Instructions to use Afreen/ner_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Afreen/ner_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Afreen/ner_test")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Afreen/ner_test") model = AutoModelForTokenClassification.from_pretrained("Afreen/ner_test") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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license: mit
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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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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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license: mit
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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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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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