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