nyu-mll/glue
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How to use gokuls/hBERTv2_new_pretrain_w_init__stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hBERTv2_new_pretrain_w_init__stsb") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hBERTv2_new_pretrain_w_init__stsb", device_map="auto")This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_wt_init 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 |
|---|---|---|---|---|---|---|
| 2.2654 | 1.0 | 45 | 2.4836 | 0.2041 | 0.1912 | 0.1976 |
| 1.9657 | 2.0 | 90 | 2.1138 | 0.2744 | 0.2547 | 0.2646 |
| 1.6665 | 3.0 | 135 | 2.2375 | 0.3087 | 0.3002 | 0.3044 |
| 1.3265 | 4.0 | 180 | 2.0270 | 0.3743 | 0.3670 | 0.3707 |
| 1.0731 | 5.0 | 225 | 2.3748 | 0.3294 | 0.3212 | 0.3253 |
| 0.7974 | 6.0 | 270 | 2.6753 | 0.3338 | 0.3353 | 0.3345 |
| 0.6738 | 7.0 | 315 | 2.5125 | 0.3590 | 0.3464 | 0.3527 |
| 0.5384 | 8.0 | 360 | 2.3740 | 0.3310 | 0.3211 | 0.3261 |
| 0.4589 | 9.0 | 405 | 2.3911 | 0.3709 | 0.3690 | 0.3699 |