nyu-mll/glue
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How to use gokuls/sa_BERT_48_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/sa_BERT_48_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/sa_BERT_48_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/sa_BERT_48_rte")This model is a fine-tuned version of gokuls/bert_base_48 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 |
|---|---|---|---|---|
| 0.7322 | 1.0 | 26 | 0.7111 | 0.4729 |
| 0.7015 | 2.0 | 52 | 0.6990 | 0.5307 |
| 0.6641 | 3.0 | 78 | 0.7983 | 0.5090 |
| 0.4974 | 4.0 | 104 | 1.1008 | 0.5271 |
| 0.3758 | 5.0 | 130 | 1.2088 | 0.4801 |
| 0.2588 | 6.0 | 156 | 1.4545 | 0.5162 |
| 0.1716 | 7.0 | 182 | 1.4467 | 0.5090 |