nyu-mll/glue
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How to use gokuls/add_BERT_24_sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/add_BERT_24_sst2") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_24_sst2", dtype="auto")This model is a fine-tuned version of gokuls/add_bert_12_layer_model_complete_training_new on the GLUE SST2 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 | Accuracy |
|---|---|---|---|---|
| 0.537 | 1.0 | 527 | 0.4505 | 0.7844 |
| 0.2966 | 2.0 | 1054 | 0.5377 | 0.8073 |
| 0.2313 | 3.0 | 1581 | 0.4930 | 0.8016 |
| 0.1989 | 4.0 | 2108 | 0.5843 | 0.7959 |
| 0.1696 | 5.0 | 2635 | 0.5200 | 0.8039 |
| 0.1491 | 6.0 | 3162 | 0.6076 | 0.7856 |