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output_logits option behaves like output_scores, but returns the raw, unprocessed prediction logit scores, ie. the values before they undergo logit processing and/or warping. The latter happens by default for the regular output scores. It's useful to have the unprocessed logit scores in certain circumstances. For example, unprocessed logit scores are very useful with causallm models when one wants to determine the probability of a certain answer, e.g. when asking a question with a yes/no answer. In that case getting the next-token probabilities of both "yes" and "no" (and/or their relative ratio) is of interest for classification. The reason for getting these _before_ logit processing and/or warping is b/c a) that can change the probabilities or b) reject the tokens of interest / reduce the number of tokens to just 1. For an example use-case see paper TabLLM: Few-shot Classification of Tabular Data with Large Language Models by Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag. https://arxiv.org/abs/2210.10723 In addition: - added dedicated unit test: tests/generation/test_utils/test_return_unprocessed_logit_scores which tests return of logics with output_logits=True in generation. - set output_logits=True in all other generation unit tests, that also have output_scores=True. Implemented @gante's and @amyeroberts review feedback Co-authored-by: kx79wq <max.baak@ing.com> |
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| .. | ||
| benchmark | ||
| bettertransformer | ||
| deepspeed | ||
| extended | ||
| fixtures | ||
| fsdp | ||
| generation | ||
| models | ||
| optimization | ||
| peft_integration | ||
| pipelines | ||
| quantization | ||
| repo_utils | ||
| sagemaker | ||
| tokenization | ||
| tools | ||
| trainer | ||
| utils | ||
| __init__.py | ||
| test_backbone_common.py | ||
| test_cache_utils.py | ||
| test_configuration_common.py | ||
| test_configuration_utils.py | ||
| test_feature_extraction_common.py | ||
| test_feature_extraction_utils.py | ||
| test_image_processing_common.py | ||
| test_image_processing_utils.py | ||
| test_image_transforms.py | ||
| test_modeling_common.py | ||
| test_modeling_flax_common.py | ||
| test_modeling_flax_utils.py | ||
| test_modeling_tf_common.py | ||
| test_modeling_tf_utils.py | ||
| test_modeling_utils.py | ||
| test_pipeline_mixin.py | ||
| test_processing_common.py | ||
| test_sequence_feature_extraction_common.py | ||
| test_tokenization_common.py | ||
| test_tokenization_utils.py | ||