nyu-mll/glue
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How to use Hartunka/bert_base_rand_5_v2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_5_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_5_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_5_v2_cola")This model is a fine-tuned version of Hartunka/bert_base_rand_5_v2 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 |
|---|---|---|---|---|---|
| 0.6133 | 1.0 | 34 | 0.6206 | 0.0 | 0.6913 |
| 0.5905 | 2.0 | 68 | 0.6223 | 0.0348 | 0.6894 |
| 0.539 | 3.0 | 102 | 0.6405 | 0.0967 | 0.6625 |
| 0.4892 | 4.0 | 136 | 0.6829 | 0.1169 | 0.6414 |
| 0.4391 | 5.0 | 170 | 0.6773 | 0.0943 | 0.6702 |
| 0.3908 | 6.0 | 204 | 0.8018 | 0.0495 | 0.6500 |
Base model
Hartunka/bert_base_rand_5_v2