nyu-mll/glue
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How to use gokuls/sa_BERT_48_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/sa_BERT_48_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/sa_BERT_48_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/sa_BERT_48_cola")This model is a fine-tuned version of gokuls/bert_base_48 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 |
|---|---|---|---|---|---|
| 0.6218 | 1.0 | 90 | 0.6131 | 0.0 | 0.6913 |
| 0.5938 | 2.0 | 180 | 0.6131 | 0.0867 | 0.6951 |
| 0.5535 | 3.0 | 270 | 0.6607 | 0.0838 | 0.6472 |
| 0.5036 | 4.0 | 360 | 0.6657 | 0.0746 | 0.6721 |
| 0.4578 | 5.0 | 450 | 0.8804 | 0.1134 | 0.6222 |
| 0.4167 | 6.0 | 540 | 0.7758 | 0.0645 | 0.6194 |
| 0.3852 | 7.0 | 630 | 0.8373 | 0.0386 | 0.6299 |