huggingface--transformers/docs/source/en/model_doc/deberta-v2.md

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*This model was published in HF papers on 2020-06-05 and contributed to Hugging Face Transformers on 2021-02-19.*

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# DeBERTa-v2

[DeBERTa-v2](https://huggingface.co/papers/2006.03654) improves on the original [DeBERTa](./deberta) architecture by using a SentencePiece-based tokenizer and a new vocabulary size of 128K. It also adds an additional convolutional layer within the first transformer layer to better learn local dependencies of input tokens. Finally, the position projection and content projection matrices are shared in the attention layer to reduce the number of parameters.

You can find all the original [DeBERTa-v2] checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=deberta-v2) organization.

> [!TIP]
> This model was contributed by [Pengcheng He](https://huggingface.co/DeBERTa).
>
> Click on the DeBERTa-v2 models in the right sidebar for more examples of how to apply DeBERTa-v2 to different language tasks.

The example below demonstrates how to classify text with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipeline = pipeline(
    task="text-classification",
    model="microsoft/deberta-v2-xlarge-mnli",
    device=0,
)
result = pipeline("DeBERTa-v2 is great at understanding context!")
print(result)
```

</hfoption>
<hfoption id="AutoModel">

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "microsoft/deberta-v2-xlarge-mnli"
)
model = AutoModelForSequenceClassification.from_pretrained(
    "microsoft/deberta-v2-xlarge-mnli",
    device_map="auto"
)

inputs = tokenizer("DeBERTa-v2 is great at understanding context!", return_tensors="pt").to(model.device)
outputs = model(**inputs)

logits = outputs.logits
predicted_class_id = logits.argmax().item()
predicted_label = model.config.id2label[predicted_class_id]
print(f"Predicted label: {predicted_label}")
```

</hfoption>
</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [bitsandbytes quantization](../quantization/bitsandbytes) to only quantize the weights to 4-bit.

```py
from transformers import AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig

model_id = "microsoft/deberta-v2-xlarge-mnli"
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    dtype="float16"
 device_map="auto")

inputs = tokenizer("DeBERTa-v2 is great at understanding context!", return_tensors="pt").to(model.device)
outputs = model(**inputs)
logits = outputs.logits
predicted_class_id = logits.argmax().item()
predicted_label = model.config.id2label[predicted_class_id]
print(f"Predicted label: {predicted_label}")

```

## DebertaV2Config

[[autodoc]] DebertaV2Config

## DebertaV2Tokenizer

[[autodoc]] DebertaV2Tokenizer
    - get_special_tokens_mask
    - save_vocabulary

## DebertaV2TokenizerFast

[[autodoc]] DebertaV2TokenizerFast

## DebertaV2Model

[[autodoc]] DebertaV2Model
    - forward

## DebertaV2PreTrainedModel

[[autodoc]] DebertaV2PreTrainedModel
    - forward

## DebertaV2ForMaskedLM

[[autodoc]] DebertaV2ForMaskedLM
    - forward

## DebertaV2ForSequenceClassification

[[autodoc]] DebertaV2ForSequenceClassification
    - forward

## DebertaV2ForTokenClassification

[[autodoc]] DebertaV2ForTokenClassification
    - forward

## DebertaV2ForQuestionAnswering

[[autodoc]] DebertaV2ForQuestionAnswering
    - forward

## DebertaV2ForMultipleChoice

[[autodoc]] DebertaV2ForMultipleChoice
    - forward