nyu-mll/glue
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How to use Vedarutvija/bert-fine-tuned-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Vedarutvija/bert-fine-tuned-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Vedarutvija/bert-fine-tuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("Vedarutvija/bert-fine-tuned-cola", device_map="auto")This model is a fine-tuned version of bert-base-cased on the glue 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 |
|---|---|---|---|---|
| 0.4577 | 1.0 | 1069 | 0.4346 | 0.5262 |
| 0.3284 | 2.0 | 2138 | 0.6484 | 0.5806 |
| 0.1966 | 3.0 | 3207 | 0.7762 | 0.5825 |
Base model
google-bert/bert-base-cased