nyu-mll/glue
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How to use liziyang625/bert-fine-tuned-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="liziyang625/bert-fine-tuned-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("liziyang625/bert-fine-tuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("liziyang625/bert-fine-tuned-cola")This model is a fine-tuned version of bert-base-cased 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 |
|---|---|---|---|---|
| 0.4613 | 1.0 | 1069 | 0.4303 | 0.5507 |
| 0.3238 | 2.0 | 2138 | 0.6988 | 0.5778 |
| 0.1973 | 3.0 | 3207 | 0.7804 | 0.5692 |
Base model
google-bert/bert-base-cased