nyu-mll/glue
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How to use thrunlab/t5-base_cola_moe_ex19_epochs-3_decoder_all_sparsity10_mare_mlp with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="thrunlab/t5-base_cola_moe_ex19_epochs-3_decoder_all_sparsity10_mare_mlp") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_cola_moe_ex19_epochs-3_decoder_all_sparsity10_mare_mlp")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_cola_moe_ex19_epochs-3_decoder_all_sparsity10_mare_mlp", 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 |
|---|---|---|---|---|
| 0.5636 | 0.19 | 50 | 0.9030 | 0.8255 |
| 0.5623 | 0.37 | 100 | 0.7397 | 0.8322 |
| 0.571 | 0.56 | 150 | 0.7188 | 0.8159 |
| 0.4997 | 0.75 | 200 | 0.6449 | 0.8322 |
| 0.5069 | 0.93 | 250 | 0.5668 | 0.8332 |
| 0.374 | 1.12 | 300 | 0.6804 | 0.8245 |
| 0.3617 | 1.31 | 350 | 0.6122 | 0.8313 |
| 0.3928 | 1.5 | 400 | 0.5891 | 0.8274 |
| 0.3772 | 1.68 | 450 | 0.6124 | 0.8245 |
| 0.3275 | 1.87 | 500 | 0.5892 | 0.8255 |
| 0.2992 | 2.06 | 550 | 0.6055 | 0.8255 |
| 0.4092 | 2.24 | 600 | 0.6054 | 0.8293 |
| 0.288 | 2.43 | 650 | 0.5972 | 0.8313 |
| 0.3493 | 2.62 | 700 | 0.6449 | 0.8313 |
| 0.2419 | 2.8 | 750 | 0.6198 | 0.8332 |
| 0.3811 | 2.99 | 800 | 0.6252 | 0.8322 |
Base model
google-t5/t5-base