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README.md
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@@ -14,9 +14,30 @@ This is the EnvRoBERTa-environmental language model. A language model that is tr
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Using the [EnvRoBERTa-base](https://huggingface.co/ESGBERT/EnvRoBERTa-base) model as a starting point, the EnvRoBERTa-environmental Language Model is additionally fine-trained on a 2k environmental dataset to detect environmental text samples.
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Get
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More details can be found in the paper:
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```bibtex
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@article{Schimanski23ESGBERT,
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title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}},
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Using the [EnvRoBERTa-base](https://huggingface.co/ESGBERT/EnvRoBERTa-base) model as a starting point, the EnvRoBERTa-environmental Language Model is additionally fine-trained on a 2k environmental dataset to detect environmental text samples.
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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/climate_detection"
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tokenizer_name = "ESGBERT/EnvRoBERTa-environmental"
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model_name = "ESGBERT/EnvRoBERTa-environmental"
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_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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print(pipe("Scope 1 emissions are reported here on a like-for-like basis against the 2013 baseline and exclude emissions from additional vehicles used during repairs."))
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```
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## More details can be found in the paper
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```bibtex
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@article{Schimanski23ESGBERT,
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title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}},
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