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"""
Hugging Face compatible ESG Hierarchical Multi-Task Learning Model
"""
import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig, AutoModel, AutoTokenizer
from transformers.modeling_outputs import SequenceClassifierOutput
from typing import Optional, Tuple, Dict, Any
class ESGHierarchicalConfig(PretrainedConfig):
"""
Configuration class for ESG Hierarchical Multi-Task Learning model
"""
model_type = "esg_hierarchical"
def __init__(
self,
backbone_model_name: str = "Alibaba-NLP/gte-multilingual-base",
num_esg_classes: int = 4,
num_e_sub_classes: int = 3,
num_s_sub_classes: int = 7,
num_g_sub_classes: int = 5,
shared_hidden_dim_factor: float = 0.5,
head_hidden_dim_factor: float = 0.25,
dropout_rate: float = 0.1,
freeze_backbone: bool = True,
esg_categories: list = None,
sub_factors_e: list = None,
sub_factors_s: list = None,
sub_factors_g: list = None,
final_others_label: str = "Others",
max_length: int = 4096,
**kwargs
):
super().__init__(**kwargs)
# Model architecture parameters
self.backbone_model_name = backbone_model_name
self.num_esg_classes = num_esg_classes
self.num_e_sub_classes = num_e_sub_classes
self.num_s_sub_classes = num_s_sub_classes
self.num_g_sub_classes = num_g_sub_classes
self.shared_hidden_dim_factor = shared_hidden_dim_factor
self.head_hidden_dim_factor = head_hidden_dim_factor
self.dropout_rate = dropout_rate
self.freeze_backbone = freeze_backbone
self.max_length = max_length
# Label mappings
self.esg_categories = esg_categories or ["E", "S", "G", "Others_ESG"]
self.sub_factors_e = sub_factors_e or ["Emission", "Resource Use", "Product Innovation"]
self.sub_factors_s = sub_factors_s or ["Community", "Diversity", "Employment", "HS", "HR", "PR", "Training"]
self.sub_factors_g = sub_factors_g or ["BFunction", "BStructure", "Compensation", "Shareholder", "Vision"]
self.final_others_label = final_others_label
# Create label mappings
self.esg_label2id = {label: i for i, label in enumerate(self.esg_categories)}
self.esg_id2label = {i: label for i, label in enumerate(self.esg_categories)}
self.e_sub_label2id = {label: i for i, label in enumerate(self.sub_factors_e)}
self.e_sub_id2label = {i: label for i, label in enumerate(self.sub_factors_e)}
self.s_sub_label2id = {label: i for i, label in enumerate(self.sub_factors_s)}
self.s_sub_id2label = {i: label for i, label in enumerate(self.sub_factors_s)}
self.g_sub_label2id = {label: i for i, label in enumerate(self.sub_factors_g)}
self.g_sub_id2label = {i: label for i, label in enumerate(self.sub_factors_g)}
# Final combined mappings
all_sub_factors = self.sub_factors_e + self.sub_factors_s + self.sub_factors_g
all_final_labels = all_sub_factors + [self.final_others_label]
self.final_label2id = {label: i for i, label in enumerate(all_final_labels)}
self.final_id2label = {i: label for i, label in enumerate(all_final_labels)}
class ESGHierarchicalForSequenceClassification(PreTrainedModel):
"""
ESG Hierarchical Multi-Task Learning Model for Hugging Face Hub
This model performs hierarchical ESG classification:
1. First predicts main ESG category (E, S, G, Others_ESG)
2. Then predicts corresponding sub-factor based on the main category
"""
config_class = ESGHierarchicalConfig
def __init__(self, config: ESGHierarchicalConfig, backbone=None):
super().__init__(config)
self.config = config
# Use provided backbone or create empty one (weights will be loaded by from_pretrained)
if backbone is not None:
self.gte_model = backbone
else:
# During from_pretrained, just create the architecture - weights loaded automatically
from transformers import AutoConfig
