pytorch tensorboard albert text-classification dataset:glue transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-large-v2-finetuned-wnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: wnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5352112676056338
---

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# albert-large-v2-finetuned-wnli

This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6919
- Accuracy: 0.5352

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 17   | 0.7292          | 0.4366   |
| No log        | 2.0   | 34   | 0.6919          | 0.5352   |
| No log        | 3.0   | 51   | 0.7084          | 0.4648   |
| No log        | 4.0   | 68   | 0.7152          | 0.5352   |
| No log        | 5.0   | 85   | 0.7343          | 0.5211   |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
