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SynthID: better example #34372

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Oct 25, 2024
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SynthID: better example #34372

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c94bddf
better example
gante Oct 24, 2024
4e26d27
Update src/transformers/generation/configuration_utils.py
gante Oct 24, 2024
f2f9b87
Update src/transformers/generation/logits_process.py
gante Oct 24, 2024
065b6c9
nits
gante Oct 25, 2024
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4 changes: 1 addition & 3 deletions docs/source/en/internal/generation_utils.md
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Expand Up @@ -428,13 +428,11 @@ A [`Constraint`] can be used to force the generation to include specific tokens
- __call__

[[autodoc]] BayesianDetectorConfig
- __call__

[[autodoc]] BayesianDetectorModel
- __call__
- forward

[[autodoc]] SynthIDTextWatermarkingConfig
- __call__

[[autodoc]] SynthIDTextWatermarkDetector
- __call__
10 changes: 5 additions & 5 deletions src/transformers/generation/configuration_utils.py
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Expand Up @@ -1471,8 +1471,8 @@ class SynthIDTextWatermarkingConfig(BaseWatermarkingConfig):
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, SynthIDTextWatermarkingConfig

>>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b-it')
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b-it')
>>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b', padding_side="left")
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b')

>>> # SynthID Text configuration
>>> watermarking_config = SynthIDTextWatermarkingConfig(
Expand All @@ -1481,11 +1481,11 @@ class SynthIDTextWatermarkingConfig(BaseWatermarkingConfig):
... )

>>> # Generation with watermarking
>>> tokenized_prompts = tokenizer(["your prompts here"])
>>> tokenized_prompts = tokenizer(["Once upon a time, "], return_tensors="pt", padding=True)
>>> output_sequences = model.generate(
... **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True,
... **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True, max_new_tokens=10
... )
>>> watermarked_text = tokenizer.batch_decode(output_sequences)
>>> watermarked_text = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
```
"""

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10 changes: 5 additions & 5 deletions src/transformers/generation/logits_process.py
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Expand Up @@ -2565,8 +2565,8 @@ class SynthIDTextWatermarkLogitsProcessor(LogitsProcessor):
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, SynthIDTextWatermarkingConfig

>>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b-it')
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b-it')
>>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b', padding_side="left")
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b')

>>> # SynthID Text configuration
>>> watermarking_config = SynthIDTextWatermarkingConfig(
Expand All @@ -2575,11 +2575,11 @@ class SynthIDTextWatermarkLogitsProcessor(LogitsProcessor):
... )

>>> # Generation with watermarking
>>> tokenized_prompts = tokenizer(["your prompts here"])
>>> tokenized_prompts = tokenizer(["Once upon a time, "], return_tensors="pt", padding=True)
>>> output_sequences = model.generate(
... **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True,
... **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True, max_new_tokens=10
... )
>>> watermarked_text = tokenizer.batch_decode(output_sequences)
>>> watermarked_text = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
```
"""

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