nyu-mll/glue
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How to use gokuls/add_BERT_48_qnli with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/add_BERT_48_qnli") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_48_qnli", device_map="auto")This model is a fine-tuned version of gokuls/add_bert_12_layer_model_complete_training_new_48 on the GLUE QNLI 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 |
|---|---|---|---|---|
| 0.6959 | 1.0 | 819 | 0.6933 | 0.5103 |
| 0.6945 | 2.0 | 1638 | 0.6917 | 0.4946 |
| 0.6938 | 3.0 | 2457 | 0.6899 | 0.5263 |
| 0.6925 | 4.0 | 3276 | 0.6903 | 0.5318 |
| 0.6961 | 5.0 | 4095 | 0.6940 | 0.5222 |
| 0.6929 | 6.0 | 4914 | 0.6914 | 0.5252 |
| 0.6922 | 7.0 | 5733 | 0.6921 | 0.5043 |
| 0.692 | 8.0 | 6552 | 0.6904 | 0.5303 |