Detection Phase

Hello,

I already generated watermarked data with the below code sample:

```
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
SynthIDTextWatermarkingConfig,
)

# Standard model and tokenizer initialization
tokenizer = AutoTokenizer.from_pretrained('repo/id')
model = AutoModelForCausalLM.from_pretrained('repo/id')

# SynthID Text configuration
watermarking_config = SynthIDTextWatermarkingConfig(
keys=[654, 400, 836, 123, 340, 443, 597, 160, 57, ...],
ngram_len=5,
)

# Generation with watermarking
tokenized_prompts = tokenizer(["your prompts here"])
output_sequences = model.generate(
**tokenized_prompts,
watermarking_config=watermarking_config,
do_sample=True,
)
watermarked_text = tokenizer.batch_decode(output_sequences)
```
Could you help me with:
How to train the detector and how to detect the watermark?
My length of output text is maximum 200 tokens, can you suggest the threshold for detection?