nyu-mll/glue
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How to use thrunlab/t5-base_cola_dense_epochs-3 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-3") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_cola_dense_epochs-3")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_cola_dense_epochs-3", 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 |
|---|---|---|---|---|
| 0.5796 | 0.19 | 50 | 0.5780 | 0.6913 |
| 0.4821 | 0.37 | 100 | 0.6683 | 0.7546 |
| 0.4703 | 0.56 | 150 | 0.4976 | 0.8035 |
| 0.4252 | 0.75 | 200 | 0.4958 | 0.8150 |
| 0.4915 | 0.93 | 250 | 0.5360 | 0.8044 |
| 0.3812 | 1.12 | 300 | 0.4645 | 0.8322 |
| 0.3603 | 1.31 | 350 | 0.4788 | 0.8293 |
| 0.3336 | 1.49 | 400 | 0.5135 | 0.8245 |
| 0.4157 | 1.68 | 450 | 0.5311 | 0.8322 |
| 0.4094 | 1.87 | 500 | 0.5042 | 0.8284 |
| 0.2836 | 2.05 | 550 | 0.5277 | 0.8313 |
| 0.2993 | 2.24 | 600 | 0.5515 | 0.8341 |
| 0.2843 | 2.43 | 650 | 0.5195 | 0.8332 |
| 0.2288 | 2.61 | 700 | 0.5129 | 0.8332 |
| 0.3165 | 2.8 | 750 | 0.5126 | 0.8360 |
| 0.2717 | 2.99 | 800 | 0.5083 | 0.8332 |
Base model
google-t5/t5-base