nyu-mll/glue
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How to use gokulsrinivasagan/bert_tiny_lda_50_v1_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_tiny_lda_50_v1_cola") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_tiny_lda_50_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_tiny_lda_50_v1_cola", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_tiny_lda_50_v1 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 | 0.6161 | 0.0 | 0.6913 |
| 0.6043 | 2.0 | 68 | 0.6183 | 0.0 | 0.6913 |
| 0.5936 | 3.0 | 102 | 0.6145 | 0.0137 | 0.6903 |
| 0.571 | 4.0 | 136 | 0.6410 | 0.0135 | 0.6817 |
| 0.5262 | 5.0 | 170 | 0.6339 | 0.0823 | 0.6606 |
| 0.4847 | 6.0 | 204 | 0.6978 | 0.0594 | 0.6577 |
| 0.4484 | 7.0 | 238 | 0.7012 | 0.0706 | 0.6558 |
| 0.4148 | 8.0 | 272 | 0.7537 | 0.1134 | 0.6577 |
Base model
gokulsrinivasagan/bert_tiny_lda_50_v1