nyu-mll/glue
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How to use Hartunka/distilbert_rand_5_v1_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_rand_5_v1_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_5_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_rand_5_v1_cola")This model is a fine-tuned version of Hartunka/distilbert_rand_5_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 |
|---|---|---|---|---|---|
| 0.6177 | 1.0 | 34 | 0.6143 | 0.0 | 0.6913 |
| 0.5895 | 2.0 | 68 | 0.6215 | 0.0303 | 0.6894 |
| 0.546 | 3.0 | 102 | 0.6137 | 0.0869 | 0.6913 |
| 0.4996 | 4.0 | 136 | 0.6762 | 0.1278 | 0.6683 |
| 0.442 | 5.0 | 170 | 0.7010 | 0.1474 | 0.6548 |
| 0.3915 | 6.0 | 204 | 0.7664 | 0.0579 | 0.6347 |
| 0.3473 | 7.0 | 238 | 0.8268 | 0.1269 | 0.6481 |
| 0.3136 | 8.0 | 272 | 0.9089 | 0.1481 | 0.6577 |
Base model
Hartunka/distilbert_rand_5_v1