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Improve mismatched sizes management when loading a pretrained model (#17257)

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- Add --ignore_mismatched_sizes argument to classification examples

- Expand the error message when loading a model whose head dimensions are different from expected dimensions
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regisss
2022-05-17 17:58:14 +02:00
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parent 1f13ba818e
commit 28a0811652
13 changed files with 64 additions and 9 deletions
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examples/pytorch/image-classification/README.md
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@@ -62,9 +62,11 @@ python run_image_classification.py \
Note that you can replace the model and dataset by simply setting the `model_name_or_path` and `dataset_name` arguments respectively, with any model or dataset from the [hub](https://huggingface.co/). For an overview of all possible arguments, we refer to the [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) of the `TrainingArguments`, which can be passed as flags.
> If your model classification head dimensions do not fit the number of labels in the dataset, you can specify `--ignore_mismatched_sizes` to adapt it.
### Using your own data
To use your own dataset, there are 2 ways:
To use your own dataset, there are 2 ways:
- you can either provide your own folders as `--train_dir` and/or `--validation_dir` arguments
- you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.