nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_15p_ra_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_base_train_book_ent_15p_ra_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_15p_ra_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_15p_ra_cola")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_15p_ra 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 |
|---|---|---|---|---|---|
| 0.6148 | 1.0 | 34 | 0.6123 | -0.0234 | 0.6874 |
| 0.5856 | 2.0 | 68 | 0.6230 | 0.0615 | 0.6922 |
| 0.496 | 3.0 | 102 | 0.6223 | 0.1482 | 0.6807 |
| 0.3811 | 4.0 | 136 | 0.6716 | 0.1993 | 0.6989 |
| 0.2817 | 5.0 | 170 | 0.7722 | 0.2012 | 0.6922 |
| 0.2059 | 6.0 | 204 | 0.8630 | 0.2130 | 0.7018 |
Base model
distilbert/distilbert-base-uncased