nyu-mll/glue
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How to use gchhablani/fnet-large-finetuned-cola-copy2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gchhablani/fnet-large-finetuned-cola-copy2") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gchhablani/fnet-large-finetuned-cola-copy2")
model = AutoModelForSequenceClassification.from_pretrained("gchhablani/fnet-large-finetuned-cola-copy2", device_map="auto")This model is a fine-tuned version of google/fnet-large 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 |
|---|---|---|---|---|
| 0.6192 | 1.0 | 2138 | 0.6443 | 0.0 |
| 0.6177 | 2.0 | 4276 | 0.6296 | 0.0 |
| 0.6128 | 3.0 | 6414 | 0.6173 | 0.0 |