nyu-mll/glue
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How to use gokuls/bert-base-uncased-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/bert-base-uncased-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/bert-base-uncased-cola")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/bert-base-uncased-cola")This model is a fine-tuned version of bert-base-uncased on the GLUE COLA 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 |
|---|---|---|---|---|
| 0.4952 | 1.0 | 67 | 0.4678 | 0.5145 |
| 0.2907 | 2.0 | 134 | 0.4136 | 0.5992 |
| 0.1654 | 3.0 | 201 | 0.5372 | 0.5727 |
| 0.1187 | 4.0 | 268 | 0.4595 | 0.5838 |
| 0.0901 | 5.0 | 335 | 0.6232 | 0.5887 |
| 0.0675 | 6.0 | 402 | 0.6140 | 0.5837 |
| 0.0596 | 7.0 | 469 | 0.7469 | 0.6006 |
| 0.0502 | 8.0 | 536 | 0.6665 | 0.5905 |
| 0.0391 | 9.0 | 603 | 1.0236 | 0.5550 |