---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
base_model: google/bert_uncased_L-2_H-128_A-2
model-index:
- name: tiny-vanilla-target-tweet
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: emotion
      split: train
      args: emotion
    metrics:
    - type: accuracy
      value: 0.7032085561497327
      name: Accuracy
    - type: f1
      value: 0.704229444708009
      name: F1
---

<!-- 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. -->

# tiny-vanilla-target-tweet

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9887
- Accuracy: 0.7032
- F1: 0.7042

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1604        | 4.9   | 500  | 0.9784          | 0.6604   | 0.6290 |
| 0.7656        | 9.8   | 1000 | 0.8273          | 0.7139   | 0.6905 |
| 0.534         | 14.71 | 1500 | 0.8138          | 0.7219   | 0.7143 |
| 0.3832        | 19.61 | 2000 | 0.8591          | 0.7086   | 0.7050 |
| 0.2722        | 24.51 | 2500 | 0.9250          | 0.7112   | 0.7118 |
| 0.1858        | 29.41 | 3000 | 0.9887          | 0.7032   | 0.7042 |


### Framework versions

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