|
--- |
|
library_name: peft |
|
license: apache-2.0 |
|
pipeline_tag: text-classification |
|
tags: |
|
- hatespeech |
|
- hatecot |
|
- cot |
|
- llama |
|
--- |
|
|
|
## Introduction |
|
This is the LoRA-adapater for the Llama-7B introduced in the paper |
|
*HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models*. |
|
The base model is instruction-finetuned on 52,000 samples that includes augmented humman annotation to produce |
|
legible explanations based on predefined criteria in the **provided definition**. |
|
|
|
|
|
To use the model, please load along with the original Llama model (detailed configuration in the *Training Procedure*). |
|
For instruction to load Peft models: https://huggingface.co./docs/transformers/main/en/peft |
|
|
|
These adapters can also be finetuned on a new set of data. See the article for more details. |
|
|
|
## Usage |
|
Use the following template to prompt the model: |
|
``` |
|
### Instruction |
|
Perform this task by considering the following Definitions. |
|
Based on the message, label the input as only one of the following categories: |
|
[Class 1], [Class 2], ..., or [Class N]. |
|
Provide a brief paragraph to explain step-by-step why the post should be classsified |
|
with the provided Label based on the given Definitions. If this post targets a group or |
|
entity relevant to the definition of the specified Label, explain who this target is and how |
|
that leads to that Label. |
|
Append the string '<END>' to the end of your response. Provide your response in the following format: |
|
EXPLANATION: [text] |
|
LABEL:[text] <END> |
|
### Definitions: |
|
[Class 1]: [Definition 1] |
|
[Class 2]: [Definition 2] |
|
... |
|
[Class N]: [Definition 3] |
|
### Input |
|
{post} |
|
### Response: |
|
``` |
|
|
|
## Citation |
|
```bibtex |
|
@article{nghiem2024hatecot, |
|
title={HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models}, |
|
author={Nghiem, Huy and Daum{\'e} III, Hal}, |
|
journal={arXiv preprint arXiv:2403.11456}, |
|
year={2024} |
|
} |
|
``` |
|
|
|
## Original Model |
|
Please visit the main repository to gain permission to download original model weights. |
|
|
|
https://huggingface.co./meta-llama |
|
|
|
|
|
|
|
## Training procedure |
|
|
|
|
|
The following `bitsandbytes` quantization config was used during training: |
|
- quant_method: bitsandbytes |
|
- load_in_8bit: True |
|
- load_in_4bit: False |
|
- llm_int8_threshold: 6.0 |
|
- llm_int8_skip_modules: None |
|
- llm_int8_enable_fp32_cpu_offload: False |
|
- llm_int8_has_fp16_weight: False |
|
- bnb_4bit_quant_type: nf4 |
|
- bnb_4bit_use_double_quant: False |
|
- bnb_4bit_compute_dtype: float16 |
|
### Framework versions |
|
|
|
|
|
- PEFT 0.5.0 |
