metadata

language: en
license: apache-2.0
datasets: climatebert/environmental_claims + custom annotations
tags:
  - Env Claims

Model Card for environmental-claims

Model Description

Trained for specific Environmentional claims, for emerging markets models | - |

Citation Information

@misc{stammbach2022environmentalclaims, title = {A Dataset for Detecting Real-World Environmental Claims}, author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus}, year = {2022}, doi = {10.48550/ARXIV.2209.00507}, url = {https://arxiv.org/abs/2209.00507}, publisher = {arXiv}, } @misc{ title = {Custom Emerging markets}, author = {Tushar Aggarwal}, year = {December 2022}, }

How to Get Started With the Model

You can use the model with a pipeline for text classification:

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

dataset_name = "climatebert/environmental_claims"



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)