nyu-mll/glue
Viewer • Updated • 1.49M • 477k • 504
How to use gokulsrinivasagan/bert_base_train_book_ent_2_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_2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_2_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_2_cola")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_2 on the GLUE COLA dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6183 | 1.0 | 34 | 0.6265 | 0.0 | 0.6913 |
| 0.6103 | 2.0 | 68 | 0.6217 | 0.0 | 0.6913 |
| 0.6039 | 3.0 | 102 | 0.6141 | 0.0364 | 0.6913 |
| 0.5947 | 4.0 | 136 | 0.6363 | 0.0424 | 0.6769 |
| 0.5597 | 5.0 | 170 | 0.6327 | 0.0313 | 0.6788 |
| 0.5262 | 6.0 | 204 | 0.6443 | 0.0939 | 0.6481 |
| 0.4888 | 7.0 | 238 | 0.6998 | 0.1285 | 0.6328 |
| 0.4348 | 8.0 | 272 | 0.7573 | 0.0834 | 0.6337 |
Base model
distilbert/distilbert-base-uncased