YOSO

## [](#overview)Overview

The YOSO model was proposed in [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714)  
by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh. YOSO approximates standard softmax self-attention via a Bernoulli sampling scheme based on Locality Sensitive Hashing (LSH). In principle, all the Bernoulli random variables can be sampled with a single hash.

The abstract from the paper is the following:

_Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is the self-attention mechanism, which captures the interactions of token pairs in the input sequences and depends quadratically on the sequence length. Training such models on longer sequences is expensive. In this paper, we show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear. We bypass the quadratic cost by considering self-attention as a sum of individual tokens associated with Bernoulli random variables that can, in principle, be sampled at once by a single hash (although in practice, this number may be a small constant). This leads to an efficient sampling scheme to estimate self-attention which relies on specific modifications of LSH (to enable deployment on GPU architectures). We evaluate our algorithm on the GLUE benchmark with standard 512 sequence length where we see favorable performance relative to a standard pretrained Transformer. On the Long Range Arena (LRA) benchmark, for evaluating performance on long sequences, our method achieves results consistent with softmax self-attention but with sizable speed-ups and memory savings and often outperforms other efficient self-attention methods. Our code is available at this https URL_

Tips:

-   The YOSO attention algorithm is implemented through custom CUDA kernels, functions written in CUDA C++ that can be executed multiple times in parallel on a GPU.
-   The kernels provide a `fast_hash` function, which approximates the random projections of the queries and keys using the Fast Hadamard Transform. Using these hash codes, the `lsh_cumulation` function approximates self-attention via LSH-based Bernoulli sampling.
-   To use the custom kernels, the user should set `config.use_expectation = False`. To ensure that the kernels are compiled successfully, the user must install the correct version of PyTorch and cudatoolkit. By default, `config.use_expectation = True`, which uses YOSO-E and does not require compiling CUDA kernels.

![drawing](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yoso_architecture.jpg) YOSO Attention Algorithm. Taken from the [original paper](https://arxiv.org/abs/2111.09714).

This model was contributed by [novice03](https://huggingface.co/novice03). The original code can be found [here](https://github.com/mlpen/YOSO).

## [](#documentation-resources)Documentation resources

-   [Text classification task guide](../tasks/sequence_classification)
-   [Token classification task guide](../tasks/token_classification)
-   [Question answering task guide](../tasks/question_answering)
-   [Masked language modeling task guide](../tasks/masked_language_modeling)
-   [Multiple choice task guide](../tasks/multiple_choice)

## [](#transformers.YosoConfig)YosoConfig

### class transformers.YosoConfig

[](#transformers.YosoConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/configuration_yoso.py#L29)

( vocab\_size = 50265hidden\_size = 768num\_hidden\_layers = 12num\_attention\_heads = 12intermediate\_size = 3072hidden\_act = 'gelu'hidden\_dropout\_prob = 0.1attention\_probs\_dropout\_prob = 0.1max\_position\_embeddings = 4096type\_vocab\_size = 1initializer\_range = 0.02layer\_norm\_eps = 1e-12position\_embedding\_type = 'absolute'use\_expectation = Truehash\_code\_len = 9num\_hash = 64conv\_window = Noneuse\_fast\_hash = Truelsh\_backward = Truepad\_token\_id = 1bos\_token\_id = 0eos\_token\_id = 2\*\*kwargs )

This is the configuration class to store the configuration of a [YosoModel](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoModel). It is used to instantiate an YOSO model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the YOSO [uw-madison/yoso-4096](https://huggingface.co/uw-madison/yoso-4096) architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

```
>>> from transformers import YosoConfig, YosoModel

>>> 
>>> configuration = YosoConfig()

>>> 
>>> model = YosoModel(configuration)

>>> 
>>> configuration = model.config```

## [](#transformers.YosoModel)YosoModel

### class transformers.YosoModel

[](#transformers.YosoModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L740)

( config )

Parameters

-   [](#transformers.YosoModel.config)**config** ([YosoConfig](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare YOSO Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.YosoModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L765)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.BaseModelOutputWithCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`

The [YosoModel](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, YosoModel
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("uw-madison/yoso-4096")
>>> model = YosoModel.from_pretrained("uw-madison/yoso-4096")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.YosoForMaskedLM)YosoForMaskedLM

### class transformers.YosoForMaskedLM

[](#transformers.YosoForMaskedLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L851)

( config )

Parameters

-   [](#transformers.YosoForMaskedLM.config)**config** ([YosoConfig](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

YOSO Model with a `language modeling` head on top. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.YosoForMaskedLM.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L873)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.MaskedLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)`

The [YosoForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoForMaskedLM) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, YosoForMaskedLM
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("uw-madison/yoso-4096")
>>> model = YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096")

>>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> 
>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]

>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)

>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>> 
>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

>>> outputs = model(**inputs, labels=labels)```

## [](#transformers.YosoForSequenceClassification)YosoForSequenceClassification

### class transformers.YosoForSequenceClassification

[](#transformers.YosoForSequenceClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L958)

( config )

Parameters

-   [](#transformers.YosoForSequenceClassification.config)**config** ([YosoConfig](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

YOSO Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.YosoForSequenceClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L968)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.SequenceClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`

The [YosoForSequenceClassification](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoForSequenceClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example of single-label classification:

