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
| license: mit | |
| widget: | |
| - 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." | |
| example_title: "example 1" | |
| - text: "Site was identified as a potential source of groundwater contamination after the City performed Assessments were investigated further for potential contamination." | |
| example_title: "example 2" | |
| - text: "Chromium releases from the UST is probably a major contributor to groundwater contamination in this area." | |
| example_title: "example 3" | |
| ## About the Model | |
| 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 | |
| - Dataset: https://data.mendeley.com/datasets/tx6vmd4g9p/4 | |
| - Dataset Reasearch Paper: https://doi.org/10.1016/j.dib.2022.108579 | |
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