nyu-mll/glue
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How to use gokuls/hBERTv2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hBERTv2_cola") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hBERTv2_cola", dtype="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 |
|---|---|---|---|---|
| 0.6296 | 1.0 | 34 | 0.6182 | 0.0 |
| 0.614 | 2.0 | 68 | 0.6182 | 0.0 |
| 0.6089 | 3.0 | 102 | 0.6214 | 0.0 |
| 0.6092 | 4.0 | 136 | 0.6191 | 0.0 |
| 0.6103 | 5.0 | 170 | 0.6221 | 0.0 |
| 0.6094 | 6.0 | 204 | 0.6183 | 0.0 |
| 0.6096 | 7.0 | 238 | 0.6196 | 0.0 |