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---
license: mit
datasets:
- OxAISH-AL-LLM/wiki_toxic
- textdetox/multilingual_toxic_spans
language:
- en
base_model:
- openai-community/gpt2
---
# Model Card for Toxic Text GEN
This model is a decision Tranformer for text generation with controlled toxicity (0-1).
## Model Details
### Model Description
Made using a decision transformer, it can generate toxic sentences based on a toxicity control (defined as reward-to-go/rtg).
Current text generation is not very coherent due to lack of variety in training data and low compute.
- **Developed by:** [Ashed00]
- **Finetuned from model:** [GPT-2]
### Model Sources [optional]
- **Repository:** [https://github.com/Ashu-00/NLP-Implementations/tree/main/Decision_Transformer]
- **Demo:** Soon
## Uses
Fun, little experiment.
## Bias, Risks, and Limitations
This model is biased based on its training data. I take no responsibility for its generation.
Most generated text is non-coherent due to lack of variety of training data.
## How to Get Started with the Model
```python
import torch.nn.functional as F
def generate_conditioned_text2(model, tokenizer, prompt, target_rtg, max_length=50, temperature=1.0, top_k=50):
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
attention_mask = inputs["attention_mask"].to(device)
# Create RTG tensor with the target value for each token in the prompt
rtg = torch.tensor([[target_rtg] * input_ids.shape[1]], dtype=torch.float).to(device)
seq_length = input_ids.shape[1]
for _ in range(max_length):
with torch.no_grad():
# Slice rtg to match current sequence length
rtg_current = rtg[:, :seq_length]
outputs = model(
input_ids=input_ids,
attention_mask=attention_mask,
rtg=rtg_current,
return_dict=True
)
# Get next token logits and apply temperature scaling
next_token_logits = outputs["logits"][:, -1, :] / temperature
# Apply top-k filtering
top_k_logits, top_k_indices = torch.topk(next_token_logits, top_k)
probabilities = F.softmax(top_k_logits, dim=-1)
next_token = top_k_indices[0, torch.multinomial(probabilities, num_samples=1)]
# Append the predicted token to input_ids and update attention mask
input_ids = torch.cat([input_ids, next_token], dim=-1)
attention_mask = torch.cat([attention_mask, torch.ones_like(next_token)], dim=-1)
# Append the target reward for the new token
new_rtg = torch.tensor([[target_rtg]], dtype=torch.float).to(device)
rtg = torch.cat([rtg, new_rtg], dim=1)
# Stop if EOS token is generated
if next_token.item() == tokenizer.eos_token_id:
break
seq_length += 1
return tokenizer.decode(input_ids[0], skip_special_tokens=True)
less_toxic_text = generate_conditioned_text2(model, tokenizer, prompt, target_rtg=1)
more_toxic_text = generate_conditioned_text2(model, tokenizer, prompt, target_rtg=0.0)
avg_toxic = generate_conditioned_text2(model,tokenizer, prompt, target_rtg=0.5 )
print("More Toxic Text:", less_toxic_text)
print("Less Toxic Text:", more_toxic_text)
print("Avg Toxic Text:", avg_toxic)
```
## Training Details
Refer to the github for training datasets and procedure. |