nyu-mll/glue
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How to use gokuls/hBERTv2_data_aug_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hBERTv2_data_aug_cola") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hBERTv2_data_aug_cola", device_map="auto")This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 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 |
|---|---|---|---|---|
| 0.6082 | 1.0 | 835 | 0.6237 | 0.0 |
| 0.6068 | 2.0 | 1670 | 0.6186 | 0.0 |
| 0.6064 | 3.0 | 2505 | 0.6181 | 0.0 |
| 0.6062 | 4.0 | 3340 | 0.6218 | 0.0 |
| 0.6061 | 5.0 | 4175 | 0.6212 | 0.0 |
| 0.6062 | 6.0 | 5010 | 0.6186 | 0.0 |
| 0.606 | 7.0 | 5845 | 0.6189 | 0.0 |
| 0.6062 | 8.0 | 6680 | 0.6203 | 0.0 |