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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_add_GLUE_Experiment_mrpc_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.6838235294117647
- name: F1
type: f1
value: 0.8122270742358079
---
<!-- 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_mrpc_256
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6207
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480
## 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 | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6419 | 1.0 | 29 | 0.6266 | 0.6838 | 0.8122 | 0.7480 |
| 0.6297 | 2.0 | 58 | 0.6236 | 0.6838 | 0.8122 | 0.7480 |
| 0.6307 | 3.0 | 87 | 0.6241 | 0.6838 | 0.8122 | 0.7480 |
| 0.63 | 4.0 | 116 | 0.6243 | 0.6838 | 0.8122 | 0.7480 |
| 0.6283 | 5.0 | 145 | 0.6219 | 0.6838 | 0.8122 | 0.7480 |
| 0.6243 | 6.0 | 174 | 0.6207 | 0.6838 | 0.8122 | 0.7480 |
| 0.6206 | 7.0 | 203 | 0.6346 | 0.6838 | 0.8122 | 0.7480 |
| 0.6034 | 8.0 | 232 | 0.6519 | 0.6348 | 0.7545 | 0.6947 |
| 0.5877 | 9.0 | 261 | 0.6375 | 0.6838 | 0.8122 | 0.7480 |
| 0.5722 | 10.0 | 290 | 0.6446 | 0.6299 | 0.7504 | 0.6902 |
| 0.5619 | 11.0 | 319 | 0.6733 | 0.6814 | 0.8105 | 0.7459 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
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