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--- |
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language: |
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- en |
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tags: |
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- Text Classification |
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co2_eq_emissions: 0.1069 Kg |
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widget: |
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- text: "At the every month post-injection monitoring event, TCE, carbon tetrachloride, and chloroform concentrations were above CBSGs in three of the wells" |
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example_title: "Remediation Standards" |
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- text: "TRPH exceedances were observed in the subsurface soils immediately above the water table and there are no TRPH exceedances in surface soils." |
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example_title: "Extent of Contamination" |
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- text: "weathered shale was encountered below the surface area with fluvial deposits. Sediments in the coastal plain region are found above and below the bedrock with sandstones and shales that form the basement rock" |
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example_title: "Geology" |
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--- |
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## About the Model |
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An Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of contamination, the types of contaminants present at the site, the flow of contaminants and their interaction with ground water, surface water and other surrounding water bodies . |
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This model was built on top of distilbert-base-uncased model and trained for 10 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512. |
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- Dataset : Custom Data |
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- Carbon emission 0.1069 Kg |
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| Train Accuracy | Validation Accuracy | Train loss | Test loss | |
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|---------------:| -------------------:| ----------:|----------:| |
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| 99.10 | 01.00 | 0.04 | 0.003 | |
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## Usage |
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The easiest way is to load through the pipeline object offered by transformers library. |
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```python |
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from transformers import AutoTokenizer, TFAutoModelForSequenceClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("d4data/environmental-due-diligence-model") |
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model = TFAutoModelForSequenceClassification.from_pretrained("d4data/environmental-due-diligence-model") |
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classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) # cuda = 0,1 based on gpu availability |
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classifier("At the every month post-injection monitoring event, TCE, carbon tetrachloride, and chloroform concentrations were above CBSGs in three of the wells") |
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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, Shaina Raza. If you use this work (code, model or dataset), please cite as: |
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> Environmental Due Diligence, (2020), GitHub repository, <...> |
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