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

- Add --ignore_mismatched_sizes argument to classification examples

- Expand the error message when loading a model whose head dimensions are different from expected dimensions

Author: regisss

examples/pytorch/audio-classification/README.md

@@ -18,13 +18,13 @@ limitations under the License.
 
 The following examples showcase how to fine-tune `Wav2Vec2` for audio classification using PyTorch.
 
-Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, 
-*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), 
-[HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html), 
-[XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only 
+Speech recognition models that have been pretrained in unsupervised fashion on audio data alone,
+*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html),
+[HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html),
+[XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only
 very little annotated data to yield good performance on speech classification datasets.
 
-## Single-GPU 
+## Single-GPU
 
 The following command shows how to fine-tune [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the 🗣️ [Keyword Spotting subset](https://huggingface.co/datasets/superb#ks) of the SUPERB dataset.
 
@@ -63,7 +63,9 @@ On a single V100 GPU (16GB), this script should run in ~14 minutes and yield acc
 
 👀 See the results here: [anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting)
 
-## Multi-GPU 
+> 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.
+
+## Multi-GPU
 
 The following command shows how to fine-tune [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) for 🌎 **Language Identification** on the [CommonLanguage dataset](https://huggingface.co/datasets/anton-l/common_language).
 
@@ -139,7 +141,7 @@ It has been verified that the script works for the following datasets:
 
 | Dataset | Pretrained Model | # transformer layers | Accuracy on eval | GPU setup | Training time | Fine-tuned Model & Logs |
 |---------|------------------|----------------------|------------------|-----------|---------------|--------------------------|
-| Keyword Spotting | [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) | 2 | 0.9706 | 1 V100 GPU | 11min  | [here](https://huggingface.co/anton-l/distilhubert-ft-keyword-spotting) | 
+| Keyword Spotting | [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) | 2 | 0.9706 | 1 V100 GPU | 11min  | [here](https://huggingface.co/anton-l/distilhubert-ft-keyword-spotting) |
 | Keyword Spotting | [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 12 | 0.9826 | 1 V100 GPU | 14min  | [here](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) |
 | Keyword Spotting | [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) | 12 | 0.9819 | 1 V100 GPU | 14min  | [here](https://huggingface.co/anton-l/hubert-base-ft-keyword-spotting) |
 | Keyword Spotting | [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 24 | 0.9757 | 1 V100 GPU | 15min  | [here](https://huggingface.co/anton-l/sew-mid-100k-ft-keyword-spotting) |

examples/pytorch/audio-classification/run_audio_classification.py

@@ -163,6 +163,10 @@ class ModelArguments:
     freeze_feature_extractor: Optional[bool] = field(
         default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
     )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
 
     def __post_init__(self):
         if not self.freeze_feature_extractor and self.freeze_feature_encoder:
@@ -333,6 +337,7 @@ def compute_metrics(eval_pred):
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
     # freeze the convolutional waveform encoder

examples/pytorch/image-classification/README.md

@@ -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.
 

examples/pytorch/image-classification/run_image_classification.py

@@ -150,6 +150,10 @@ class ModelArguments:
             )
         },
     )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
 
 
 def collate_fn(examples):
@@ -269,6 +273,7 @@ def compute_metrics(p):
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
     feature_extractor = AutoFeatureExtractor.from_pretrained(
         model_args.feature_extractor_name or model_args.model_name_or_path,

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -165,6 +165,11 @@ def parse_args():
         action="store_true",
         help="Whether to load in all available experiment trackers from the environment and use them for logging.",
     )
+    parser.add_argument(
+        "--ignore_mismatched_sizes",
+        action="store_true",
+        help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
+    )
     args = parser.parse_args()
 
     # Sanity checks
@@ -278,6 +283,7 @@ def main():
         args.model_name_or_path,
         from_tf=bool(".ckpt" in args.model_name_or_path),
         config=config,
+        ignore_mismatched_sizes=args.ignore_mismatched_sizes,
     )
 
     # Preprocessing the datasets

examples/pytorch/text-classification/README.md

@@ -22,7 +22,7 @@ Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/
 
 Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
 Evaluation](https://gluebenchmark.com/). This script can fine-tune any of the models on the [hub](https://huggingface.co/models)
-and can also be used for a dataset hosted on our [hub](https://huggingface.co/datasets) or your own data in a csv or a JSON file 
+and can also be used for a dataset hosted on our [hub](https://huggingface.co/datasets) or your own data in a csv or a JSON file
 (the script might need some tweaks in that case, refer to the comments inside for help).
 
 GLUE is made up of a total of 9 different tasks. Here is how to run the script on one of them:
@@ -79,6 +79,8 @@ python run_glue.py \
   --output_dir /tmp/imdb/
 ```
 
+> 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.
+
 
 ### Mixed precision training
 

examples/pytorch/text-classification/run_glue.py

@@ -196,6 +196,10 @@ class ModelArguments:
             )
         },
     )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
 
 
 def main():
@@ -364,6 +368,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
     # Preprocessing the raw_datasets

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -170,6 +170,11 @@ def parse_args():
         action="store_true",
         help="Whether to load in all available experiment trackers from the environment and use them for logging.",
     )
+    parser.add_argument(
+        "--ignore_mismatched_sizes",
+        action="store_true",
+        help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
+    )
     args = parser.parse_args()
 
     # Sanity checks
@@ -288,6 +293,7 @@ def main():
         args.model_name_or_path,
         from_tf=bool(".ckpt" in args.model_name_or_path),
         config=config,
+        ignore_mismatched_sizes=args.ignore_mismatched_sizes,
     )
 
     # Preprocessing the datasets

examples/pytorch/text-classification/run_xnli.py

@@ -162,6 +162,10 @@ class ModelArguments:
             )
         },
     )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
 
 
 def main():
@@ -291,6 +295,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
     # Preprocessing the datasets

examples/pytorch/token-classification/README.md

@@ -55,6 +55,8 @@ uses special features of those tokenizers. You can check if your favorite model
 [this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version
 of the script.
 
+> 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.
+
 ## Old version of the script
 
 You can find the old version of the PyTorch script [here](https://github.com/huggingface/transformers/blob/main/examples/legacy/token-classification/run_ner.py).