🚀 蒸馏罗伯塔基础气候检测器模型卡片

本模型是一个微调后的语言模型，配备分类头，用于检测与气候相关的段落。它能有效助力气候相关文本的分类工作，为气候研究和信息处理提供有力支持。

🚀 快速开始

你可以使用管道对该模型进行文本分类：

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from transformers.pipelines.pt_utils import KeyDataset
import datasets
from tqdm.auto import tqdm

dataset_name = "climatebert/climate_detection"
model_name = "climatebert/distilroberta-base-climate-detector"

# If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading
dataset = datasets.load_dataset(dataset_name, split="test")

model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512)

pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)

# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)):
   print(out)

✨ 主要特性

这是经过微调的ClimateBERT语言模型，带有分类头，用于检测与气候相关的段落。以climatebert/distilroberta-base-climate-f语言模型为起点，distilroberta-base-climate-detector模型在climatebert/climate_detection数据集上进行了微调。

⚠️ 重要提示

该模型是在段落上进行训练的，在句子上的表现可能不佳。

📄 许可证

本模型使用的是Apache-2.0许可证。

📚 详细文档

引用信息

@techreport{bingler2023cheaptalk,
    title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
    author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
    type={Working paper},
    institution={Available at SSRN 3998435},
    year={2023}
}

模型信息