Refine model card.
Browse files
README.md
CHANGED
|
@@ -6,6 +6,8 @@ license: cc-by-nc-sa-4.0
|
|
| 6 |
|
| 7 |
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 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.
|
| 8 |
|
|
|
|
|
|
|
| 9 |
## Usage
|
| 10 |
```python
|
| 11 |
from llmlingua import PromptCompressor
|
|
|
|
| 6 |
|
| 7 |
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 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.
|
| 8 |
|
| 9 |
+
For more details, please check the home page of [LLMLingua-2]() and [LLMLingua Series](https://llmlingua.com/).
|
| 10 |
+
|
| 11 |
## Usage
|
| 12 |
```python
|
| 13 |
from llmlingua import PromptCompressor
|