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README.md
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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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## Usage
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```python
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from llmlingua import PromptCompressor
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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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## Usage
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```python
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from llmlingua import PromptCompressor
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