readme: ---
  language:
  - en
  widget:
  - text: "In fiscal year 2019, we reduced our comprehensive carbon footprint for the fourth consecutive year—down 35 percent compared to 2015, when Apple’s carbon emissions peaked, even as net revenue increased by 11 percent over that same period. In the past year, we avoided over 10 million metric tons from our emissions reduction initiatives—like our Supplier Clean Energy Program, which lowered our footprint by 4.4 million metric tons. "
    example_title: "Reduced carbon footprint"
  - text: "We believe it is essential to establish validated conflict-free sources of 3TG within the Democratic Republic of the Congo (the “DRC”) and adjoining countries (together, with the DRC, the “Covered Countries”), so that these minerals can be procured in a way that contributes to economic growth and development in the region. To aid in this effort, we have established a conflict minerals policy and an internal team to implement the policy."
    example_title: "Conflict minerals policy"
  ---
  # Model Card for ESG-BERT
  Domain Specific BERT Model for Text Mining in Sustainable Investing
   
   
   
  # Model Details
   
  ## Model Description
   
   
   
  - **Developed by:** [Charan Pothireddi](https://www.linkedin.com/in/sree-charan-pothireddi-6a0a3587/) and [Parabole.ai](https://www.linkedin.com/in/sree-charan-pothireddi-6a0a3587/)
  - **Shared by [Optional]:** HuggingFace
  - **Model type:** Language model
  - **Language(s) (NLP):** en
  - **License:** More information needed
  - **Related Models:** 
    - **Parent Model:** BERT
  - **Resources for more information:** 
   - [GitHub Repo](https://github.com/mukut03/ESG-BERT)
   - [Blog Post](https://towardsdatascience.com/nlp-meets-sustainable-investing-d0542b3c264b?source=friends_link&sk=1f7e6641c3378aaff319a81decf387bf)
   
  # Uses
   
   
  ## Direct Use
   
  Text Mining in Sustainable Investing
   
  ## Downstream Use [Optional]
   
  The applications of ESG-BERT can be expanded way beyond just text classification. It can be fine-tuned to perform various other downstream NLP tasks in the domain of Sustainable Investing.
   
  ## Out-of-Scope Use
   
  The model should not be used to intentionally create hostile or alienating environments for people. 
  # Bias, Risks, and Limitations
   
   
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
   
   
  ## Recommendations
   
   
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
   
   
  # Training Details
   
  ## Training Data
   
  More information needed
   
  ## Training Procedure
   
  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
   
  ### Preprocessing
   
  More information needed
   
  ### Speeds, Sizes, Times
   
  More information needed
   
  # Evaluation
   
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  The fine-tuned model for text classification is also available [here](https://drive.google.com/drive/folders/1Qz4HP3xkjLfJ6DGCFNeJ7GmcPq65_HVe?usp=sharing). It can be used directly to make predictions using just a few steps.  First, download the fine-tuned pytorch_model.bin, config.json, and vocab.txt
   
  ### Factors
   
  More information needed
   
  ### Metrics
   
  More information needed
   
  ## Results 
   
  ESG-BERT was further trained on unstructured text data with accuracies of 100% and 98% for Next Sentence Prediction and Masked Language Modelling tasks. Fine-tuning ESG-BERT for text classification yielded an F-1 score of 0.90. For comparison, the general BERT (BERT-base) model scored 0.79 after fine-tuning, and the sci-kit learn approach scored 0.67.
   
  # Model Examination
   
  More information needed
   
  # Environmental Impact
   
   
  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
   
  - **Hardware Type:** More information needed
  - **Hours used:** More information needed
  - **Cloud Provider:**  information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
   
  # Technical Specifications [optional]
   
  ## Model Architecture and Objective
   
  More information needed
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### Software
   
  JDK 11 is needed to serve the model
   
  # Citation
   
  <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
   
  **BibTeX:**
   
  More information needed
   
  **APA:**
   
  More information needed
   
  # Glossary [optional]
   
  <