---
license: mit
tags:
- generated_from_trainer
datasets:
- tweets_hate_speech_detection
metrics:
- accuracy
- f1
model-index:
- name: FirstTry
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweets_hate_speech_detection
      type: tweets_hate_speech_detection
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9759098967567004
    - name: F1
      type: f1
      value: 0.8034042553191489
---

<!-- 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. -->

# FirstTry

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweets_hate_speech_detection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0977
- Accuracy: 0.9759
- F1: 0.8034

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.04  | 50   | 0.2125          | 0.9337   | 0.0    |
| No log        | 0.07  | 100  | 0.2210          | 0.9341   | 0.0125 |
| No log        | 0.11  | 150  | 0.1832          | 0.9554   | 0.5103 |
| No log        | 0.14  | 200  | 0.1539          | 0.9583   | 0.6377 |
| No log        | 0.18  | 250  | 0.2435          | 0.9523   | 0.4434 |
| No log        | 0.21  | 300  | 0.1818          | 0.9589   | 0.5736 |
| No log        | 0.25  | 350  | 0.1138          | 0.9618   | 0.7136 |
| No log        | 0.29  | 400  | 0.1045          | 0.9667   | 0.7243 |
| No log        | 0.32  | 450  | 0.0958          | 0.9676   | 0.7330 |
| 0.1788        | 0.36  | 500  | 0.0935          | 0.9695   | 0.7306 |
| 0.1788        | 0.39  | 550  | 0.1289          | 0.9666   | 0.7178 |
| 0.1788        | 0.43  | 600  | 0.1039          | 0.9648   | 0.7507 |
| 0.1788        | 0.46  | 650  | 0.1234          | 0.9646   | 0.6435 |
| 0.1788        | 0.5   | 700  | 0.0984          | 0.9703   | 0.7725 |
| 0.1788        | 0.54  | 750  | 0.1364          | 0.9702   | 0.7185 |
| 0.1788        | 0.57  | 800  | 0.1004          | 0.9739   | 0.7792 |
| 0.1788        | 0.61  | 850  | 0.0998          | 0.9684   | 0.7616 |
| 0.1788        | 0.64  | 900  | 0.1068          | 0.9738   | 0.7857 |
| 0.1788        | 0.68  | 950  | 0.1206          | 0.9732   | 0.7644 |
| 0.1198        | 0.71  | 1000 | 0.0977          | 0.9759   | 0.8034 |
| 0.1198        | 0.75  | 1050 | 0.0864          | 0.9742   | 0.7916 |
| 0.1198        | 0.79  | 1100 | 0.1297          | 0.9727   | 0.7849 |
| 0.1198        | 0.82  | 1150 | 0.0969          | 0.9751   | 0.8026 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.1
- Datasets 2.10.1
- Tokenizers 0.13.3