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---
language:
- en
base_model: gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv1_new_pretrain_w_init_48_ver2_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: 0.7181372549019608
- name: F1
type: f1
value: 0.8099173553719009
---
<!-- 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_new_pretrain_w_init_48_ver2_mrpc
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5916
- Accuracy: 0.7181
- F1: 0.8099
- Combined Score: 0.7640
## 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: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6666 | 1.0 | 58 | 0.6274 | 0.6912 | 0.8006 | 0.7459 |
| 0.6239 | 2.0 | 116 | 0.5916 | 0.7181 | 0.8099 | 0.7640 |
| 0.5981 | 3.0 | 174 | 0.6532 | 0.6225 | 0.7004 | 0.6615 |
| 0.518 | 4.0 | 232 | 0.6251 | 0.7108 | 0.8059 | 0.7584 |
| 0.3848 | 5.0 | 290 | 0.7553 | 0.6814 | 0.7869 | 0.7341 |
| 0.2708 | 6.0 | 348 | 1.0696 | 0.6838 | 0.7994 | 0.7416 |
| 0.2062 | 7.0 | 406 | 1.2159 | 0.6103 | 0.7145 | 0.6624 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.1
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