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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- Env Claims |
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--- |
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# Model Card for environmental-claims |
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## Model Description |
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Trained for specific Environmentional claims, for emerging markets models | - | |
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## Citation Information |
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@misc{stammbach2022environmentalclaims, |
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title = {A Dataset for Detecting Real-World Environmental Claims}, |
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author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus}, |
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year = {2022}, |
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} |
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@misc{ |
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title = {Custom Emerging markets}, |
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author = {Tushar Aggarwal}, |
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year = {December 2022}, |
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} |
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## How to Get Started With the Model |
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You can use the model with a pipeline for text classification: |
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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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dataset_name = "climatebert/environmental_claims" |
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dataset = datasets.load_dataset(dataset_name, split="test") |
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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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pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) |
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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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``` |
