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@@ -28,4 +28,30 @@ Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/clim
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  institution={Available at SSRN 3998435},
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  year={2023}
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  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  institution={Available at SSRN 3998435},
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  year={2023}
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  }
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+ ```
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+
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+ ## How to Get Started With the Model
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+
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+ You can use the model with a pipeline for text classification:
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+
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+ ```python
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+ from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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+ from transformers.pipelines.pt_utils import KeyDataset
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+ import datasets
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+ from tqdm.auto import tqdm
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+
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+ dataset_name = "climatebert/climate_sentiment"
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+ model_name = "climatebert/distilroberta-base-climate-sentiment"
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+
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+ # If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading
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+ dataset = datasets.load_dataset(dataset_name, split="test")
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+
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+ model = AutoModelForSequenceClassification.from_pretrained(model_name)
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+ tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512)
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+
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+ pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)
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+
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+ # See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
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+ for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)):
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+ print(out)
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  ```