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---
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
|