Total runs:
103.5K
Run Growth:
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Growth Rate:
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Sponsored by
Seko
d4data / EnviDueDiligence_NER
Model's Last Updated: December 30 2023
token-classification
Introduction of EnviDueDiligence_NER
Model Details of EnviDueDiligence_NER
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
Usage
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="d4data/EnviDueDiligence_NER")
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("d4data/EnviDueDiligence_NER")
model = AutoModelForTokenClassification.from_pretrained("d4data/EnviDueDiligence_NER")
Author
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:
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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Runs of d4data EnviDueDiligence_NER on huggingface.co
135
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24-hour runs
63
3-day runs
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7-day runs
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30-day runs
More Information About EnviDueDiligence_NER huggingface.co Model
Url of EnviDueDiligence_NER
EnviDueDiligence_NER huggingface.co Url
Provider of EnviDueDiligence_NER huggingface.co
Other API from d4data
Updated:July 02 2023
Total runs:
4.5K
Run Growth:
-1.4K
Growth Rate:
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Total runs:
10
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88.89%
Total runs:
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Growth Rate:
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