nyu-mll/glue
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How to use gokuls/add_BERT_48_sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/add_BERT_48_sst2") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/add_BERT_48_sst2", dtype="auto")This model is a fine-tuned version of gokuls/add_bert_12_layer_model_complete_training_new_48 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 |
|---|---|---|---|---|
| 0.4532 | 1.0 | 527 | 0.4410 | 0.8028 |
| 0.2621 | 2.0 | 1054 | 0.4674 | 0.8073 |
| 0.2037 | 3.0 | 1581 | 0.4985 | 0.8005 |
| 0.1699 | 4.0 | 2108 | 0.5998 | 0.7959 |
| 0.1441 | 5.0 | 2635 | 0.6110 | 0.7821 |
| 0.1238 | 6.0 | 3162 | 0.6674 | 0.8165 |