nyu-mll/glue
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How to use Hartunka/tiny_bert_rand_50_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_50_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_50_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_50_v2_cola")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_50_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_50_v2_cola")This model is a fine-tuned version of Hartunka/tiny_bert_rand_50_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 |
|---|---|---|---|---|---|
| 0.6135 | 1.0 | 34 | 0.6176 | 0.0 | 0.6913 |
| 0.6006 | 2.0 | 68 | 0.6197 | 0.0 | 0.6913 |
| 0.5776 | 3.0 | 102 | 0.6242 | 0.0372 | 0.6903 |
| 0.5383 | 4.0 | 136 | 0.6582 | 0.0622 | 0.6721 |
| 0.4936 | 5.0 | 170 | 0.6671 | 0.0857 | 0.6491 |
| 0.4569 | 6.0 | 204 | 0.7221 | 0.0817 | 0.6376 |
Base model
Hartunka/tiny_bert_rand_50_v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_50_v2_cola")