philipp-zettl/vrom-hf-docs huggingface dataset:
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) : Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) : It is a [Cache](/docs/transformers/v5.6.2/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
#### transformers.modeling_outputs.BaseModelOutputWithPast[[transformers.modeling_outputs.BaseModelOutputWithPast]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L123)

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

**Parameters:**

last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) : Sequence of hidden-states at the output of the last layer of the model.  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output.
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) : It is a [Cache](/docs/transformers/v5.6.2/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
#### transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions[[transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L238)

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

**Parameters:**

last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) : Sequence of hidden-states at the output of the last layer of the model.  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output.
#### transformers.modeling_outputs.Seq2SeqModelOutput[[transformers.modeling_outputs.Seq2SeqModelOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L500)

Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.

**Parameters:**

last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) : Sequence of hidden-states at the output of the last layer of the decoder of the model.  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output.
past_key_values (`EncoderDecoderCache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) : It is a [EncoderDecoderCache](/docs/transformers/v5.6.2/en/internal/generation_utils#transformers.EncoderDecoderCache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.  Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) : Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) : Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) : Sequence of hidden-states at the output of the last layer of the encoder of the model.

encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.  Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) : Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
#### transformers.modeling_outputs.CausalLMOutput[[transformers.modeling_outputs.CausalLMOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L629)

Base class for causal language model (or autoregressive) outputs.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Language modeling loss (for next-token prediction).

logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) : Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
#### transformers.modeling_outputs.CausalLMOutputWithCrossAttentions[[transformers.modeling_outputs.CausalLMOutputWithCrossAttentions]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L693)

Base class for causal language model (or autoregressive) outputs.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Language modeling loss (for next-token prediction).

logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) : Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) : Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.  Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.

past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) : It is a [Cache](/docs/transformers/v5.6.2/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).  Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
#### transformers.modeling_outputs.CausalLMOutputWithPast[[transformers.modeling_outputs.CausalLMOutputWithPast]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L658)

Base class for causal language model (or autoregressive) outputs.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Language modeling loss (for next-token prediction).

logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) : Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) : It is a [Cache](/docs/transformers/v5.6.2/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
#### transformers.modeling_outputs.MaskedLMOutput[[transformers.modeling_outputs.MaskedLMOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L770)

Base class for masked language models outputs.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Masked language modeling (MLM) loss.

logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) : Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
#### transformers.modeling_outputs.Seq2SeqLMOutput[[transformers.modeling_outputs.Seq2SeqLMOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L799)

Base class for sequence-to-sequence language models outputs.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Language modeling loss.

logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) : Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`EncoderDecoderCache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) : It is a [EncoderDecoderCache](/docs/transformers/v5.6.2/en/internal/generation_utils#transformers.EncoderDecoderCache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) : Sequence of hidden-states at the output of the last layer of the encoder of the model.

encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
#### transformers.modeling_outputs.NextSentencePredictorOutput[[transformers.modeling_outputs.NextSentencePredictorOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L930)

Base class for outputs of models predicting if two sentences are consecutive or not.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `next_sentence_label` is provided) : Next sequence prediction (classification) loss.

logits (`torch.FloatTensor` of shape `(batch_size, 2)`) : Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
#### transformers.modeling_outputs.SequenceClassifierOutput[[transformers.modeling_outputs.SequenceClassifierOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L960)

Base class for outputs of sentence classification models.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification (or regression if config.num_labels==1) loss.

logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) : Classification (or regression if config.num_labels==1) scores (before SoftMax).
#### transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput[[transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L989)

Base class for outputs of sequence-to-sequence sentence classification models.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided) : Classification (or regression if config.num_labels==1) loss.

logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) : Classification (or regression if config.num_labels==1) scores (before SoftMax).
#### transformers.modeling_outputs.MultipleChoiceModelOutput[[transformers.modeling_outputs.MultipleChoiceModelOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L1047)

Base class for outputs of multiple choice models.

**Parameters:**

loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided) : Classification loss.

logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`) : *num_choices* is the second dimension of the input tensors. (see *input_ids* above).  Classification scores (before SoftMax).
#### transformers.modeling_outputs.TokenClassifierOutput[[transformers.modeling_outputs.TokenClassifierOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L1078)

Base class for outputs of token classification models.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification loss.

logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) : Classification scores (before SoftMax).

hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
#### transformers.modeling_outputs.QuestionAnsweringModelOutput[[transformers.modeling_outputs.QuestionAnsweringModelOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L1107)

Base class for outputs of question answering models.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) : Span-start scores (before SoftMax).

end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) : Span-end scores (before SoftMax).
#### transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput[[transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L1139)

Base class for outputs of sequence-to-sequence question answering models.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) : Span-start scores (before SoftMax).

end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) : Span-end scores (before SoftMax).
#### transformers.modeling_outputs.Seq2SeqSpectrogramOutput[[transformers.modeling_outputs.Seq2SeqSpectrogramOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L1470)

Base class for sequence-to-sequence spectrogram outputs.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Spectrogram generation loss.

spectrogram (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_bins)`) : The predicted spectrogram.
#### transformers.modeling_outputs.SemanticSegmenterOutput[[transformers.modeling_outputs.SemanticSegmenterOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L1200)

Base class for outputs of semantic segmentation models.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification (or regression if config.num_labels==1) loss.

logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`) : Classification scores for each pixel.    The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the original image size as post-processing. You should always check your logits shape and resize as needed.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`.  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) : Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`.  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
#### transformers.modeling_outputs.ImageClassifierOutput[[transformers.modeling_outputs.ImageClassifierOutput]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L1238)

Base class for outputs of image classification models.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification (or regression if config.num_labels==1) loss.

logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) : Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage.

attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) : Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`.  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
#### transformers.modeling_outputs.ImageClassifierOutputWithNoAttention[[transformers.modeling_outputs.ImageClassifierOutputWithNoAttention]]

[Source](https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/modeling_outputs.py#L1266)

Base class for outputs of image classification models.

**Parameters:**

loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification (or regression if config.num_labels==1) loss.

logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) : Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) : Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage.