---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: xlnet-base-mnli-finetuned
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9118695873662761
---

<!-- 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. -->

# xlnet-base-mnli-finetuned

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3456
- Accuracy: 0.9119

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.336         | 1.0   | 49087 | 0.3299          | 0.9010   |
| 0.2582        | 2.0   | 98174 | 0.3456          | 0.9119   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1