Update
- The paper has been accepted to EMNLP 2024 Findings: https://aclanthology.org/2024.findings-emnlp.343/
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
@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
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