nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_data_aug_wnli_256 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_sa_GLUE_Experiment_data_aug_wnli_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_wnli_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_wnli_256", device_map="auto")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE WNLI 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 |
|---|---|---|---|---|
| 0.6528 | 1.0 | 435 | 2.3011 | 0.1549 |
| 0.4834 | 2.0 | 870 | 3.5400 | 0.0986 |
| 0.4353 | 3.0 | 1305 | 5.1022 | 0.1127 |
| 0.4022 | 4.0 | 1740 | 6.6353 | 0.1408 |
| 0.3757 | 5.0 | 2175 | 10.7943 | 0.0986 |
| 0.3399 | 6.0 | 2610 | 14.3396 | 0.0845 |