enginex-mlu370-any2any/transformers/docs/source/en/model_doc/ernie.md

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*This model was released on 2019-04-19 and added to Hugging Face Transformers on 2022-09-09.*

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# ERNIE

[ERNIE1.0](https://huggingface.co/papers/1904.09223), [ERNIE2.0](https://ojs.aaai.org/index.php/AAAI/article/view/6428),
[ERNIE3.0](https://huggingface.co/papers/2107.02137), [ERNIE-Gram](https://huggingface.co/papers/2010.12148), [ERNIE-health](https://huggingface.co/papers/2110.07244) are a series of powerful models proposed by baidu, especially in Chinese tasks.

ERNIE (Enhanced Representation through kNowledge IntEgration) is designed to learn language representation enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking.

Other ERNIE models released by baidu can be found at [Ernie 4.5](./ernie4_5), and [Ernie 4.5 MoE](./ernie4_5_moe).

> [!TIP]
> This model was contributed by [nghuyong](https://huggingface.co/nghuyong), and the official code can be found in [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) (in PaddlePaddle).
>
> Click on the ERNIE models in the right sidebar for more examples of how to apply ERNIE to different language tasks.

The example below demonstrates how to predict the `[MASK]` token with [`Pipeline`], [`AutoModel`], and from the command line.

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

```py
from transformers import pipeline

pipeline = pipeline(
    task="fill-mask",
    model="nghuyong/ernie-3.0-xbase-zh"
)

pipeline("巴黎是[MASK]国的首都。")
```

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

```py
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "nghuyong/ernie-3.0-xbase-zh",
)
model = AutoModelForMaskedLM.from_pretrained(
    "nghuyong/ernie-3.0-xbase-zh",
    dtype=torch.float16,
    device_map="auto"
)
inputs = tokenizer("巴黎是[MASK]国的首都。", return_tensors="pt").to(model.device)

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

masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
predicted_token = tokenizer.decode(predicted_token_id)

print(f"The predicted token is: {predicted_token}")
```

</hfoption>
<hfoption id="transformers CLI">

```bash
echo -e "巴黎是[MASK]国的首都。" | transformers run --task fill-mask --model nghuyong/ernie-3.0-xbase-zh --device 0
```

</hfoption>
</hfoptions>

## Notes

Model variants are available in different sizes and languages.

|     Model Name      | Language |           Description           |
|:-------------------:|:--------:|:-------------------------------:|
|  ernie-1.0-base-zh  | Chinese  | Layer:12, Heads:12, Hidden:768  |
|  ernie-2.0-base-en  | English  | Layer:12, Heads:12, Hidden:768  |
| ernie-2.0-large-en  | English  | Layer:24, Heads:16, Hidden:1024 |
|  ernie-3.0-base-zh  | Chinese  | Layer:12, Heads:12, Hidden:768  |
| ernie-3.0-medium-zh | Chinese  |  Layer:6, Heads:12, Hidden:768  |
|  ernie-3.0-mini-zh  | Chinese  |  Layer:6, Heads:12, Hidden:384  |
| ernie-3.0-micro-zh  | Chinese  |  Layer:4, Heads:12, Hidden:384  |
|  ernie-3.0-nano-zh  | Chinese  |  Layer:4, Heads:12, Hidden:312  |
|   ernie-health-zh   | Chinese  | Layer:12, Heads:12, Hidden:768  |
|    ernie-gram-zh    | Chinese  | Layer:12, Heads:12, Hidden:768  |

## Resources

You can find all the supported models from huggingface's model hub: [huggingface.co/nghuyong](https://huggingface.co/nghuyong), and model details from paddle's official
repo: [PaddleNLP](https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers/ERNIE/contents.html)
and [ERNIE's legacy branch](https://github.com/PaddlePaddle/ERNIE/tree/legacy/develop).

## ErnieConfig

[[autodoc]] ErnieConfig
    - all

## Ernie specific outputs

[[autodoc]] models.ernie.modeling_ernie.ErnieForPreTrainingOutput

## ErnieModel

[[autodoc]] ErnieModel
    - forward

## ErnieForPreTraining

[[autodoc]] ErnieForPreTraining
    - forward

## ErnieForCausalLM

[[autodoc]] ErnieForCausalLM
    - forward

## ErnieForMaskedLM

[[autodoc]] ErnieForMaskedLM
    - forward

## ErnieForNextSentencePrediction

[[autodoc]] ErnieForNextSentencePrediction
    - forward

## ErnieForSequenceClassification

[[autodoc]] ErnieForSequenceClassification
    - forward

## ErnieForMultipleChoice

[[autodoc]] ErnieForMultipleChoice
    - forward

## ErnieForTokenClassification

[[autodoc]] ErnieForTokenClassification
    - forward

## ErnieForQuestionAnswering

[[autodoc]] ErnieForQuestionAnswering
    - forward