```
>>> import torch
>>> from transformers import AutoTokenizer, YosoForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("uw-madison/yoso-4096")
>>> model = YosoForSequenceClassification.from_pretrained("uw-madison/yoso-4096")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_id = logits.argmax().item()

>>> 
>>> num_labels = len(model.config.id2label)
>>> model = YosoForSequenceClassification.from_pretrained("uw-madison/yoso-4096", num_labels=num_labels)

>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss```

Example of multi-label classification:

```
>>> import torch
>>> from transformers import AutoTokenizer, YosoForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("uw-madison/yoso-4096")
>>> model = YosoForSequenceClassification.from_pretrained("uw-madison/yoso-4096", problem_type="multi_label_classification")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

>>> 
>>> num_labels = len(model.config.id2label)
>>> model = YosoForSequenceClassification.from_pretrained(
...     "uw-madison/yoso-4096", num_labels=num_labels, problem_type="multi_label_classification"
... )

>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).loss```

## [](#transformers.YosoForMultipleChoice)YosoForMultipleChoice

### class transformers.YosoForMultipleChoice

[](#transformers.YosoForMultipleChoice)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L1049)

( config )

Parameters

-   [](#transformers.YosoForMultipleChoice.config)**config** ([YosoConfig](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

YOSO Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.YosoForMultipleChoice.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L1060)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.MultipleChoiceModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput) or `tuple(torch.FloatTensor)`

The [YosoForMultipleChoice](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoForMultipleChoice) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, YosoForMultipleChoice
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("uw-madison/yoso-4096")
>>> model = YosoForMultipleChoice.from_pretrained("uw-madison/yoso-4096")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0)  

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
>>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  

>>> 
>>> loss = outputs.loss
>>> logits = outputs.logits```

## [](#transformers.YosoForTokenClassification)YosoForTokenClassification

### class transformers.YosoForTokenClassification

[](#transformers.YosoForTokenClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L1140)

( config )

Parameters

-   [](#transformers.YosoForTokenClassification.config)**config** ([YosoConfig](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

YOSO Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.YosoForTokenClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L1152)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.TokenClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`

The [YosoForTokenClassification](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoForTokenClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, YosoForTokenClassification
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("uw-madison/yoso-4096")
>>> model = YosoForTokenClassification.from_pretrained("uw-madison/yoso-4096")

>>> inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_token_class_ids = logits.argmax(-1)

>>> 
>>> 
>>> 
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]

>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels).loss```

## [](#transformers.YosoForQuestionAnswering)YosoForQuestionAnswering

### class transformers.YosoForQuestionAnswering

[](#transformers.YosoForQuestionAnswering)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L1225)

( config )

Parameters

-   [](#transformers.YosoForQuestionAnswering.config)**config** ([YosoConfig](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

YOSO Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.YosoForQuestionAnswering.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/yoso/modeling_yoso.py#L1238)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonestart\_positions: typing.Optional\[torch.Tensor\] = Noneend\_positions: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.QuestionAnsweringModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`

The [YosoForQuestionAnswering](/docs/transformers/v4.30.0/en/model_doc/yoso#transformers.YosoForQuestionAnswering) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, YosoForQuestionAnswering
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("uw-madison/yoso-4096")
>>> model = YosoForQuestionAnswering.from_pretrained("uw-madison/yoso-4096")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()

>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]

>>> 
>>> target_start_index = torch.tensor([14])
>>> target_end_index = torch.tensor([15])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.loss```