nyu-mll/glue
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How to use Hartunka/tiny_bert_rand_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_rand_5_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_cola")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_cola")This model is a fine-tuned version of Hartunka/tiny_bert_rand_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 |
|---|---|---|---|---|---|
| 0.6181 | 1.0 | 34 | 0.6160 | 0.0 | 0.6913 |
| 0.603 | 2.0 | 68 | 0.6146 | 0.0 | 0.6913 |
| 0.5863 | 3.0 | 102 | 0.6145 | 0.0071 | 0.6855 |
| 0.5473 | 4.0 | 136 | 0.6423 | 0.1090 | 0.6874 |
| 0.5062 | 5.0 | 170 | 0.6451 | 0.1031 | 0.6663 |
| 0.4682 | 6.0 | 204 | 0.7057 | 0.0943 | 0.6596 |
| 0.4321 | 7.0 | 238 | 0.7519 | 0.0915 | 0.6357 |
| 0.4043 | 8.0 | 272 | 0.7743 | 0.0624 | 0.6232 |
Base model
Hartunka/tiny_bert_rand_5_v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_5_v2_cola")