transformers tensorboard safetensors xlnet text-classification generated_from_trainer dataset:emotion base_model:xlnet/xlnet-base-cased base_model:finetune:xlnet/xlnet-base-cased license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: xlnet/xlnet-base-cased
datasets:
- emotion
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: emotion_xlnet
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9155
      name: Accuracy
---

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# emotion_xlnet

This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co/xlnet/xlnet-base-cased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2021
- Accuracy: 0.9155

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 250  | 0.3639          | 0.8715   |
| 0.5892        | 2.0   | 500  | 0.2404          | 0.911    |
| 0.5892        | 3.0   | 750  | 0.2102          | 0.9175   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
