nyu-mll/glue
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How to use gokuls/add_BERT_no_pretrain_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/add_BERT_no_pretrain_stsb") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_no_pretrain_stsb", device_map="auto")This model is a fine-tuned version of on the GLUE STSB 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 | Pearson | Spearmanr | Combined Score |
|---|---|---|---|---|---|---|
| 2.4937 | 1.0 | 45 | 2.6373 | 0.0410 | 0.0287 | 0.0348 |
| 2.22 | 2.0 | 90 | 2.4288 | 0.0740 | 0.0586 | 0.0663 |
| 2.1554 | 3.0 | 135 | 2.3869 | 0.0609 | 0.0498 | 0.0554 |
| 2.0556 | 4.0 | 180 | 2.5740 | 0.0800 | 0.0717 | 0.0759 |
| 2.0221 | 5.0 | 225 | 2.4656 | 0.0849 | 0.0654 | 0.0752 |
| 2.0403 | 6.0 | 270 | 2.3307 | 0.0719 | 0.0575 | 0.0647 |
| 2.1732 | 7.0 | 315 | 2.5174 | 0.0699 | 0.0584 | 0.0641 |
| 2.0399 | 8.0 | 360 | 2.5648 | 0.0718 | 0.0605 | 0.0662 |
| 2.0765 | 9.0 | 405 | 2.3373 | 0.0621 | 0.0491 | 0.0556 |
| 2.0538 | 10.0 | 450 | 2.6402 | 0.0463 | 0.0431 | 0.0447 |
| 2.0147 | 11.0 | 495 | 2.4727 | 0.0540 | 0.0471 | 0.0506 |