nyu-mll/glue
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How to use Hartunka/tiny_bert_km_5_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_5_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_5_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_5_v2_cola")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_5_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_5_v2_cola")This model is a fine-tuned version of Hartunka/tiny_bert_km_5_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 |
|---|---|---|---|---|---|
| 0.6164 | 1.0 | 34 | 0.6186 | 0.0 | 0.6913 |
| 0.6071 | 2.0 | 68 | 0.6137 | 0.0 | 0.6913 |
| 0.6025 | 3.0 | 102 | 0.6132 | 0.0 | 0.6913 |
| 0.5865 | 4.0 | 136 | 0.6316 | 0.0464 | 0.6922 |
| 0.5525 | 5.0 | 170 | 0.6342 | 0.1771 | 0.7028 |
| 0.5122 | 6.0 | 204 | 0.6610 | 0.0883 | 0.6318 |
| 0.4796 | 7.0 | 238 | 0.7213 | 0.0713 | 0.6779 |
| 0.446 | 8.0 | 272 | 0.7225 | 0.0664 | 0.6433 |
Base model
Hartunka/tiny_bert_km_5_v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_5_v2_cola")