nyu-mll/glue
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How to use gokuls/hBERTv2_new_pretrain_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hBERTv2_new_pretrain_stsb") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hBERTv2_new_pretrain_stsb", device_map="auto")This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_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 |
|---|---|---|---|---|---|---|
| 2.399 | 1.0 | 45 | 2.6135 | 0.0824 | 0.0835 | 0.0830 |
| 1.9751 | 2.0 | 90 | 2.2332 | 0.2243 | 0.2144 | 0.2193 |
| 1.6719 | 3.0 | 135 | 2.3954 | 0.2796 | 0.2667 | 0.2732 |
| 1.3496 | 4.0 | 180 | 3.7160 | 0.3057 | 0.2958 | 0.3007 |
| 1.1653 | 5.0 | 225 | 2.7682 | 0.3327 | 0.3203 | 0.3265 |
| 0.8439 | 6.0 | 270 | 2.4277 | 0.3960 | 0.3888 | 0.3924 |
| 0.6687 | 7.0 | 315 | 2.3823 | 0.3995 | 0.3905 | 0.3950 |