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### **Watermarking in Text Generation**

The `generate()` method in Hugging Face Transformers supports **watermarking** generated text by randomly marking a portion of tokens as "green." These watermarked tokens have a slight bias added to their logits, making them more likely to be generated by the model. This allows detection of machine-generated content without needing an additional classification model.

### **How Watermarking Works**
- **Green Tokens**: A subset of generated tokens is marked as "green," and these tokens are given a small bias in their probability distribution. This increases their likelihood of being selected during text generation.
- **Detection**: After generation, the proportion of "green" tokens in the output can be analyzed to determine the likelihood that the text is machine-generated. This strategy was proposed in the paper **“On the Reliability of Watermarks for Large Language Models”**.
  
### **Using Watermarking**
To enable watermarking in the text generation process, you must:
1. **Configure Watermarking**: Use `WatermarkingConfig` to set the necessary arguments such as the bias value and the seeding scheme.
2. **Pass the Configuration**: Pass the `WatermarkingConfig` to the `generate()` method or include it in the `GenerationConfig` when calling the `generate()` method.
3. **Detection**: After generating the watermarked text, the watermark can be detected using `WatermarkDetector`, which analyzes the proportion of "green" tokens.

### **Example Code for Watermarking**

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, WatermarkDetector, WatermarkingConfig

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
tok.pad_token_id = tok.eos_token_id  # Set padding token to eos token
tok.padding_side = "left"

# Prepare input text
inputs = tok(["This is the beginning of a long story", "Alice and Bob are"], padding=True, return_tensors="pt")
input_len = inputs["input_ids"].shape[-1]

# Configure watermarking
watermarking_config = WatermarkingConfig(bias=2.5, seeding_scheme="selfhash")

# Generate watermarked text
out = model.generate(**inputs, watermarking_config=watermarking_config, do_sample=False, max_length=20)

# Use WatermarkDetector to detect the watermark
detector = WatermarkDetector(model_config=model.config, device="cpu", watermarking_config=watermarking_config)
detection_out = detector(out, return_dict=True)

# Check detection result
print(detection_out.prediction)  # Output: [True, True]
```

### **Explanation of the Code:**
1. **Loading Model & Tokenizer**: We load the `GPT-2` model and tokenizer. The padding token is set to the `eos_token_id`, and the padding side is set to the left.
2. **Input Preparation**: We prepare the input text with padding.
3. **Watermarking Configuration**: We define the watermark configuration with a **bias** of `2.5` and a **seeding scheme** of `"selfhash"`.
4. **Text Generation**: The text is generated with watermarking applied, and the `do_sample=False` ensures deterministic output.
5. **Watermark Detection**: The generated output is passed through the `WatermarkDetector` to detect whether the text is watermarked (machine-generated). The result is returned as an array of booleans, where `True` indicates that the text is machine-generated.

### **Key Parameters in Watermarking Config:**
- **bias**: The value added to the logits of "green" tokens to increase their chances of being selected.
- **seeding_scheme**: Defines the method used for determining which tokens to mark as "green" (e.g., `"selfhash"`).

### **Benefits and Use Cases:**
- **Content Authenticity**: Watermarking helps to distinguish machine-generated content from human-generated content, providing a way to verify the origin of the text.
- **No Extra Classification Needed**: Watermarking does not require an external classification model for detection; it uses a simple statistical approach to determine if the text has been watermarked.

### **Recommended Considerations:**
- When detecting watermarked text, it is advisable to strip off any input prompt that is significantly longer than the generated text, as it could affect the analysis of "green" token proportions.
- Ensure that the watermark configuration used during detection matches the configuration used during generation for accurate results.

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