nyu-mll/glue
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How to use vxbrandon/t5-base_cola_dense with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="vxbrandon/t5-base_cola_dense") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("vxbrandon/t5-base_cola_dense")
model = AutoModelForSequenceClassification.from_pretrained("vxbrandon/t5-base_cola_dense")This model is a fine-tuned version of t5-base 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 | Accuracy |
|---|---|---|---|---|
| 0.6331 | 0.07 | 10 | 0.6263 | 0.6855 |
| 0.626 | 0.15 | 20 | 0.6247 | 0.6826 |
| 0.6412 | 0.22 | 30 | 0.6240 | 0.6865 |
| 0.6497 | 0.3 | 40 | 0.6210 | 0.6874 |
| 0.6226 | 0.37 | 50 | 0.6213 | 0.6874 |
| 0.6183 | 0.45 | 60 | 0.6198 | 0.6894 |
| 0.6034 | 0.52 | 70 | 0.6202 | 0.6894 |
| 0.5802 | 0.6 | 80 | 0.6219 | 0.6913 |
| 0.6005 | 0.67 | 90 | 0.6261 | 0.6913 |
| 0.6178 | 0.75 | 100 | 0.6331 | 0.6922 |
| 0.5887 | 0.82 | 110 | 0.6344 | 0.6913 |
| 0.6492 | 0.9 | 120 | 0.6371 | 0.6913 |
| 0.6333 | 0.97 | 130 | 0.6376 | 0.6913 |
Base model
google-t5/t5-base