
# 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},
  
}
@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:

```python
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)
```
{"language": "en", "license": "apache-2.0", "tags": ["Env Claims"]}
transformers pytorch safetensors roberta text-classification Env Claims en license:apache-2.0 autotrain_compatible endpoints_compatible region:us