Model Card for ESG-BERT

Domain Specific BERT Model for Text Mining in Sustainable Investing

Model Details

Model Description

• Developed by: Mukut Mukherjee, Charan Pothireddi and Parabole.ai
• 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
• Blog Post

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) and Bender et al. (2021)). 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

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. 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 presented in Lacoste et al. (2019).

• 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

BibTeX:

More information needed

APA:

More information needed

Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

Mukut Mukherjee, Charan Pothireddi and Parabole.ai, in collaboration with the Ezi Ozoani and the HuggingFace Team

Model Card Contact

More information needed

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

pip install torchserve torch-model-archiver
 
pip install torchvision
 
pip install transformers
 

Next up, we'll set up the handler script. It is a basic handler for text classification that can be improved upon. Save this script as "handler.py" in your directory. [1]

 
from abc import ABC
 
import json
 
import logging
 
import os
 
import torch
 
from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
from ts.torch_handler.base_handler import BaseHandler
 
logger = logging.getLogger(__name__)
 
class TransformersClassifierHandler(BaseHandler, ABC):
 
   """
 
   Transformers text classifier handler class. This handler takes a text (string) and
 
   as input and returns the classification text based on the serialized transformers checkpoint.
 
   """
 
   def __init__(self):
 
       super(TransformersClassifierHandler, self).__init__()
 
       self.initialized = False
 
def initialize(self, ctx):
 
       self.manifest = ctx.manifest
 
properties = ctx.system_properties
 
       model_dir = properties.get("model_dir")
 
       self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
 
# Read model serialize/pt file
 
       self.model = AutoModelForSequenceClassification.from_pretrained(model_dir)
 
       self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
 
self.model.to(self.device)
 
       self.model.eval()
 
logger.debug('Transformer model from path {0} loaded successfully'.format(model_dir))
 
# Read the mapping file, index to object name
 
       mapping_file_path = os.path.join(model_dir, "index_to_name.json")
 
if os.path.isfile(mapping_file_path):
 
           with open(mapping_file_path) as f:
 
               self.mapping = json.load(f)
 
       else:
 
           logger.warning('Missing the index_to_name.json file. Inference output will not include class name.')
 
self.initialized = True
 
def preprocess(self, data):
 
       """ Very basic preprocessing code - only tokenizes.
 
           Extend with your own preprocessing steps as needed.
 
       """
 
       text = data[0].get("data")
 
       if text is None:
 
           text = data[0].get("body")
 
       sentences = text.decode('utf-8')
 
       logger.info("Received text: '%s'", sentences)
 
inputs = self.tokenizer.encode_plus(
 
           sentences,
 
           add_special_tokens=True,
 
           return_tensors="pt"
 
       )
 
       return inputs
 
def inference(self, inputs):
 
       """
 
       Predict the class of a text using a trained transformer model.
 
       """
 
       # NOTE: This makes the assumption that your model expects text to be tokenized 
 
       # with "input_ids" and "token_type_ids" - which is true for some popular transformer models, e.g. bert.
 
       # If your transformer model expects different tokenization, adapt this code to suit
 
       # its expected input format.
 
       prediction = self.model(
 
           inputs['input_ids'].to(self.device),
 
           token_type_ids=inputs['token_type_ids'].to(self.device)
 
       )[0].argmax().item()
 
       logger.info("Model predicted: '%s'", prediction)
 
if self.mapping:
 
           prediction = self.mapping[str(prediction)]
 
return [prediction]
 
def postprocess(self, inference_output):
 
       # TODO: Add any needed post-processing of the model predictions here
 
       return inference_output
 
_service = TransformersClassifierHandler()
 
def handle(data, context):
 
   try:
 
       if not _service.initialized:
 
           _service.initialize(context)
 
if data is None:
 
           return None
 
data = _service.preprocess(data)
 
       data = _service.inference(data)
 
       data = _service.postprocess(data)
 
return data
 
   except Exception as e:
 
       raise e
 
 
 

TorcheServe uses a format called MAR (Model Archive). We can convert our PyTorch model to a .mar file using this command:

 
torch-model-archiver --model-name "bert" --version 1.0 --serialized-file ./bert_model/pytorch_model.bin --extra-files "./bert_model/config.json,./bert_model/vocab.txt" --handler "./handler.py"
 

Move the .mar file into a new directory:

 
mkdir model_store && mv bert.mar model_store
 

Finally, we can start TorchServe using the command:

 
torchserve --start --model-store model_store --models bert=bert.mar
 

We can now query the model from another terminal window using the Inference API. We pass a text file containing text that the model will try to classify.

 
curl -X POST http://127.0.0.1:8080/predictions/bert -T predict.txt
 

This returns a label number which correlates to a textual label. This is stored in the label_dict.txt dictionary file.

 
__label__Business_Ethics :  0
 
__label__Data_Security :  1
 
__label__Access_And_Affordability :  2
 
__label__Business_Model_Resilience :  3
 
__label__Competitive_Behavior :  4
 
__label__Critical_Incident_Risk_Management :  5
 
__label__Customer_Welfare :  6
 
__label__Director_Removal :  7
 
__label__Employee_Engagement_Inclusion_And_Diversity :  8
 
__label__Employee_Health_And_Safety :  9
 
__label__Human_Rights_And_Community_Relations :  10
 
__label__Labor_Practices :  11
 
__label__Management_Of_Legal_And_Regulatory_Framework :  12
 
__label__Physical_Impacts_Of_Climate_Change :  13
 
__label__Product_Quality_And_Safety :  14
 
__label__Product_Design_And_Lifecycle_Management :  15
 
__label__Selling_Practices_And_Product_Labeling :  16
 
__label__Supply_Chain_Management :  17
 
__label__Systemic_Risk_Management :  18
 
__label__Waste_And_Hazardous_Materials_Management :  19
 
__label__Water_And_Wastewater_Management :  20
 
__label__Air_Quality :  21
 
__label__Customer_Privacy :  22
 
__label__Ecological_Impacts :  23
 
__label__Energy_Management :  24
 
__label__GHG_Emissions :  25
 

<\details>