nyu-mll/glue
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How to use thrunlab/t5-base_cola_moe_ex38_epochs-2_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_ex38_epochs-2_decoder_all_sparsity10_mare_mlp") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_cola_moe_ex38_epochs-2_decoder_all_sparsity10_mare_mlp")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_cola_moe_ex38_epochs-2_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 |
|---|---|---|---|---|
| 0.6136 | 0.37 | 50 | 0.8261 | 0.8236 |
| 0.5765 | 0.75 | 100 | 0.7518 | 0.8236 |
| 0.4863 | 1.12 | 150 | 0.6893 | 0.8332 |
| 0.4761 | 1.49 | 200 | 0.7211 | 0.8245 |
| 0.4241 | 1.87 | 250 | 0.6790 | 0.8313 |
Base model
google-t5/t5-base