---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: base-vanilla-target-tweet
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: emotion
      split: train
      args: emotion
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7780748663101604
    - name: F1
      type: f1
      value: 0.7772664883136655
---

<!-- 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. -->

# base-vanilla-target-tweet

This model is a fine-tuned version of [google/bert_uncased_L-12_H-768_A-12](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8380
- Accuracy: 0.7781
- F1: 0.7773

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3831        | 4.9   | 500  | 0.9800          | 0.7807   | 0.7785 |
| 0.0414        | 9.8   | 1000 | 1.4175          | 0.7754   | 0.7765 |
| 0.015         | 14.71 | 1500 | 1.6411          | 0.7754   | 0.7708 |
| 0.0166        | 19.61 | 2000 | 1.5930          | 0.7941   | 0.7938 |
| 0.0175        | 24.51 | 2500 | 1.3934          | 0.7888   | 0.7852 |
| 0.0191        | 29.41 | 3000 | 1.9407          | 0.7647   | 0.7658 |
| 0.0137        | 34.31 | 3500 | 1.8380          | 0.7781   | 0.7773 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2