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import streamlit as st
import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM
import os
import time
# Model Architecture
class TinyTransformer(nn.Module):
def __init__(self, vocab_size, embed_dim, num_heads, ff_dim, num_layers):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim)
self.pos_encoding = nn.Parameter(torch.zeros(1, 512, embed_dim))
encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dim_feedforward=ff_dim, batch_first=True)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
self.fc = nn.Linear(embed_dim, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.embedding(x) + self.pos_encoding[:, :x.size(1), :]
x = self.transformer(x)
x = x.mean(dim=1) # Global average pooling
x = self.fc(x)
return self.sigmoid(x)
class TinyTransformerConfig(PretrainedConfig):
model_type = "tiny_transformer"
def __init__(
self,
vocab_size=30522,
embed_dim=64,
num_heads=2,
ff_dim=128,
num_layers=4,
max_position_embeddings=512,
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.embed_dim = embed_dim
self.num_heads = num_heads
self.ff_dim = ff_dim
self.num_layers = num_layers
self.max_position_embeddings = max_position_embeddings
class TinyTransformerForSequenceClassification(PreTrainedModel):
config_class = TinyTransformerConfig
def __init__(self, config):
super().__init__(config)
self.num_labels = 1
self.transformer = TinyTransformer(
config.vocab_size,
config.embed_dim,
config.num_heads,
config.ff_dim,
config.num_layers
)
def forward(self, input_ids, attention_mask=None):
outputs = self.transformer(input_ids)
return {"logits": outputs}
# Load models and tokenizers
@st.cache_resource
def load_models_and_tokenizers():
device = torch.device("cpu") # forcing CPU as overhead of inference on GPU slows down the inference
models = {}
tokenizers = {}
# Load Tiny-toxic-detector
config = TinyTransformerConfig.from_pretrained("AssistantsLab/Tiny-Toxic-Detector")
models["Tiny-toxic-detector"] = TinyTransformerForSequenceClassification.from_pretrained("AssistantsLab/Tiny-Toxic-Detector", config=config).to(device)
tokenizers["Tiny-toxic-detector"] = AutoTokenizer.from_pretrained("AssistantsLab/Tiny-Toxic-Detector")
# Load other models
model_configs = [
("s-nlp/roberta_toxicity_classifier", AutoModelForSequenceClassification, "s-nlp/roberta_toxicity_classifier"),
("martin-ha/toxic-comment-model", AutoModelForSequenceClassification, "martin-ha/toxic-comment-model"),
("lmsys/toxicchat-t5-large-v1.0", AutoModelForSeq2SeqLM, "t5-large")
]
for model_name, model_class, tokenizer_name in model_configs:
models[model_name] = model_class.from_pretrained(model_name).to(device)
tokenizers[model_name] = AutoTokenizer.from_pretrained(tokenizer_name)
return models, tokenizers, device
# Prediction function
def predict_toxicity(text, model, tokenizer, device, model_name):
start_time = time.time()
if model_name == "lmsys/toxicchat-t5-large-v1.0":
prefix = "ToxicChat: "
inputs = tokenizer(prefix + text, return_tensors="pt", max_length=512, truncation=True).to(device)
with torch.no_grad():
outputs = model.generate(**inputs)
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).strip().lower()
else:
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128, padding="max_length").to(device)
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
with torch.no_grad():
outputs = model(**inputs)
if model_name == "Tiny-toxic-detector":
logits = outputs["logits"].squeeze()
prediction = "Toxic" if logits > 0.5 else "Not Toxic"
else:
logits = outputs.logits.squeeze()
prediction = "Toxic" if logits[1] > logits[0] else "Not Toxic"
end_time = time.time()
inference_time = end_time - start_time
return prediction, inference_time
def main():
st.set_page_config(page_title="Toxicity Detector Model Comparison", layout="wide")
st.title("Toxicity Detector Model Comparison")
# Explanation text
st.markdown("""
### How It Works
This application compares various toxicity detection models to classify whether a given text is toxic or not. The models being compared include:
- [**Tiny-Toxic-Detector**](https://huggingface.co/AssistantsLab/Tiny-Toxic-Detector): A 2M parameter model with a new architecture released by [AssistantsLab](https://huggingface.co/AssistantsLab).
- [**RoBERTa-Toxicity-Classifier**](s-nlp/roberta_toxicity_classifier): A 124M parameter RoBERTa-based model.
- [**Toxic-Comment-Model**](https://huggingface.co/martin-ha/toxic-comment-model): A 67M parameter DistilBERT-based model.
- [**ToxicChat-T5**](https://huggingface.co/lmsys/toxicchat-t5-large-v1.0): A 738M parameter T5-based model.
Simply enter the text you want to classify, and the app will provide the predictions from each model, along with the inference time.
Please note these models are (mostly) English-only.
""")
# Load models
models, tokenizers, device = load_models_and_tokenizers()
# Reorder the models dictionary so that "Tiny-toxic-detector" is last
model_names = sorted(models.keys(), key=lambda x: x == "Tiny-toxic-detector")
# User input
text = st.text_area("Enter text to classify:", height=150)
if st.button("Classify"):
if text:
progress_bar = st.progress(0)
results = []
for i, model_name in enumerate(model_names):
with st.spinner(f"Classifying with {model_name}..."):
prediction, inference_time = predict_toxicity(text, models[model_name], tokenizers[model_name], device, model_name)
results.append((model_name, prediction, inference_time))
progress_bar.progress((i + 1) / len(model_names))
st.success("Classification complete!")
progress_bar.empty()
# Display results in a grid
col1, col2, col3 = st.columns(3)
for i, (model_name, prediction, inference_time) in enumerate(results):
with [col1, col2, col3][i % 3]:
st.subheader(model_name)
st.write(f"Prediction: {prediction}")
st.write(f"Inference Time: {inference_time:.4f}s")
st.write("---")
else:
st.warning("Please enter some text to classify.")
if __name__ == "__main__":
main()
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