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ERNIE

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, ERNIE2.0, ERNIE3.0, ERNIE-Gram, ERNIE-health 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, and Ernie 4.5 MoE.

[!TIP] This model was contributed by nghuyong, and the official code can be found in 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">
python
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">
python
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",
    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> </hfoptions>

Notes

Model variants are available in different sizes and languages.

Model NameLanguageDescription
ernie-1.0-base-zhChineseLayer:12, Heads:12, Hidden:768
ernie-2.0-base-enEnglishLayer:12, Heads:12, Hidden:768
ernie-2.0-large-enEnglishLayer:24, Heads:16, Hidden:1024
ernie-3.0-base-zhChineseLayer:12, Heads:12, Hidden:768
ernie-3.0-medium-zhChineseLayer:6, Heads:12, Hidden:768
ernie-3.0-mini-zhChineseLayer:6, Heads:12, Hidden:384
ernie-3.0-micro-zhChineseLayer:4, Heads:12, Hidden:384
ernie-3.0-nano-zhChineseLayer:4, Heads:12, Hidden:312
ernie-health-zhChineseLayer:12, Heads:12, Hidden:768
ernie-gram-zhChineseLayer:12, Heads:12, Hidden:768

Resources

You can find all the supported models from huggingface's model hub: huggingface.co/nghuyong, and model details from paddle's official repo: PaddleNLP and ERNIE's legacy branch.

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