metadata
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv2_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: 0.6936274509803921
- name: F1
type: f1
value: 0.8085758039816232
hBERTv2_mrpc
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.5772
- Accuracy: 0.6936
- F1: 0.8086
- Combined Score: 0.7511
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 |
|---|---|---|---|---|---|---|
| 0.6388 | 1.0 | 15 | 0.6297 | 0.6838 | 0.8122 | 0.7480 |
| 0.612 | 2.0 | 30 | 0.6315 | 0.6887 | 0.8135 | 0.7511 |
| 0.5725 | 3.0 | 45 | 0.5772 | 0.6936 | 0.8086 | 0.7511 |
| 0.512 | 4.0 | 60 | 0.6261 | 0.7010 | 0.8152 | 0.7581 |
| 0.3924 | 5.0 | 75 | 0.6433 | 0.7279 | 0.8195 | 0.7737 |
| 0.2592 | 6.0 | 90 | 0.7531 | 0.6863 | 0.7594 | 0.7228 |
| 0.1689 | 7.0 | 105 | 0.7904 | 0.7377 | 0.8158 | 0.7768 |
| 0.1292 | 8.0 | 120 | 0.9954 | 0.7623 | 0.8381 | 0.8002 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2