nyu-mll/glue
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How to use Hartunka/distilbert_rand_50_v2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_rand_50_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_50_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_rand_50_v2_cola")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_50_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_rand_50_v2_cola")This model is a fine-tuned version of Hartunka/distilbert_rand_50_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 |
|---|---|---|---|---|---|
| 0.6138 | 1.0 | 34 | 0.6141 | 0.0 | 0.6913 |
| 0.5901 | 2.0 | 68 | 0.6175 | 0.0257 | 0.6913 |
| 0.5446 | 3.0 | 102 | 0.6304 | 0.0832 | 0.6826 |
| 0.4862 | 4.0 | 136 | 0.7370 | 0.0899 | 0.6366 |
| 0.4254 | 5.0 | 170 | 0.7218 | 0.1086 | 0.6635 |
| 0.372 | 6.0 | 204 | 0.8227 | 0.0725 | 0.6079 |
Base model
Hartunka/distilbert_rand_50_v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/distilbert_rand_50_v2_cola")