 0.7464 | 0.4670 |
| 0.2204 | 5.0 | 2675 | 0.8280 | 0.4957 |