Distilroberta Base Climate Specificity

🚀 用於distilroberta-base-climate-specificity的模型卡片

本模型卡片介紹了一個經過微調的語言模型，可將與氣候相關的段落分類為特定和非特定段落，為氣候相關文本的分析提供了有力工具。

🚀 快速開始

你可以使用管道進行文本分類來使用該模型：

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

dataset_name = "climatebert/climate_specificity"
model_name = "climatebert/distilroberta-base-climate-specificity"

# 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

這是一個經過微調的ClimateBERT語言模型，帶有一個分類頭，用於將與氣候相關的段落分類為特定和非特定段落。該模型以 climatebert/distilroberta-base-climate-f 語言模型為起點，在 climatebert/climate_specificity 數據集上進行了微調。

⚠️ 重要提示

此模型是在段落上進行訓練的，在句子上的表現可能不佳。

📚 詳細文檔

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}
}

📄 許可證

本模型使用的是Apache-2.0許可證。