google/bigbench
Updated • 620 • 72
How to use kennethge123/bigbench_entailedpolarity-t5-base with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="kennethge123/bigbench_entailedpolarity-t5-base") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kennethge123/bigbench_entailedpolarity-t5-base")
model = AutoModelForSequenceClassification.from_pretrained("kennethge123/bigbench_entailedpolarity-t5-base")This model is a fine-tuned version of t5-base on the bigbench 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 |
|---|---|---|---|---|
| No log | 1.0 | 24 | 0.4860 | 0.875 |
| No log | 2.0 | 48 | 0.3200 | 0.875 |
| No log | 3.0 | 72 | 0.3107 | 0.9167 |
| No log | 4.0 | 96 | 0.3666 | 0.9167 |
| No log | 5.0 | 120 | 0.5273 | 0.9167 |
| No log | 6.0 | 144 | 0.3190 | 0.9583 |
| No log | 7.0 | 168 | 0.3328 | 0.9583 |
| No log | 8.0 | 192 | 0.5994 | 0.9167 |
| No log | 9.0 | 216 | 0.6515 | 0.9167 |
| No log | 10.0 | 240 | 0.6435 | 0.9167 |
| No log | 11.0 | 264 | 0.6450 | 0.9167 |
| No log | 12.0 | 288 | 0.6565 | 0.9167 |
| No log | 13.0 | 312 | 0.6484 | 0.9167 |
| No log | 14.0 | 336 | 0.6376 | 0.9167 |
| No log | 15.0 | 360 | 0.6808 | 0.9167 |
| No log | 16.0 | 384 | 0.6884 | 0.9167 |
| No log | 17.0 | 408 | 0.6502 | 0.9167 |
| No log | 18.0 | 432 | 0.6781 | 0.9167 |
| No log | 19.0 | 456 | 0.3894 | 0.9583 |
| No log | 20.0 | 480 | 0.3881 | 0.9583 |
Base model
google-t5/t5-base