---
license: apache-2.0
base_model: agvidit1/GoogleTinyBert_HateSpeech_pretrain
tags:
- generated_from_trainer
datasets:
- hate_speech18
metrics:
- accuracy
model-index:
- name: berttiny-hate_speech18-bothpretrained
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hate_speech18
      type: hate_speech18
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.879798903107861
---

<!-- 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. -->

# berttiny-hate_speech18-bothpretrained

This model is a fine-tuned version of [agvidit1/GoogleTinyBert_HateSpeech_pretrain](https://huggingface.co/agvidit1/GoogleTinyBert_HateSpeech_pretrain) on the hate_speech18 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4682
- Accuracy: 0.8798

## 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: 7.12739424754752e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 34
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4804        | 1.0   | 479  | 0.4778          | 0.8624   |
| 0.4592        | 2.0   | 958  | 0.4688          | 0.8761   |
| 0.4471        | 3.0   | 1437 | 0.4681          | 0.8780   |
| 0.4405        | 4.0   | 1916 | 0.4682          | 0.8798   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0