nyu-mll/glue
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How to use thrunlab/t5-base_mrpc_dense_sp0_ar0 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="thrunlab/t5-base_mrpc_dense_sp0_ar0") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_mrpc_dense_sp0_ar0")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_mrpc_dense_sp0_ar0", device_map="auto")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 |
|---|---|---|---|---|
| 0.717 | 0.64 | 25 | 0.6894 | 0.5307 |
| 0.6467 | 1.28 | 50 | 0.6510 | 0.6173 |
| 0.6062 | 1.92 | 75 | 0.5660 | 0.7292 |
| 0.503 | 2.56 | 100 | 0.5416 | 0.7473 |
| 0.4691 | 3.21 | 125 | 0.5493 | 0.7220 |
| 0.4518 | 3.85 | 150 | 0.5516 | 0.7509 |
| 0.4087 | 4.49 | 175 | 0.5405 | 0.7690 |
| 0.3352 | 5.13 | 200 | 0.5216 | 0.7870 |
Base model
google-t5/t5-base