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
| v3 | v1 | |
|---|---|---|
| Base Model | bert-base-multilingual-cased | nlpaueb/legal-bert-small-uncased |
| Base Tokenizer | bert-base-multilingual-cased | bert-base-multilingual-cased |
| Framework | PyTorch | TensorFlow |
| Dataset Size | 3.0M | 2.68M |
| Train Split | 80% English20% English + 100% Multilingual | None |
| English Train Accuracy | 99.5% | N/A (≈97.5%) |
| Other Train Accuracy | 98.6% | 96.6% |
| Final Val Accuracy | 96.8% | 94.6% |
| Languages | 55 | N/A (≈35) |
| Hyperparameters | maxlen=208padding='max_length'batch_size=112optimizer=AdamWlearning_rate=1e-5loss=BCEWithLogitsLoss() | maxlen=192padding='max_length'batch_size=16optimizer=Adamlearning_rate=1e-5loss="binary_crossentropy" |
| Training Stopped | 7/20/2023 | 9/05/2022 |
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.
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.
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.