---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-tweet_eval-emotion
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-tweet_eval-emotion

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: 0.6404
- Accuracy: 0.6529
- Precision: 0.8110
- Recall: 0.6529
- F1: 0.6507
- Auroc: 0.9184

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auroc  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.7228        | 0.55  | 500  | 0.7232          | 0.6030   | 0.5625    | 0.6030 | 0.5760 | 0.8937 |
| 0.64          | 1.1   | 1000 | 0.6404          | 0.6529   | 0.8110    | 0.6529 | 0.6507 | 0.9184 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1