nyu-mll/glue
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How to use JeremiahZ/roberta-base-rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-rte")
model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-rte")This model is a fine-tuned version of roberta-base on the GLUE RTE 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 |
|---|---|---|---|---|
| No log | 1.0 | 156 | 0.7023 | 0.4729 |
| No log | 2.0 | 312 | 0.6356 | 0.6895 |
| No log | 3.0 | 468 | 0.5177 | 0.7617 |
| 0.6131 | 4.0 | 624 | 0.6238 | 0.7473 |
| 0.6131 | 5.0 | 780 | 0.5446 | 0.7978 |
| 0.6131 | 6.0 | 936 | 0.9697 | 0.7545 |
| 0.2528 | 7.0 | 1092 | 1.1004 | 0.7690 |
| 0.2528 | 8.0 | 1248 | 1.1937 | 0.7726 |
| 0.2528 | 9.0 | 1404 | 1.3313 | 0.7726 |
| 0.1073 | 10.0 | 1560 | 1.3534 | 0.7726 |