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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- Token Classification |
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widget: |
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- text: "Monitored Natural Attenuation and, if necessary as a contingency, In Situ Chemical Oxidation to address the injection of a strong chemical oxidant to chemically treat the before the contingency can be implemented at the spill site." |
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example_title: "example 1" |
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- text: "Site was identified as a potential source of groundwater contamination after the City performed Assessments were investigated further for potential contamination." |
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example_title: "example 2" |
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- text: "Chromium releases from the UST is probably a major contributor to groundwater contamination in this area." |
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example_title: "example 3" |
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--- |
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## About the Model |
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An Environmental Named Entity Recognition model, trained on dataset from USEPA to recognize environmental due diligence (7 entities) from a given text corpus (remediation reports, record of decision, 5 year record etc). This model was built on top of distilbert-base-uncased |
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- Dataset: https://data.mendeley.com/datasets/tx6vmd4g9p/4 |
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- Dataset Reasearch Paper: https://doi.org/10.1016/j.dib.2022.108579 |
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## Usage |
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The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("token-classification", model="d4data/EnviDueDiligence_NER") |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("d4data/EnviDueDiligence_NER") |
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model = AutoModelForTokenClassification.from_pretrained("d4data/EnviDueDiligence_NER") |
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``` |
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## Author |
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This model is part of the Research topic "Environmental Due Diligence" conducted by Deepak John Reji, Afreen Aman. If you use this work (code, model or dataset), please cite: |
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> Aman, A. and Reji, D.J., 2022. EnvBert: An NLP model for Environmental Due Diligence data classification. Software Impacts, 14, p.100427. |
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## You can support me here :) |
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<a href="https://www.buymeacoffee.com/deepakjohnreji" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> |
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