nyu-mll/glue
Viewer • Updated • 1.49M • 472k • 497
How to use gokuls/distilbert_add_GLUE_Experiment_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/distilbert_add_GLUE_Experiment_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/distilbert_add_GLUE_Experiment_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/distilbert_add_GLUE_Experiment_cola")This model is a fine-tuned version of distilbert-base-uncased on the GLUE COLA dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.6218 | 1.0 | 34 | 0.6182 | 0.0 |
| 0.611 | 2.0 | 68 | 0.6194 | 0.0 |
| 0.6084 | 3.0 | 102 | 0.6226 | 0.0 |
| 0.6104 | 4.0 | 136 | 0.6186 | 0.0 |
| 0.6102 | 5.0 | 170 | 0.6214 | 0.0 |
| 0.6095 | 6.0 | 204 | 0.6187 | 0.0 |