Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mginoben/roberta-tagalog-profanity-classifier")
model = AutoModelForSequenceClassification.from_pretrained("mginoben/roberta-tagalog-profanity-classifier")Quick Links
YAML Metadata Error:"base_model" with value "https://huggingface.co/jcblaise/roberta-tagalog-base" is not valid. Use a model id from https://hf.co/models.
roberta-tagalog-profanity-classifier
This model is a fine-tuned version of jcblaise/roberta-tagalog-base on mginoben/tagalog-profanity-dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.3019
- Accuracy: 0.8898
- Precision: 0.8523
- Recall: 0.8944
- F1: 0.8728
Model description
The Model classifies tagalog texts that contains profanities as either Abusive or Non-Abusive.
It only classifies texts with the following profanities:
- bobo
- bwiset
- gago
- kupal
- pakshet
- pakyu
- pucha
- punyeta
- puta
- putangina
- tanga
- tangina
- tarantado
- ulol
Intended uses & limitations
For content moderation accross different social medias
Training and evaluation data
- Training: 11,110
- Validation: 2,778
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 174 | 0.3006 | 0.8776 | 0.8620 | 0.8458 | 0.8538 |
| No log | 2.0 | 348 | 0.2899 | 0.8834 | 0.8801 | 0.8382 | 0.8586 |
| 0.2993 | 3.0 | 522 | 0.2869 | 0.8873 | 0.8491 | 0.8918 | 0.8700 |
| 0.2993 | 4.0 | 696 | 0.3019 | 0.8898 | 0.8523 | 0.8944 | 0.8728 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Downloads last month
- 3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mginoben/roberta-tagalog-profanity-classifier")