nyu-mll/glue
Viewer • Updated • 1.49M • 388k • 516
How to use Hartunka/tiny_bert_rand_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_rand_20_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_20_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_20_v2_cola")This model is a fine-tuned version of Hartunka/tiny_bert_rand_20_v2 on the GLUE COLA dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6153 | 1.0 | 34 | 0.6182 | 0.0 | 0.6913 |
| 0.601 | 2.0 | 68 | 0.6202 | 0.0 | 0.6913 |
| 0.5777 | 3.0 | 102 | 0.6294 | -0.0041 | 0.6846 |
| 0.5335 | 4.0 | 136 | 0.6736 | 0.0676 | 0.6750 |
| 0.4924 | 5.0 | 170 | 0.6791 | 0.0717 | 0.6462 |
| 0.4569 | 6.0 | 204 | 0.7303 | 0.0557 | 0.6395 |
Base model
Hartunka/tiny_bert_rand_20_v2