nyu-mll/glue
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How to use gokuls/mobilebert_add_GLUE_Experiment_logit_kd_pretrain_sst2 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_pretrain_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_pretrain_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_pretrain_sst2", device_map="auto")This model is a fine-tuned version of gokuls/mobilebert_add_pre-training-complete 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 |
|---|---|---|---|---|
| 0.0 | 1.0 | 527 | nan | 0.4908 |
| 0.0 | 2.0 | 1054 | nan | 0.4908 |
| 0.0 | 3.0 | 1581 | nan | 0.4908 |
| 0.0 | 4.0 | 2108 | nan | 0.4908 |
| 0.0 | 5.0 | 2635 | nan | 0.4908 |
| 0.0 | 6.0 | 3162 | nan | 0.4908 |