nyu-mll/glue
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How to use gokuls/add_BERT_no_pretrain_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/add_BERT_no_pretrain_cola") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_no_pretrain_cola", device_map="auto")This model is a fine-tuned version of 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 | Accuracy |
|---|---|---|---|---|---|
| 0.6339 | 1.0 | 67 | 0.6182 | 0.0 | 0.6913 |
| 0.6177 | 2.0 | 134 | 0.6421 | 0.0 | 0.6913 |
| 0.6204 | 3.0 | 201 | 0.6295 | 0.0 | 0.6913 |
| 0.6182 | 4.0 | 268 | 0.6268 | 0.0 | 0.6913 |
| 0.6149 | 5.0 | 335 | 0.6181 | 0.0 | 0.6913 |
| 0.612 | 6.0 | 402 | 0.6189 | 0.0 | 0.6913 |
| 0.6132 | 7.0 | 469 | 0.6292 | 0.0 | 0.6913 |
| 0.6125 | 8.0 | 536 | 0.6185 | 0.0 | 0.6913 |
| 0.6108 | 9.0 | 603 | 0.6280 | 0.0 | 0.6913 |
| 0.6092 | 10.0 | 670 | 0.6310 | 0.0 | 0.6913 |