nyu-mll/glue
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How to use gokuls/sa_BERT_48_sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/sa_BERT_48_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/sa_BERT_48_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/sa_BERT_48_sst2")This model is a fine-tuned version of gokuls/bert_base_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 |
|---|---|---|---|---|
| 0.36 | 1.0 | 702 | 0.4556 | 0.8028 |
| 0.2158 | 2.0 | 1404 | 0.5677 | 0.8039 |
| 0.1674 | 3.0 | 2106 | 0.5359 | 0.8119 |
| 0.1358 | 4.0 | 2808 | 0.6864 | 0.7878 |
| 0.11 | 5.0 | 3510 | 0.4984 | 0.8085 |
| 0.0927 | 6.0 | 4212 | 0.6800 | 0.7856 |