nyu-mll/glue
Viewer • Updated • 1.49M • 424k • 523
How to use thrunlab/t5-base_cola_moe_ex38_epochs-3_decoder_all_sparsity20_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-3_decoder_all_sparsity20_mare_mlp") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_cola_moe_ex38_epochs-3_decoder_all_sparsity20_mare_mlp")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_cola_moe_ex38_epochs-3_decoder_all_sparsity20_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:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.54 | 0.19 | 50 | 0.9351 | 0.8178 |
| 0.508 | 0.37 | 100 | 0.7150 | 0.8332 |
| 0.5206 | 0.56 | 150 | 0.6512 | 0.8265 |
| 0.4831 | 0.75 | 200 | 0.6504 | 0.8274 |
| 0.5094 | 0.93 | 250 | 0.5474 | 0.8313 |
| 0.3632 | 1.12 | 300 | 0.6911 | 0.8226 |
| 0.3467 | 1.31 | 350 | 0.6089 | 0.8303 |
| 0.3803 | 1.5 | 400 | 0.5704 | 0.8360 |
| 0.3281 | 1.68 | 450 | 0.6079 | 0.8313 |
| 0.3239 | 1.87 | 500 | 0.5792 | 0.8284 |
| 0.2903 | 2.06 | 550 | 0.5910 | 0.8293 |
| 0.3892 | 2.24 | 600 | 0.6007 | 0.8341 |
| 0.2846 | 2.43 | 650 | 0.5993 | 0.8351 |
| 0.3209 | 2.62 | 700 | 0.6508 | 0.8360 |
| 0.2325 | 2.8 | 750 | 0.6217 | 0.8341 |
| 0.3949 | 2.99 | 800 | 0.6201 | 0.8341 |
Base model
google-t5/t5-base