b0f0c61899
* Add SynthIDTextWatermarkLogitsProcessor * esolving comments. * Resolving comments. * esolving commits, * Improving SynthIDWatermark tests. * switch to PT version * detector as pretrained model + style * update training + style * rebase * Update logits_process.py * Improving SynthIDWatermark tests. * Shift detector training to wikitext negatives and stabilize with lower learning rate. * Clean up. * in for 7B * cleanup * upport python 3.8. * README and final cleanup. * HF Hub upload and initiaze. * Update requirements for synthid_text. * Adding SynthIDTextWatermarkDetector. * Detector testing. * Documentation changes. * Copyrights fix. * Fix detector api. * ironing out errors * ironing out errors * training checks * make fixup and make fix-copies * docstrings and add to docs * copyright * BC * test docstrings * move import * protect type hints * top level imports * watermarking example * direct imports * tpr fpr meaning * process_kwargs * SynthIDTextWatermarkingConfig docstring * assert -> exception * example updates * no immutable dict (cant be serialized) * pack fn * einsum equivalent * import order * fix test on gpu * add detector example --------- Co-authored-by: Sumedh Ghaisas <sumedhg@google.com> Co-authored-by: Marc Sun <marc@huggingface.co> Co-authored-by: sumedhghaisas2 <138781311+sumedhghaisas2@users.noreply.github.com> Co-authored-by: raushan <raushan@huggingface.co>
2.2 KiB
Generation
Each framework has a generate method for text generation implemented in their respective GenerationMixin class:
- PyTorch [
~generation.GenerationMixin.generate] is implemented in [~generation.GenerationMixin]. - TensorFlow [
~generation.TFGenerationMixin.generate] is implemented in [~generation.TFGenerationMixin]. - Flax/JAX [
~generation.FlaxGenerationMixin.generate] is implemented in [~generation.FlaxGenerationMixin].
Regardless of your framework of choice, you can parameterize the generate method with a [~generation.GenerationConfig]
class instance. Please refer to this class for the complete list of generation parameters, which control the behavior
of the generation method.
To learn how to inspect a model's generation configuration, what are the defaults, how to change the parameters ad hoc, and how to create and save a customized generation configuration, refer to the text generation strategies guide. The guide also explains how to use related features, like token streaming.
GenerationConfig
autodoc generation.GenerationConfig - from_pretrained - from_model_config - save_pretrained - update - validate - get_generation_mode
GenerationMixin
autodoc GenerationMixin - generate - compute_transition_scores
TFGenerationMixin
autodoc TFGenerationMixin - generate - compute_transition_scores
FlaxGenerationMixin
autodoc FlaxGenerationMixin - generate