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Rename the model.

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  1. README.md +2 -2
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@@ -4,7 +4,7 @@ license: cc-by-nc-sa-4.0
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  # LLMLingua-2-Bert-base-Multilingual-Cased-MeetingBank
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- This model was introduced in the paper [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024)](). It is a [BERT multilingual base model (cased)](https://huggingface.co/google-bert/bert-base-multilingual-cased) finetuned to perform token classification for task agnostic prompt compression. The probability $p_{preserve}$ of each token $x_i$ is used as the metric for compression. This model is trained on an extractive text compression dataset constructed with the methodology proposed in the [LLMLingua-2], using training examples from [MeetingBank (Hu et al, 2023)](https://meetingbank.github.io/) as the seed data.
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  For more details, please check the home page of [LLMLingua-2]() and [LLMLingua Series](https://llmlingua.com/).
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@@ -13,7 +13,7 @@ For more details, please check the home page of [LLMLingua-2]() and [LLMLingua S
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  from llmlingua import PromptCompressor
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  compressor = PromptCompressor(
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- model_name="qianhuiwu/llmlingua-2-bert-base-multilingual-cased-meetingbank",
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  use_llmlingua2=True
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  )
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  # LLMLingua-2-Bert-base-Multilingual-Cased-MeetingBank
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+ This model was introduced in the paper [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024)](). It is a [BERT multilingual base model (cased)](https://huggingface.co/google-bert/bert-base-multilingual-cased) finetuned to perform token classification for task agnostic prompt compression. The probability $p_{preserve}$ of each token $x_i$ is used as the metric for compression. This model is trained on [an extractive text compression dataset]() constructed with the methodology proposed in the [LLMLingua-2], using training examples from [MeetingBank (Hu et al, 2023)](https://meetingbank.github.io/) as the seed data.
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  For more details, please check the home page of [LLMLingua-2]() and [LLMLingua Series](https://llmlingua.com/).
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  from llmlingua import PromptCompressor
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  compressor = PromptCompressor(
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+ model_name="microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank",
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  use_llmlingua2=True
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  )
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