---
language:
- en
tags:
- Text Classification
co2_eq_emissions: 0.1069 Kg
widget:
- text: "At the every month post-injection monitoring event, TCE, carbon tetrachloride, and chloroform concentrations were above CBSGs in three of the wells"
  example_title: "Remediation Standards"
- text: "TRPH exceedances were observed in the subsurface soils immediately above the water table and there are no TRPH exceedances in surface soils."
  example_title: "Extent of Contamination"
- 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"
  example_title: "Geology"

---

## About the Model
An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences. This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.

- Dataset : Custom Data
- Carbon emission 0.1069 Kg

| Train Accuracy | Validation Accuracy | Train loss | Test loss |
|---------------:| -------------------:| ----------:|----------:|
|          99.10 |               01.00 |       0.04 |     0.003 |

## Usage
The easiest way is to load through the pipeline object offered by transformers library.
```python
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("d4data/environmental-due-diligence-model")
model = TFAutoModelForSequenceClassification.from_pretrained("d4data/environmental-due-diligence-model")

classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) # cuda = 0,1 based on gpu availability
classifier("The irony, of course, is that the exhibit that invites people to throw trash at vacuuming Ivanka Trump lookalike reflects every stereotype feminists claim to stand against, oversexualizing Ivanka’s body and ignoring her hard work.")
```

## Author
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:
> Environmental Due Diligence, (2020), GitHub repository, <...>