nyu-mll/glue
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How to use Hartunka/distilbert_rand_100_v2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_rand_100_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_100_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_rand_100_v2_cola")This model is a fine-tuned version of Hartunka/distilbert_rand_100_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 |
|---|---|---|---|---|---|
| 0.6127 | 1.0 | 34 | 0.6148 | 0.0 | 0.6913 |
| 0.591 | 2.0 | 68 | 0.6217 | -0.0163 | 0.6884 |
| 0.5421 | 3.0 | 102 | 0.6132 | 0.0748 | 0.6826 |
| 0.4864 | 4.0 | 136 | 0.7308 | 0.1075 | 0.6596 |
| 0.4232 | 5.0 | 170 | 0.7523 | 0.1393 | 0.6577 |
| 0.3623 | 6.0 | 204 | 0.8275 | 0.1102 | 0.6500 |
| 0.3196 | 7.0 | 238 | 0.9465 | 0.1025 | 0.6328 |
| 0.2848 | 8.0 | 272 | 1.0343 | 0.1314 | 0.6481 |
Base model
Hartunka/distilbert_rand_100_v2