nyu-mll/glue
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How to use kdo6301/bert-base-uncased-finetuned-cola-2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="kdo6301/bert-base-uncased-finetuned-cola-2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kdo6301/bert-base-uncased-finetuned-cola-2")
model = AutoModelForSequenceClassification.from_pretrained("kdo6301/bert-base-uncased-finetuned-cola-2")This model is a fine-tuned version of bert-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 |
|---|---|---|---|---|
| 0.4906 | 1.0 | 535 | 0.5046 | 0.5080 |
| 0.2901 | 2.0 | 1070 | 0.5881 | 0.5235 |
| 0.1818 | 3.0 | 1605 | 0.7253 | 0.5584 |
| 0.1177 | 4.0 | 2140 | 0.8316 | 0.5927 |
| 0.0826 | 5.0 | 2675 | 0.9235 | 0.6016 |