File size: 5,694 Bytes
f62cb7b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
---
license: apache-2.0
datasets:
- ruanchaves/hatebr
language:
- pt
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- hate-speech
widget:
- text: "Não concordo com a sua opinião."
example_title: Exemplo
- text: "Pega a sua opinião e vai a merda com ela!"
example_title: Exemplo
---
# TeenyTinyLlama-460m-HateBR
TeenyTinyLlama is a series of small foundational models trained in Brazilian Portuguese.
This repository contains a version of [TeenyTinyLlama-460m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m) (`TeenyTinyLlama-460m-HateBR`) fine-tuned on the [HateBR dataset](https://huggingface.co/datasets/ruanchaves/hatebr).
## Details
- **Number of Epochs:** 3
- **Batch size:** 16
- **Optimizer:** `torch.optim.AdamW` (learning_rate = 4e-5, epsilon = 1e-8)
- **GPU:** 1 NVIDIA A100-SXM4-40GB
## Usage
Using `transformers.pipeline`:
```python
from transformers import pipeline
text = "Pega a sua opinião e vai a merda com ela!"
classifier = pipeline("text-classification", model="nicholasKluge/TeenyTinyLlama-460m-HateBR")
classifier(text)
# >>> [{'label': 'TOXIC', 'score': 0.9998729228973389}]
```
## Reproducing
To reproduce the fine-tuning process, use the following code snippet:
```python
# Hatebr
! pip install transformers datasets evaluate accelerate -q
import evaluate
import numpy as np
from huggingface_hub import login
from datasets import load_dataset, Dataset, DatasetDict
from transformers import AutoTokenizer, DataCollatorWithPadding
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
# Load the task
dataset = load_dataset("ruanchaves/hatebr")
# Format the dataset
train = dataset['train'].to_pandas()
train = train[['instagram_comments', 'offensive_language']]
train.columns = ['text', 'labels']
train.labels = train.labels.astype(int)
train = Dataset.from_pandas(train)
test = dataset['test'].to_pandas()
test = test[['instagram_comments', 'offensive_language']]
test.columns = ['text', 'labels']
test.labels = test.labels.astype(int)
test = Dataset.from_pandas(test)
dataset = DatasetDict({
"train": train,
"test": test
})
# Create a `ModelForSequenceClassification`
model = AutoModelForSequenceClassification.from_pretrained(
"nicholasKluge/TeenyTinyLlama-460m",
num_labels=2,
id2label={0: "NONTOXIC", 1: "TOXIC"},
label2id={"NONTOXIC": 0, "TOXIC": 1}
)
tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-460m")
# Preprocess the dataset
def preprocess_function(examples):
return tokenizer(examples["text"], truncation=True)
dataset_tokenized = dataset.map(preprocess_function, batched=True)
# Create a simple data collactor
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# Use accuracy as evaluation metric
accuracy = evaluate.load("accuracy")
# Function to compute accuracy
def compute_metrics(eval_pred):
predictions, labels = eval_pred
predictions = np.argmax(predictions, axis=1)
return accuracy.compute(predictions=predictions, references=labels)
# Define training arguments
training_args = TrainingArguments(
output_dir="checkpoints",
learning_rate=4e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
weight_decay=0.01,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
push_to_hub=True,
hub_token="your_token_here",
hub_model_id="username/model-ID",
)
# Define the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset_tokenized["train"],
eval_dataset=dataset_tokenized["test"],
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics,
)
# Train!
trainer.train()
```
## Fine-Tuning Comparisons
| Models | [HateBr](https://huggingface.co/datasets/ruanchaves/hatebr) |
|--------------------------------------------------------------------------------------------|-------------------------------------------------------------|
| [Teeny Tiny Llama 460m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m) | 91.64 |
| [Bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased)| 91.57 |
| [Bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) | 91.28 |
| [Teeny Tiny Llama 160m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m) | 90.71 |
| [Gpt2-small-portuguese](https://huggingface.co/pierreguillou/gpt2-small-portuguese) | 87.42 |
## Cite as 🤗
```latex
@misc{nicholas22llama,
doi = {10.5281/zenodo.6989727},
url = {https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m},
author = {Nicholas Kluge Corrêa},
title = {TeenyTinyLlama},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
}
```
## Funding
This repository was built as part of the RAIES ([Rede de Inteligência Artificial Ética e Segura](https://www.raies.org/)) initiative, a project supported by FAPERGS - ([Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul](https://fapergs.rs.gov.br/inicial)), Brazil.
## License
TeenyTinyLlama-460m-HateBR is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details. |