nyu-mll/glue
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How to use gokuls/add_BERT_24_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/add_BERT_24_rte") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_24_rte", dtype="auto")This model is a fine-tuned version of gokuls/add_bert_12_layer_model_complete_training_new 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 |
|---|---|---|---|---|
| 0.7559 | 1.0 | 20 | 0.6977 | 0.5271 |
| 0.704 | 2.0 | 40 | 0.6936 | 0.5271 |
| 0.701 | 3.0 | 60 | 0.6969 | 0.5126 |
| 0.6801 | 4.0 | 80 | 0.7166 | 0.4729 |
| 0.6792 | 5.0 | 100 | 0.7125 | 0.5307 |
| 0.6577 | 6.0 | 120 | 0.7617 | 0.4874 |
| 0.5614 | 7.0 | 140 | 0.9793 | 0.4765 |