nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_15p_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_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_15p_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_15p_cola", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_15p 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 |
|---|---|---|---|---|---|
| 0.6171 | 1.0 | 34 | 0.6198 | 0.0 | 0.6913 |
| 0.5943 | 2.0 | 68 | 0.6190 | 0.0461 | 0.6798 |
| 0.5231 | 3.0 | 102 | 0.6267 | 0.1303 | 0.6625 |
| 0.426 | 4.0 | 136 | 0.6903 | 0.1690 | 0.6692 |
| 0.3238 | 5.0 | 170 | 0.7482 | 0.1734 | 0.6616 |
| 0.2572 | 6.0 | 204 | 0.8871 | 0.1642 | 0.6769 |
| 0.2084 | 7.0 | 238 | 0.9010 | 0.1499 | 0.6529 |
Base model
distilbert/distilbert-base-uncased