---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: medium-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: 0.7754010695187166
    - name: F1
      type: f1
      value: 0.7745943137047872
---

<!-- 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. -->

# medium-vanilla-target-tweet

This model is a fine-tuned version of [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9845
- Accuracy: 0.7754
- F1: 0.7746

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4989        | 4.9   | 500  | 0.8358          | 0.7620   | 0.7589 |
| 0.0702        | 9.8   | 1000 | 1.3142          | 0.7674   | 0.7683 |
| 0.0233        | 14.71 | 1500 | 1.4760          | 0.7647   | 0.7650 |
| 0.015         | 19.61 | 2000 | 1.5151          | 0.7834   | 0.7841 |
| 0.0062        | 24.51 | 2500 | 1.6094          | 0.7968   | 0.7947 |
| 0.0113        | 29.41 | 3000 | 1.9273          | 0.7540   | 0.7537 |
| 0.0157        | 34.31 | 3500 | 2.0073          | 0.7433   | 0.7460 |
| 0.0124        | 39.22 | 4000 | 1.9845          | 0.7754   | 0.7746 |


### Framework versions

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