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---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv1_mrpc
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: 0.6862745098039216
- name: F1
type: f1
value: 0.7999999999999999
---
<!-- 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. -->
# hBERTv1_mrpc
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1](https://huggingface.co/gokuls/bert_12_layer_model_v1) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6051
- Accuracy: 0.6863
- F1: 0.8000
- Combined Score: 0.7431
## 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: 256
- eval_batch_size: 256
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6536 | 1.0 | 15 | 0.6243 | 0.6838 | 0.8122 | 0.7480 |
| 0.6275 | 2.0 | 30 | 0.6174 | 0.7010 | 0.8117 | 0.7564 |
| 0.6129 | 3.0 | 45 | 0.6089 | 0.6961 | 0.8182 | 0.7571 |
| 0.6087 | 4.0 | 60 | 0.6062 | 0.6887 | 0.8130 | 0.7508 |
| 0.5939 | 5.0 | 75 | 0.6104 | 0.6863 | 0.7935 | 0.7399 |
| 0.5707 | 6.0 | 90 | 0.6184 | 0.7083 | 0.8183 | 0.7633 |
| 0.5426 | 7.0 | 105 | 0.6051 | 0.6863 | 0.8000 | 0.7431 |
| 0.4819 | 8.0 | 120 | 0.6560 | 0.6936 | 0.8019 | 0.7478 |
| 0.4279 | 9.0 | 135 | 0.6673 | 0.6887 | 0.7678 | 0.7283 |
| 0.3374 | 10.0 | 150 | 0.8092 | 0.6863 | 0.7902 | 0.7382 |
| 0.2789 | 11.0 | 165 | 0.9342 | 0.6887 | 0.7935 | 0.7411 |
| 0.2216 | 12.0 | 180 | 0.9708 | 0.6838 | 0.7810 | 0.7324 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
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