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add cv + audio labels (#20114)

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Steven Liu
2022-11-09 07:40:15 -08:00
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1 changed files with 13 additions and 5 deletions
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@@ -238,11 +238,11 @@ predictions and the expected value (the label).
These labels are different according to the model head, for example: These labels are different according to the model head, for example:
- For sequence classification models ([`BertForSequenceClassification`]), the model expects a tensor of dimension - For sequence classification models, ([`BertForSequenceClassification`]), the model expects a tensor of dimension
`(batch_size)` with each value of the batch corresponding to the expected label of the entire sequence. `(batch_size)` with each value of the batch corresponding to the expected label of the entire sequence.
- For token classification models ([`BertForTokenClassification`]), the model expects a tensor of dimension - For token classification models, ([`BertForTokenClassification`]), the model expects a tensor of dimension
`(batch_size, seq_length)` with each value corresponding to the expected label of each individual token. `(batch_size, seq_length)` with each value corresponding to the expected label of each individual token.
- For masked language modeling ([`BertForMaskedLM`]), the model expects a tensor of dimension `(batch_size, - For masked language modeling, ([`BertForMaskedLM`]), the model expects a tensor of dimension `(batch_size,
seq_length)` with each value corresponding to the expected label of each individual token: the labels being the token seq_length)` with each value corresponding to the expected label of each individual token: the labels being the token
ID for the masked token, and values to be ignored for the rest (usually -100). ID for the masked token, and values to be ignored for the rest (usually -100).
- For sequence to sequence tasks, ([`BartForConditionalGeneration`], [`MBartForConditionalGeneration`]), the model - For sequence to sequence tasks, ([`BartForConditionalGeneration`], [`MBartForConditionalGeneration`]), the model
@@ -250,6 +250,14 @@ These labels are different according to the model head, for example:
associated with each input sequence. During training, both BART and T5 will make the appropriate associated with each input sequence. During training, both BART and T5 will make the appropriate
`decoder_input_ids` and decoder attention masks internally. They usually do not need to be supplied. This does not `decoder_input_ids` and decoder attention masks internally. They usually do not need to be supplied. This does not
apply to models leveraging the Encoder-Decoder framework. apply to models leveraging the Encoder-Decoder framework.
- For image classification models, ([`ViTForImageClassification`]), the model expects a tensor of dimension
`(batch_size)` with each value of the batch corresponding to the expected label of each individual image.
- For semantic segmentation models, ([`SegformerForSemanticSegmentation`]), the model expects a tensor of dimension
`(batch_size, height, width)` with each value of the batch corresponding to the expected label of each individual pixel.
- For object detection models, ([`DetrForObjectDetection`]), the model expects a list of dictionaries with a
`class_labels` and `boxes` key where each value of the batch corresponds to the expected label and number of bounding boxes of each individual image.
- For automatic speech recognition models, ([`Wav2Vec2ForCTC`]), the model expects a tensor of dimension `(batch_size,
target_length)` with each value corresponding to the expected label of each individual token.
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