nyu-mll/glue
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How to use Momorami/fnet-base-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Momorami/fnet-base-finetuned-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Momorami/fnet-base-finetuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("Momorami/fnet-base-finetuned-cola")This model is a fine-tuned version of google/fnet-base 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 |
|---|---|---|---|---|
| 0.61 | 1.0 | 268 | 0.5818 | 0.1606 |
| 0.5265 | 2.0 | 536 | 0.5489 | 0.3415 |
| 0.4161 | 3.0 | 804 | 0.5454 | 0.3451 |
| 0.3324 | 4.0 | 1072 | 0.5746 | 0.3869 |
| 0.2657 | 5.0 | 1340 | 0.6476 | 0.3934 |
Base model
google/fnet-base