nyu-mll/glue
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How to use gokuls/mobilebert_add_GLUE_Experiment_mnli with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_add_GLUE_Experiment_mnli") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_mnli")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_mnli")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE MNLI 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 |
|---|---|---|---|---|
| 1.0988 | 1.0 | 3068 | 1.0988 | 0.3182 |
| 1.0987 | 2.0 | 6136 | 1.0986 | 0.3184 |
| 1.0987 | 3.0 | 9204 | 1.0989 | 0.3274 |
| 1.0987 | 4.0 | 12272 | 1.0987 | 0.3182 |
| 1.0987 | 5.0 | 15340 | 1.0984 | 0.3545 |
| 1.0986 | 6.0 | 18408 | 1.0987 | 0.3274 |
| 1.0986 | 7.0 | 21476 | 1.0993 | 0.3274 |
| 1.0986 | 8.0 | 24544 | 1.0985 | 0.3545 |
| 1.0986 | 9.0 | 27612 | 1.0985 | 0.3545 |
| 1.0986 | 10.0 | 30680 | 1.0987 | 0.3182 |