metadata
license: apache-2.0
base_model: agvidit1/DistilledBert_HateSpeech_pretrain
tags:
- generated_from_trainer
datasets:
- hate_speech18
metrics:
- accuracy
model-index:
- name: distilbert-hate_speech18
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: 0.8587751371115173
distilbert-hate_speech18
This model is a fine-tuned version of agvidit1/DistilledBert_HateSpeech_pretrain on the hate_speech18 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4684
- Accuracy: 0.8588
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: 5.050180626898551e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4404 | 1.0 | 240 | 0.4670 | 0.8533 |
| 0.4356 | 2.0 | 480 | 0.4642 | 0.8675 |
| 0.4303 | 3.0 | 720 | 0.4649 | 0.8748 |
| 0.4282 | 4.0 | 960 | 0.4694 | 0.8592 |
| 0.4273 | 5.0 | 1200 | 0.4638 | 0.8729 |
| 0.4256 | 6.0 | 1440 | 0.4651 | 0.8679 |
| 0.425 | 7.0 | 1680 | 0.4682 | 0.8560 |
| 0.4227 | 8.0 | 1920 | 0.4684 | 0.8588 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0