nyu-mll/glue
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How to use gokuls/add_BERT_no_pretrain_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/add_BERT_no_pretrain_rte") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_no_pretrain_rte", device_map="auto")This model is a fine-tuned version of 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 |
|---|---|---|---|---|
| 0.7731 | 1.0 | 20 | 0.6942 | 0.5271 |
| 0.709 | 2.0 | 40 | 0.7189 | 0.4729 |
| 0.7188 | 3.0 | 60 | 0.6948 | 0.4729 |
| 0.7007 | 4.0 | 80 | 0.6980 | 0.4729 |
| 0.7048 | 5.0 | 100 | 0.7018 | 0.5271 |
| 0.7065 | 6.0 | 120 | 0.7269 | 0.4729 |