one-for-all-toxicity-v3

Link to the distilbert spam defender

Find the v1 (TensorFlow) model in SavedModel format on this page. The license for the v1 model is Apache 2.0

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I manually annotated more data on top of Toxi Text 3M and added them to the training set. Training on Toxi Text 3M alone results in a biased model that classifies short text with lower precision.

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Models tested for v2: roberta, xlm-roberta, bert-small, bert-base-cased/uncased, bert-multilingual-cased/uncased, and alberta-large-v2. Of these, I chose bert-multilingual-cased because it performs better with the same amount of resources as the others for this particular task.

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PyTorch

text = "hello world!"

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("FredZhang7/one-for-all-toxicity-v3")
model = AutoModelForSequenceClassification.from_pretrained("FredZhang7/one-for-all-toxicity-v3").to(device)

encoding = tokenizer.encode_plus(
    text,
    add_special_tokens=True,
    max_length=208,
    padding="max_length",
    truncation=True,
    return_tensors="pt"
)
print('device:', device)
input_ids = encoding["input_ids"].to(device)
attention_mask = encoding["attention_mask"].to(device)

with torch.no_grad():
    outputs = model(input_ids, attention_mask=attention_mask)
    logits = outputs.logits
    predicted_labels = torch.argmax(logits, dim=1)

print(predicted_labels)

Attribution

• If you distribute, remix, adapt, or build upon One-for-all Toxicity v3, please credit "AIstrova Technologies Inc." in your README.md, application description, research, or website.