---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_new_no_pretrain_rte
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE RTE
      type: glue
      config: rte
      split: validation
      args: rte
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5306859205776173
---

<!-- 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. -->

# hBERTv2_new_no_pretrain_rte

This model is a fine-tuned version of [](https://huggingface.co/) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6981
- Accuracy: 0.5307

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7697        | 1.0   | 20   | 0.7526          | 0.5271   |
| 0.7285        | 2.0   | 40   | 0.7208          | 0.5271   |
| 0.7201        | 3.0   | 60   | 0.7112          | 0.5343   |
| 0.7043        | 4.0   | 80   | 0.6981          | 0.5307   |
| 0.6569        | 5.0   | 100  | 0.7251          | 0.5235   |
| 0.5762        | 6.0   | 120  | 0.8571          | 0.4765   |
| 0.4336        | 7.0   | 140  | 0.9540          | 0.4765   |
| 0.3299        | 8.0   | 160  | 1.2464          | 0.4838   |
| 0.2561        | 9.0   | 180  | 1.4299          | 0.5018   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3