Llmlingua 2 Xlm Roberta Large Meetingbank

🚀 LLMLingua-2-Bert-base-Multilingual-Cased-MeetingBank

This model is finetuned for task - agnostic prompt compression, leveraging techniques from LLMLingua - 2 and trained on a specialized dataset.

🚀 Quick Start

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 [XLM - RoBERTa (large - sized model)](https://huggingface.co/FacebookAI/xlm - roberta - large) 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 [the extractive text compression dataset](https://huggingface.co/datasets/microsoft/MeetingBank - LLMCompressed) constructed with the methodology proposed in the LLMLingua - 2, using training examples from MeetingBank (Hu et al, 2023) as the seed data.

You can evaluate the model on downstream tasks such as question answering (QA) and summarization over compressed meeting transcripts using [this dataset](https://huggingface.co/datasets/microsoft/MeetingBank - QA - Summary).

For more details, please check the home page of LLMLingua - 2 and LLMLingua Series.

💻 Usage Examples

Basic Usage

from llmlingua import PromptCompressor

compressor = PromptCompressor(
    model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
    use_llmlingua2=True
)

original_prompt = """John: So, um, I've been thinking about the project, you know, and I believe we need to, uh, make some changes. I mean, we want the project to succeed, right? So, like, I think we should consider maybe revising the timeline.
Sarah: I totally agree, John. I mean, we have to be realistic, you know. The timeline is, like, too tight. You know what I mean? We should definitely extend it.
"""
results = compressor.compress_prompt_llmlingua2(
    original_prompt,
    rate=0.6,
    force_tokens=['\n', '.', '!', '?', ','],
    chunk_end_tokens=['.', '\n'],
    return_word_label=True,
    drop_consecutive=True
)

print(results.keys())
print(f"Compressed prompt: {results['compressed_prompt']}")
print(f"Original tokens: {results['origin_tokens']}")
print(f"Compressed tokens: {results['compressed_tokens']}")
print(f"Compression rate: {results['rate']}")

# get the annotated results over the original prompt
word_sep = "\t\t|\t\t"
label_sep = " "
lines = results["fn_labeled_original_prompt"].split(word_sep)
annotated_results = []
for line in lines:
    word, label = line.split(label_sep)
    annotated_results.append((word, '+') if label == '1' else (word, '-')) # list of tuples: (word, label)
print("Annotated results:")
for word, label in annotated_results[:10]:
    print(f"{word} {label}")

📄 License

This project is released under the MIT license.

📚 Citation

@article{wu2024llmlingua2,
    title = "{LLML}ingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression",
    author = "Zhuoshi Pan and Qianhui Wu and Huiqiang Jiang and Menglin Xia and Xufang Luo and Jue Zhang and Qingwei Lin and Victor Ruhle and Yuqing Yang and Chin-Yew Lin and H. Vicky Zhao and Lili Qiu and Dongmei Zhang",
    url = "https://arxiv.org/abs/2403.12968",
    journal = "ArXiv preprint",
    volume = "abs/2403.12968",
    year = "2024",
}