transformers pytorch tensorboard distilbert text-classification generated_from_trainer dataset:tweet_eval license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- tweet_eval
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: tweet_emotions_classifier
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: emotion
      split: test
      args: emotion
    metrics:
    - type: accuracy
      value: 0.7914438502673797
      name: Accuracy
---

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# Tweet Emotion Classifier

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6042
- Accuracy: 0.7914

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8464        | 1.0   | 408  | 0.6180          | 0.7931   |
| 0.4583        | 2.0   | 816  | 0.5700          | 0.8037   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
