nyu-mll/glue
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How to use Hartunka/tiny_bert_km_20_v2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_20_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_20_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_20_v2_cola")This model is a fine-tuned version of Hartunka/tiny_bert_km_20_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 |
|---|---|---|---|---|---|
| 0.6209 | 1.0 | 34 | 0.6208 | 0.0 | 0.6913 |
| 0.6054 | 2.0 | 68 | 0.6204 | 0.0 | 0.6913 |
| 0.5937 | 3.0 | 102 | 0.6190 | 0.0 | 0.6913 |
| 0.5732 | 4.0 | 136 | 0.6477 | 0.0284 | 0.6424 |
| 0.5393 | 5.0 | 170 | 0.6415 | 0.0604 | 0.6731 |
| 0.4907 | 6.0 | 204 | 0.6778 | 0.0740 | 0.6644 |
| 0.4491 | 7.0 | 238 | 0.7494 | 0.0665 | 0.6491 |
| 0.4125 | 8.0 | 272 | 0.7906 | 0.0991 | 0.6088 |
Base model
Hartunka/tiny_bert_km_20_v2