transformers pytorch tensorboard hybridbert text-classification generated_from_trainer en dataset:glue model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: add_BERT_48_rte
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE RTE
      type: glue
      config: rte
      split: validation
      args: rte
    metrics:
    - type: accuracy
      value: 0.5342960288808665
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# add_BERT_48_rte

This model is a fine-tuned version of [gokuls/add_bert_12_layer_model_complete_training_new_48](https://huggingface.co/gokuls/add_bert_12_layer_model_complete_training_new_48) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6914
- Accuracy: 0.5343

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7366        | 1.0   | 20   | 0.7002          | 0.4693   |
| 0.7043        | 2.0   | 40   | 0.6989          | 0.4621   |
| 0.7196        | 3.0   | 60   | 0.6963          | 0.5307   |
| 0.7123        | 4.0   | 80   | 0.7180          | 0.4729   |
| 0.7159        | 5.0   | 100  | 0.6933          | 0.4982   |
| 0.7033        | 6.0   | 120  | 0.7186          | 0.4729   |
| 0.7041        | 7.0   | 140  | 0.7036          | 0.4729   |
| 0.6992        | 8.0   | 160  | 0.6952          | 0.4693   |
| 0.7056        | 9.0   | 180  | 0.6920          | 0.5271   |
| 0.6988        | 10.0  | 200  | 0.6978          | 0.4621   |
| 0.6988        | 11.0  | 220  | 0.6917          | 0.5271   |
| 0.7047        | 12.0  | 240  | 0.7013          | 0.4729   |
| 0.7036        | 13.0  | 260  | 0.7099          | 0.4729   |
| 0.7018        | 14.0  | 280  | 0.6914          | 0.5343   |
| 0.694         | 15.0  | 300  | 0.6924          | 0.5271   |
| 0.6942        | 16.0  | 320  | 0.6961          | 0.5271   |
| 0.6948        | 17.0  | 340  | 0.6988          | 0.5018   |
| 0.6973        | 18.0  | 360  | 0.6930          | 0.5271   |
| 0.6886        | 19.0  | 380  | 0.7256          | 0.4621   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
