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--- |
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license: apache-2.0 |
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tags: |
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- ESG |
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- finance |
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language: |
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- en |
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--- |
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# About ESGify |
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<img src="ESGify_logo.jpeg" alt="image" width="20%" height="auto"> |
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**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: |
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# Usage |
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ESGify is based on MPNet architecture but with a custom classification head. The ESGify class is defined is follows. |
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```python |
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from collections import OrderedDict |
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from transformers import MPNetPreTrainedModel, MPNetModel, AutoTokenizer |
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import torch |
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# Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output #First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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# Definition of ESGify class because of custom,sentence-transformers like, mean pooling function and classifier head |
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class ESGify(MPNetPreTrainedModel): |
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"""Model for Classification ESG risks from text.""" |
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def __init__(self,config): #tuning only the head |
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""" |
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""" |
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super().__init__(config) |
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# Instantiate Parts of model |
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self.mpnet = MPNetModel(config,add_pooling_layer=False) |
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self.id2label = config.id2label |
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self.label2id = config.label2id |
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self.classifier = torch.nn.Sequential(OrderedDict([('norm',torch.nn.BatchNorm1d(768)), |
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('linear',torch.nn.Linear(768,512)), |
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('act',torch.nn.ReLU()), |
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('batch_n',torch.nn.BatchNorm1d(512)), |
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('drop_class', torch.nn.Dropout(0.2)), |
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('class_l',torch.nn.Linear(512 ,47))])) |
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def forward(self, input_ids, attention_mask): |
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# Feed input to mpnet model |
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outputs = self.mpnet(input_ids=input_ids, |
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attention_mask=attention_mask) |
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# mean pooling dataset and eed input to classifier to compute logits |
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logits = self.classifier( mean_pooling(outputs['last_hidden_state'],attention_mask)) |
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# apply sigmoid |
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logits = 1.0 / (1.0 + torch.exp(-logits)) |
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return logits |
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``` |
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After defining model class, we initialize ESGify and tokenizer with the pre-trained weights |
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```python |
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model = ESGify.from_pretrained('ai-lab/ESGify') |
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tokenizer = AutoTokenizer.from_pretrained('ai-lab/ESGify') |
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``` |
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Getting results from the model: |
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```python |
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texts = ['text1','text2'] |
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to_model = tokenizer.batch_encode_plus( |
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texts, |
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add_special_tokens=True, |
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max_length=512, |
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return_token_type_ids=False, |
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padding="max_length", |
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truncation=True, |
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return_attention_mask=True, |
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return_tensors='pt', |
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) |
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results = model(**to_model) |
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``` |
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To identify top-3 classes by relevance and their scores: |
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```python |
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for i in torch.topk(results, k=3).indices.tolist()[0]: |
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print(f"{model.id2label[i]}: {np.round(results.flatten()[i].item(), 3)}") |
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``` |
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For example, for the news "She faced employment rejection because of her gender", we get the following top-3 labels: |
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``` |
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Discrimination: 0.944 |
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Strategy Implementation: 0.82 |
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Indigenous People: 0.499 |
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``` |
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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. |
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An example of such preprocessing is given in https://colab.research.google.com/drive/15YcTW9KPSWesZ6_L4BUayqW_omzars0l?usp=sharing. |
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# Training procedure |
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We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. |
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Next, we do the domain-adaptation procedure by Mask Language Modeling with using texts of ESG reports. |
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Finally, we fine-tune our model on 2000 texts with manually annotation of ESG specialists. |
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