transformers pytorch bert 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
- f1
tags:
- generated_from_trainer
model-index:
- name: mini-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: 0.7540106951871658
      name: Accuracy
    - type: f1
      value: 0.7568814825340653
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mini-vanilla-target-tweet

This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5603
- Accuracy: 0.7540
- F1: 0.7569

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9285        | 4.9   | 500  | 0.7493          | 0.7273   | 0.7207 |
| 0.4468        | 9.8   | 1000 | 0.7630          | 0.7460   | 0.7437 |
| 0.2194        | 14.71 | 1500 | 0.8997          | 0.7406   | 0.7455 |
| 0.1062        | 19.61 | 2000 | 1.0822          | 0.7433   | 0.7435 |
| 0.0568        | 24.51 | 2500 | 1.2225          | 0.7620   | 0.7622 |
| 0.0439        | 29.41 | 3000 | 1.3475          | 0.7513   | 0.7527 |
| 0.0304        | 34.31 | 3500 | 1.4999          | 0.7433   | 0.7399 |
| 0.0247        | 39.22 | 4000 | 1.5603          | 0.7540   | 0.7569 |


### Framework versions

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