nyu-mll/glue
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How to use gokuls/add_BERT_24_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/add_BERT_24_stsb") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_24_stsb", dtype="auto")This model is a fine-tuned version of gokuls/add_bert_12_layer_model_complete_training_new 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 |
|---|---|---|---|---|---|---|
| 2.1466 | 1.0 | 45 | 2.2698 | 0.3930 | 0.3260 | 0.3595 |
| 1.4589 | 2.0 | 90 | 1.7726 | 0.5150 | 0.5141 | 0.5145 |
| 1.0006 | 3.0 | 135 | 1.4492 | 0.6090 | 0.6106 | 0.6098 |
| 0.6766 | 4.0 | 180 | 1.8200 | 0.5635 | 0.5672 | 0.5654 |
| 0.4849 | 5.0 | 225 | 2.1591 | 0.5213 | 0.5212 | 0.5213 |
| 0.3823 | 6.0 | 270 | 1.8541 | 0.5717 | 0.5716 | 0.5717 |
| 0.3158 | 7.0 | 315 | 1.8647 | 0.5777 | 0.5741 | 0.5759 |
| 0.2816 | 8.0 | 360 | 1.4985 | 0.6097 | 0.6083 | 0.6090 |