Create README.md

README.md

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+---
+license: mit
+datasets:
+- OxAISH-AL-LLM/wiki_toxic
+- textdetox/multilingual_toxic_spans
+language:
+- en
+base_model:
+- openai-community/gpt2
+tags:
+- not-for-all-audiences
+---
+
+# 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.