nyu-mll/glue
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How to use gokuls/hBERTv1_data_aug_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hBERTv1_data_aug_cola") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hBERTv1_data_aug_cola", dtype="auto")This model is a fine-tuned version of gokuls/bert_12_layer_model_v1 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 |
|---|---|---|---|---|
| 0.6084 | 1.0 | 835 | 0.6252 | 0.0 |
| 0.6066 | 2.0 | 1670 | 0.6183 | 0.0 |
| 0.6065 | 3.0 | 2505 | 0.6185 | 0.0 |
| 0.6062 | 4.0 | 3340 | 0.6219 | 0.0 |
| 0.6061 | 5.0 | 4175 | 0.6205 | 0.0 |
| 0.6066 | 6.0 | 5010 | 0.6184 | 0.0 |
| 0.6061 | 7.0 | 5845 | 0.6187 | 0.0 |