nyu-mll/glue
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How to use thrunlab/t5-large_rte_dense_sp0_ar0 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="thrunlab/t5-large_rte_dense_sp0_ar0") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-large_rte_dense_sp0_ar0")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-large_rte_dense_sp0_ar0", device_map="auto")This model is a fine-tuned version of t5-large 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 |
|---|---|---|---|---|
| 0.6781 | 0.16 | 25 | 0.6834 | 0.5487 |
| 0.7041 | 0.32 | 50 | 0.6878 | 0.5523 |
| 0.689 | 0.48 | 75 | 0.6836 | 0.6065 |
| 0.6902 | 0.64 | 100 | 0.6630 | 0.5740 |
| 0.6458 | 0.8 | 125 | 0.5695 | 0.7112 |
| 0.5973 | 0.96 | 150 | 0.6138 | 0.6823 |
| 0.5697 | 1.12 | 175 | 0.5707 | 0.7581 |
| 0.4567 | 1.28 | 200 | 0.6558 | 0.7256 |
| 0.3796 | 1.44 | 225 | 0.4968 | 0.7870 |
| 0.3749 | 1.6 | 250 | 0.5082 | 0.8123 |
| 0.5187 | 1.76 | 275 | 0.4428 | 0.8123 |
| 0.4176 | 1.92 | 300 | 0.3940 | 0.8556 |
| 0.2678 | 2.08 | 325 | 0.4938 | 0.8484 |
| 0.0761 | 2.24 | 350 | 0.6533 | 0.8520 |
| 0.2082 | 2.4 | 375 | 0.5901 | 0.8484 |
| 0.4081 | 2.56 | 400 | 0.5939 | 0.8520 |
Base model
google-t5/t5-large