nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_lda_book_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_base_lda_book_cola") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_lda_book_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_lda_book_cola", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_book 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 |
|---|---|---|---|---|---|
| 0.6113 | 1.0 | 34 | 0.6214 | 0.0 | 0.6913 |
| 0.603 | 2.0 | 68 | 0.6130 | 0.0 | 0.6913 |
| 0.5686 | 3.0 | 102 | 0.5723 | 0.1641 | 0.7057 |
| 0.4955 | 4.0 | 136 | 0.5847 | 0.2300 | 0.7124 |
| 0.4318 | 5.0 | 170 | 0.5832 | 0.2774 | 0.7181 |
| 0.3816 | 6.0 | 204 | 0.7024 | 0.2886 | 0.7325 |
| 0.293 | 7.0 | 238 | 0.7139 | 0.2827 | 0.7124 |
| 0.2447 | 8.0 | 272 | 0.7470 | 0.3125 | 0.7143 |
Base model
gokulsrinivasagan/bert_base_lda_book