transformers tensorboard safetensors bert text-classification generated_from_trainer en dataset:glue base_model:google/bert_uncased_L-4_H-256_A-4 base_model:finetune:google/bert_uncased_L-4_H-256_A-4 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/bert_uncased_L-4_H-256_A-4
datasets:
- glue
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_L-4_H-256_A-4_rte
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE RTE
      type: glue
      args: rte
    metrics:
    - type: accuracy
      value: 0.631768953068592
      name: Accuracy
---

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# bert_uncased_L-4_H-256_A-4_rte

This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6545
- Accuracy: 0.6318

## 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
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6982        | 1.0   | 10   | 0.6899          | 0.5451   |
| 0.6864        | 2.0   | 20   | 0.6845          | 0.5523   |
| 0.6733        | 3.0   | 30   | 0.6737          | 0.5884   |
| 0.6495        | 4.0   | 40   | 0.6554          | 0.5884   |
| 0.61          | 5.0   | 50   | 0.6573          | 0.6101   |
| 0.5697        | 6.0   | 60   | 0.6545          | 0.6318   |
| 0.5279        | 7.0   | 70   | 0.6648          | 0.6354   |
| 0.4859        | 8.0   | 80   | 0.6778          | 0.6173   |
| 0.4524        | 9.0   | 90   | 0.6933          | 0.6137   |
| 0.4126        | 10.0  | 100  | 0.6992          | 0.6245   |
| 0.386         | 11.0  | 110  | 0.7181          | 0.6426   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
