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: 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>