nyu-mll/glue
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How to use gokuls/mobilebert_add_GLUE_Experiment_logit_kd_cola_128 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_add_GLUE_Experiment_logit_kd_cola_128") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_cola_128")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_cola_128", device_map="auto")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE COLA 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 | Matthews Correlation |
|---|---|---|---|---|
| 0.827 | 1.0 | 67 | 0.6869 | 0.0 |
| 0.7971 | 2.0 | 134 | 0.6872 | 0.0 |
| 0.7968 | 3.0 | 201 | 0.6837 | 0.0 |
| 0.7956 | 4.0 | 268 | 0.6843 | 0.0 |
| 0.7957 | 5.0 | 335 | 0.6833 | 0.0 |
| 0.7934 | 6.0 | 402 | 0.6834 | 0.0 |
| 0.7822 | 7.0 | 469 | 0.7014 | 0.0 |
| 0.7636 | 8.0 | 536 | 0.6835 | 0.0 |
| 0.7509 | 9.0 | 603 | 0.6879 | 0.0 |
| 0.7482 | 10.0 | 670 | 0.6842 | 0.0 |