nyu-mll/glue
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How to use thrunlab/t5-base_cola_dense_epochs-6_exp_size_4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="thrunlab/t5-base_cola_dense_epochs-6_exp_size_4") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_cola_dense_epochs-6_exp_size_4")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_cola_dense_epochs-6_exp_size_4", 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 |
|---|---|---|---|---|
| 0.5883 | 0.19 | 50 | 0.5895 | 0.6913 |
| 0.4961 | 0.37 | 100 | 0.5788 | 0.7574 |
| 0.5036 | 0.56 | 150 | 0.5192 | 0.7891 |
| 0.4038 | 0.75 | 200 | 0.4774 | 0.8025 |
| 0.4461 | 0.93 | 250 | 0.5380 | 0.7929 |
| 0.3573 | 1.12 | 300 | 0.5382 | 0.8169 |
| 0.3508 | 1.31 | 350 | 0.4526 | 0.8255 |
| 0.3379 | 1.49 | 400 | 0.4777 | 0.8245 |
| 0.3964 | 1.68 | 450 | 0.5148 | 0.8178 |
| 0.4137 | 1.87 | 500 | 0.4622 | 0.8236 |
| 0.3036 | 2.05 | 550 | 0.5171 | 0.8236 |
| 0.2913 | 2.24 | 600 | 0.5269 | 0.8322 |
| 0.277 | 2.43 | 650 | 0.5298 | 0.8293 |
| 0.2431 | 2.61 | 700 | 0.5129 | 0.8313 |
| 0.3551 | 2.8 | 750 | 0.5396 | 0.8255 |
| 0.2697 | 2.99 | 800 | 0.5307 | 0.8293 |
| 0.2494 | 3.17 | 850 | 0.5549 | 0.8332 |
| 0.2734 | 3.36 | 900 | 0.5431 | 0.8255 |
| 0.2886 | 3.54 | 950 | 0.5412 | 0.8245 |
| 0.3155 | 3.73 | 1000 | 0.5409 | 0.8284 |
Base model
google-t5/t5-base