nyu-mll/glue
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How to use Hartunka/tiny_bert_rand_20_v1_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_20_v1_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_20_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_20_v1_cola")This model is a fine-tuned version of Hartunka/tiny_bert_rand_20_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 |
|---|---|---|---|---|---|
| 0.6196 | 1.0 | 34 | 0.6181 | 0.0 | 0.6913 |
| 0.5915 | 2.0 | 68 | 0.6220 | 0.0438 | 0.6922 |
| 0.5622 | 3.0 | 102 | 0.6283 | 0.0719 | 0.6894 |
| 0.5173 | 4.0 | 136 | 0.6516 | 0.1082 | 0.6740 |
| 0.461 | 5.0 | 170 | 0.7096 | 0.0573 | 0.6443 |
| 0.4116 | 6.0 | 204 | 0.7408 | 0.0593 | 0.6405 |
Base model
Hartunka/tiny_bert_rand_20_v1