nyu-mll/glue
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How to use gokuls/hBERTv2_data_aug_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hBERTv2_data_aug_stsb") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hBERTv2_data_aug_stsb", dtype="auto")This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 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 |
|---|---|---|---|---|---|---|
| 0.6302 | 1.0 | 1259 | 2.1357 | 0.5181 | 0.5131 | 0.5156 |
| 0.0973 | 2.0 | 2518 | 2.4678 | 0.4495 | 0.4283 | 0.4389 |
| 0.0514 | 3.0 | 3777 | 2.3102 | 0.4101 | 0.3922 | 0.4011 |
| 0.0384 | 4.0 | 5036 | 2.5410 | 0.4446 | 0.4376 | 0.4411 |
| 0.031 | 5.0 | 6295 | 2.4586 | 0.4091 | 0.3917 | 0.4004 |
| 0.0255 | 6.0 | 7554 | 2.5981 | 0.3998 | 0.3874 | 0.3936 |