nyu-mll/glue
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How to use frtna/distilbert-base-uncased-finetuned-SOMETHING with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="frtna/distilbert-base-uncased-finetuned-SOMETHING") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("frtna/distilbert-base-uncased-finetuned-SOMETHING")
model = AutoModelForSequenceClassification.from_pretrained("frtna/distilbert-base-uncased-finetuned-SOMETHING")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 |
|---|---|---|---|---|
| 0.5212 | 1.0 | 535 | 0.4654 | 0.4818 |
| 0.3438 | 2.0 | 1070 | 0.5179 | 0.4950 |
| 0.2279 | 3.0 | 1605 | 0.6018 | 0.5404 |
| 0.173 | 4.0 | 2140 | 0.7799 | 0.5637 |
| 0.1335 | 5.0 | 2675 | 0.8336 | 0.5541 |
Base model
distilbert/distilbert-base-uncased