nyu-mll/glue
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How to use Hartunka/tiny_bert_km_10_v1_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_10_v1_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_10_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_10_v1_cola")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_10_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_10_v1_cola")This model is a fine-tuned version of Hartunka/tiny_bert_km_10_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 |
|---|---|---|---|---|---|
| 0.6199 | 1.0 | 34 | 0.6199 | 0.0 | 0.6913 |
| 0.6047 | 2.0 | 68 | 0.6219 | 0.0 | 0.6913 |
| 0.5929 | 3.0 | 102 | 0.6195 | 0.0 | 0.6913 |
| 0.5688 | 4.0 | 136 | 0.6350 | 0.0256 | 0.6692 |
| 0.5277 | 5.0 | 170 | 0.6557 | 0.0556 | 0.6587 |
| 0.4781 | 6.0 | 204 | 0.7021 | 0.0673 | 0.6587 |
| 0.4279 | 7.0 | 238 | 0.7525 | 0.0628 | 0.6491 |
| 0.3883 | 8.0 | 272 | 0.7884 | 0.0834 | 0.6309 |
Base model
Hartunka/tiny_bert_km_10_v1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_10_v1_cola")