|
--- |
|
license: apache-2.0 |
|
tags: |
|
- ESG |
|
- finance |
|
language: |
|
- en |
|
|
|
--- |
|
# About ESGify |
|
**ESGify** is a model for multilabel news classification with respect to ESG risks. Our custom methodology includes 46 ESG classes and 1 non-relevant to ESG class, resulting in 47 classes in total: |
|
|
|
| E | S | G | |
|
| ----------- | ----------- | ----------- | |
|
| **Biodiversity** | **Communities Health and Safety** | **Legal Proceedings & Law Violations** | |
|
| **Emergencies (Environmental)** | **Land Acquisition and Resettlement (S)** | **Corporate Governance** | |
|
| **Hazardous Materials Management** | **Emergencies (Social)** | **Responsible Investment & Greenwashing** | |
|
| **Environmental Management** | **Human Rights** | **Economic Crime** | |
|
| **Landscape Transformation** | **Labor Relations Management** | **Disclosure** | |
|
| **Climate Risks** | **Freedom of Association and Right to Organise** | **Values and Ethics** | |
|
| **Surface Water Pollution** | **Employee Health and Safety** | **Risk Management and Internal Control** | |
|
| **Animal Welfare** | **Product Safety and Quality** | **Strategy Implementation** | |
|
| **Water Consumption** | **Indigenous People** | **Supply Chain (Economic / Governance)** | |
|
| **Greenhouse Gas Emissions** | **Cultural Heritage** || |
|
| **Air Pollution** | **Forced Labour** || |
|
| **Waste Management** | **Supply Chain (Social)** || |
|
| **Soil and Groundwater Impact** | **Discrimination** || |
|
| **Wastewater Management** | **Minimum Age and Child Labour** || |
|
| **Natural Resources** | **Data Safety** || |
|
| **Physical Impacts** | **Retrenchment** || |
|
| **Supply Chain (Environmental)** ||| |
|
| **Planning Limitations** ||| |
|
| **Energy Efficiency and Renewables** ||| |
|
| **Land Acquisition and Resettlement (E)** ||| |
|
| **Land Rehabilitation** ||| |
|
|
|
|
|
# Usage |
|
|
|
ESGify is based on MPNet architecture but with a custom classification head. The ESGify class is defined is follows. |
|
|
|
```python |
|
from collections import OrderedDict |
|
from transformers import MPNetPreTrainedModel, MPNetModel, AutoTokenizer |
|
import torch |
|
|
|
# Mean Pooling - Take attention mask into account for correct averaging |
|
def mean_pooling(model_output, attention_mask): |
|
token_embeddings = model_output #First element of model_output contains all token embeddings |
|
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
|
|
|
# Definition of ESGify class because of custom,sentence-transformers like, mean pooling function and classifier head |
|
class ESGify(MPNetPreTrainedModel): |
|
"""Model for Classification ESG risks from text.""" |
|
|
|
def __init__(self,config): #tuning only the head |
|
""" |
|
""" |
|
super().__init__(config) |
|
# Instantiate Parts of model |
|
self.mpnet = MPNetModel(config,add_pooling_layer=False) |
|
self.id2label = config.id2label |
|
self.label2id = config.label2id |
|
self.classifier = torch.nn.Sequential(OrderedDict([('norm',torch.nn.BatchNorm1d(768)), |
|
('linear',torch.nn.Linear(768,512)), |
|
('act',torch.nn.ReLU()), |
|
('batch_n',torch.nn.BatchNorm1d(512)), |
|
('drop_class', torch.nn.Dropout(0.2)), |
|
('class_l',torch.nn.Linear(512 ,47))])) |
|
|
|
|
|
def forward(self, input_ids, attention_mask): |
|
# Feed input to mpnet model |
|
outputs = self.mpnet(input_ids=input_ids, |
|
attention_mask=attention_mask) |
|
|
|
# mean pooling dataset and eed input to classifier to compute logits |
|
logits = self.classifier( mean_pooling(outputs['last_hidden_state'],attention_mask)) |
|
|
|
# apply sigmoid |
|
logits = 1.0 / (1.0 + torch.exp(-logits)) |
|
return logits |
|
``` |
|
|
|
After defining model class, we initialize ESGify and tokenizer with the pre-trained weights |
|
|
|
```python |
|
model = ESGify.from_pretrained('ai-lab/ESGify') |
|
tokenizer = AutoTokenizer.from_pretrained('ai-lab/ESGify') |
|
``` |
|
|
|
Getting results from the model: |
|
|
|
```python |
|
texts = ['text1','text2'] |
|
to_model = tokenizer.batch_encode_plus( |
|
texts, |
|
add_special_tokens=True, |
|
max_length=512, |
|
return_token_type_ids=False, |
|
padding="max_length", |
|
truncation=True, |
|
return_attention_mask=True, |
|
return_tensors='pt', |
|
) |
|
results = model(**to_model) |
|
``` |
|
|
|
To identify top-3 classes by relevance and their scores: |
|
|
|
```python |
|
for i in torch.topk(results, k=3).indices.tolist()[0]: |
|
print(f"{model.id2label[i]}: {np.round(results.flatten()[i].item(), 3)}") |
|
``` |
|
|
|
For example, for the news "She faced employment rejection because of her gender", we get the following top-3 labels: |
|
``` |
|
Discrimination: 0.944 |
|
Strategy Implementation: 0.82 |
|
Indigenous People: 0.499 |
|
``` |
|
|
|
Before training our model, we masked words related to Organisation, Date, Country, and Person to prevent false associations between these entities and risks. Hence, we recommend to process text with FLAIR NER model before inference. |
|
An example of such preprocessing is given in https://colab.research.google.com/drive/15YcTW9KPSWesZ6_L4BUayqW_omzars0l?usp=sharing. |
|
|
|
|
|
# Training procedure |
|
|
|
We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. |
|
Next, we do the domain-adaptation procedure by Mask Language Modeling with using texts of ESG reports. |
|
Finally, we fine-tune our model on 2000 texts with manually annotation of ESG specialists. |
|
|
|
|