nyu-mll/glue
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How to use gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_cola")This model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the GLUE COLA 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 | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6069 | 1.0 | 34 | 0.6018 | 0.0 | 0.6913 |
| 0.5619 | 2.0 | 68 | 0.5891 | 0.1766 | 0.7076 |
| 0.4858 | 3.0 | 102 | 0.5796 | 0.2588 | 0.7248 |
| 0.4109 | 4.0 | 136 | 0.6467 | 0.2838 | 0.7306 |
| 0.349 | 5.0 | 170 | 0.6379 | 0.3133 | 0.7354 |
| 0.294 | 6.0 | 204 | 0.6805 | 0.3436 | 0.7440 |
| 0.2564 | 7.0 | 238 | 0.7498 | 0.3178 | 0.7363 |
| 0.2222 | 8.0 | 272 | 0.7861 | 0.3320 | 0.7383 |
Base model
google/bert_uncased_L-4_H-512_A-8