nyu-mll/glue
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How to use thrunlab/t5-base_cola_mare_ar2_ex16_size-64_collected-stats with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="thrunlab/t5-base_cola_mare_ar2_ex16_size-64_collected-stats") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_cola_mare_ar2_ex16_size-64_collected-stats")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_cola_mare_ar2_ex16_size-64_collected-stats", 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 |
|---|---|---|---|---|
| 0.6019 | 0.37 | 50 | 0.5998 | 0.6913 |
| 0.4826 | 0.75 | 100 | 0.5145 | 0.7900 |
| 0.3876 | 1.12 | 150 | 0.5321 | 0.7824 |
| 0.3986 | 1.49 | 200 | 0.4806 | 0.8063 |
| 0.3751 | 1.87 | 250 | 0.4468 | 0.8207 |
| 0.309 | 2.24 | 300 | 0.4541 | 0.8226 |
| 0.3386 | 2.61 | 350 | 0.4573 | 0.8226 |
| 0.3244 | 2.99 | 400 | 0.4506 | 0.8255 |
| 0.2634 | 3.36 | 450 | 0.4938 | 0.8274 |
| 0.3371 | 3.73 | 500 | 0.4735 | 0.8284 |
| 0.3036 | 4.1 | 550 | 0.4729 | 0.8293 |
| 0.2891 | 4.48 | 600 | 0.4719 | 0.8293 |
| 0.354 | 4.85 | 650 | 0.4874 | 0.8284 |
Base model
google-t5/t5-base