nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_base_train_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_cola")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train 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 |
|---|---|---|---|---|---|
| 0.6144 | 1.0 | 34 | 0.6205 | 0.0 | 0.6913 |
| 0.6087 | 2.0 | 68 | 0.6196 | 0.0804 | 0.6942 |
| 0.5825 | 3.0 | 102 | 0.6123 | 0.0797 | 0.6942 |
| 0.5448 | 4.0 | 136 | 0.6400 | 0.1015 | 0.6942 |
| 0.4899 | 5.0 | 170 | 0.6471 | 0.1192 | 0.6616 |
| 0.4413 | 6.0 | 204 | 0.7278 | 0.1055 | 0.6721 |
| 0.3897 | 7.0 | 238 | 0.7691 | 0.0942 | 0.6500 |
| 0.346 | 8.0 | 272 | 0.8366 | 0.1073 | 0.6347 |
Base model
distilbert/distilbert-base-uncased