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--- |
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language: en |
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license: apache-2.0 |
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datasets: climatebert/environmental_claims |
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tags: |
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- ClimateBERT |
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- climate |
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--- |
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# Model Card for environmental-claims |
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## Model Description |
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The environmental-claims model is fine-tuned on the [EnvironmentalClaims](https://huggingface.co/datasets/climatebert/environmental_claims) dataset by using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) model as pre-trained language model. The underlying methodology can be found in our [research paper](https://arxiv.org/abs/2209.00507). |
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## Climate Performance Model Card |
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| environmental-claims | | |
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|--------------------------------------------------------------------------|----------------| |
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| 1. Is the resulting model publicly available? | Yes | |
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| 2. How much time does the training of the final model take? | < 5 min | |
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| 3. How much time did all experiments take (incl. hyperparameter search)? | 60 hours | |
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| 4. What was the power of GPU and CPU? | 0.3 kW | |
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| 5. At which geo location were the computations performed? | Switzerland | |
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| 6. What was the energy mix at the geo location? | 89 gCO2eq/kWh | |
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| 7. How much CO2eq was emitted to train the final model? | 2.2 g | |
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| 8. How much CO2eq was emitted for all experiments? | 1.6 kg | |
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| 9. What is the average CO2eq emission for the inference of one sample? | 0.0067 mg | |
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| 10. Which positive environmental impact can be expected from this work? | This work can help detect and evaluate environmental claims and thus have a positive impact on the environment in the future. | |
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| 11. Comments | - | |
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## Citation Information |
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```bibtex |
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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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doi = {10.48550/ARXIV.2209.00507}, |
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url = {https://arxiv.org/abs/2209.00507}, |
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publisher = {arXiv}, |
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} |
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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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model_name = "climatebert/environmental-claims" |
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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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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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``` |
