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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_add_GLUE_Experiment_sst2_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.7981651376146789
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_add_GLUE_Experiment_sst2_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4543
- Accuracy: 0.7982
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6677 | 1.0 | 527 | 0.6771 | 0.5757 |
| 0.5966 | 2.0 | 1054 | 0.7135 | 0.5424 |
| 0.5714 | 3.0 | 1581 | 0.7271 | 0.5550 |
| 0.5573 | 4.0 | 2108 | 0.6892 | 0.5619 |
| 0.501 | 5.0 | 2635 | 0.4546 | 0.7798 |
| 0.2856 | 6.0 | 3162 | 0.4613 | 0.8050 |
| 0.2288 | 7.0 | 3689 | 0.4543 | 0.7982 |
| 0.2027 | 8.0 | 4216 | 0.4662 | 0.7993 |
| 0.1883 | 9.0 | 4743 | 0.5168 | 0.8039 |
| 0.1779 | 10.0 | 5270 | 0.5748 | 0.7856 |
| 0.1691 | 11.0 | 5797 | 0.5196 | 0.8028 |
| 0.1596 | 12.0 | 6324 | 0.5943 | 0.7947 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
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