nyu-mll/glue
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How to use jysh1023/bert_uncased_QAT with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="jysh1023/bert_uncased_QAT") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jysh1023/bert_uncased_QAT")
model = AutoModelForSequenceClassification.from_pretrained("jysh1023/bert_uncased_QAT")This model is a fine-tuned version of google/bert_uncased_L-6_H-768_A-12 on the glue 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 |
|---|---|---|---|---|
| 0.2453 | 1.0 | 527 | 0.2552 | 0.8979 |
| 0.1257 | 2.0 | 1054 | 0.2997 | 0.8933 |
| 0.0818 | 3.0 | 1581 | 0.2984 | 0.9094 |
| 0.057 | 4.0 | 2108 | 0.3181 | 0.9048 |
| 0.0403 | 5.0 | 2635 | 0.3299 | 0.9083 |
| 0.0274 | 6.0 | 3162 | 0.4222 | 0.9060 |
| 0.0192 | 7.0 | 3689 | 0.4797 | 0.9083 |
Base model
google/bert_uncased_L-6_H-768_A-12