metadata
license: apache-2.0
tags:
- ESG
- finance
language:
- en
pipeline_tag: text-classification
Main information
We introduce the model for multilabel ESG risks classification. There is 47 classes methodology with granularial risk definition.
Usage
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
model = ESGify.from_pretrained('ai-lab/ESGify')
tokenizer = AutoTokenizer.from_pretrained('ai-lab/ESGify')
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)
# We also recommend preprocess texts with using FLAIR model
from flair.data import Sentence
from flair.nn import Classifier
from torch.utils.data import DataLoader
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
stop_words = set(stopwords.words('english'))
tagger = Classifier.load('ner-ontonotes-large')
tag_list = ['FAC','LOC','ORG','PERSON']
texts_with_masks = []
for example_sent in texts:
filtered_sentence = []
word_tokens = word_tokenize(example_sent)
# converts the words in word_tokens to lower case and then checks whether
#they are present in stop_words or not
for w in word_tokens:
if w.lower() not in stop_words:
filtered_sentence.append(w)
# make a sentence
sentence = Sentence(' '.join(filtered_sentence))
# run NER over sentence
tagger.predict(sentence)
sent = ' '.join(filtered_sentence)
k = 0
new_string = ''
start_t = 0
for i in sentence.get_labels():
info = i.to_dict()
val = info['value']
if info['confidence']>0.8 and val in tag_list :
if i.data_point.start_position>start_t :
new_string+=sent[start_t:i.data_point.start_position]
start_t = i.data_point.end_position
new_string+= f'<{val}>'
new_string+=sent[start_t:-1]
texts_with_masks.append(new_string)
to_model = tokenizer.batch_encode_plus(
texts_with_masks,
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)
Background
The project aims to develop the ESG Risks classification model with a custom ESG risks definition methodology.
Training procedure
Pre-training
We use the pretrained microsoft/mpnet-base model.
Next, we do the domain-adaptation procedure by Mask Language Modeling pertaining with using texts of ESG reports.
Training data
We use the ESG news dataset of 2000 texts with manually annotation of ESG specialists.