backbone_config = AutoConfig.from_pretrained(config.backbone_model_name, trust_remote_code=True)
self.gte_model = AutoModel.from_config(backbone_config, trust_remote_code=True)
# Freeze backbone if specified
if config.freeze_backbone:
for param in self.gte_model.parameters():
param.requires_grad = False
# Get embedding dimension from backbone
embedding_dim = self.gte_model.config.hidden_size
shared_hidden_dim = int(embedding_dim * config.shared_hidden_dim_factor)
head_hidden_dim = int(embedding_dim * config.head_hidden_dim_factor)
# Shared projector layer
self.shared_projector = nn.Sequential(
nn.Linear(embedding_dim, shared_hidden_dim),
nn.LayerNorm(shared_hidden_dim),
nn.ReLU(),
nn.Dropout(config.dropout_rate)
)
# Classification heads
self.esg_head = nn.Sequential(
nn.Linear(shared_hidden_dim, head_hidden_dim),
nn.LayerNorm(head_hidden_dim),
nn.ReLU(),
nn.Dropout(config.dropout_rate),
nn.Linear(head_hidden_dim, config.num_esg_classes)
)
self.e_sub_head = nn.Sequential(
nn.Linear(shared_hidden_dim, head_hidden_dim),
nn.LayerNorm(head_hidden_dim),
nn.ReLU(),
nn.Dropout(config.dropout_rate),
nn.Linear(head_hidden_dim, config.num_e_sub_classes)
)
self.s_sub_head = nn.Sequential(
nn.Linear(shared_hidden_dim, head_hidden_dim),
nn.LayerNorm(head_hidden_dim),
nn.ReLU(),
nn.Dropout(config.dropout_rate),
nn.Linear(head_hidden_dim, config.num_s_sub_classes)
)
self.g_sub_head = nn.Sequential(
nn.Linear(shared_hidden_dim, head_hidden_dim),
nn.LayerNorm(head_hidden_dim),
nn.ReLU(),
nn.Dropout(config.dropout_rate),
nn.Linear(head_hidden_dim, config.num_g_sub_classes)
)
# Initialize weights
self._init_weights()
def _init_weights(self):
"""Initialize the weights using Xavier initialization"""
for module in [self.shared_projector, self.esg_head,
self.e_sub_head, self.s_sub_head, self.g_sub_head]:
for m in module:
if isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[Dict[str, torch.Tensor]] = None,
return_dict: Optional[bool] = None,
**kwargs
) -> SequenceClassifierOutput:
"""
Forward pass of the model
Args:
input_ids: Input token IDs
attention_mask: Attention mask for padding tokens
labels: Dictionary containing target labels for each task
return_dict: Whether to return dictionary format
Returns:
SequenceClassifierOutput containing logits and loss
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Get backbone outputs
backbone_outputs = self.gte_model(
input_ids=input_ids,
attention_mask=attention_mask
)
# Use CLS token embedding
cls_embedding = backbone_outputs.last_hidden_state[:, 0, :]
# Ensure dtype consistency - convert to float32
cls_embedding = cls_embedding.float()
# Apply clipping to prevent extreme values
cls_embedding = torch.clamp(cls_embedding, min=-100, max=100)
# Get shared features
shared_features = self.shared_projector(cls_embedding)
# Get logits from all heads
esg_logits = self.esg_head(shared_features)
e_sub_logits = self.e_sub_head(shared_features)
s_sub_logits = self.s_sub_head(shared_features)
g_sub_logits = self.g_sub_head(shared_features)
# Prepare output
logits = {
'esg': esg_logits,
'e_sub': e_sub_logits,
's_sub': s_sub_logits,
'g_sub': g_sub_logits
}
loss = None
if labels is not None:
# Calculate loss if labels provided
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
losses = []
loss_weights = {"esg": 1.0, "e_sub": 0.7, "s_sub": 0.7, "g_sub": 0.7}
for task_name, task_logits in logits.items():
if task_name in labels:
task_loss = loss_fct(task_logits, labels[task_name])
losses.append(loss_weights[task_name] * task_loss)
if losses:
loss = sum(losses)
if not return_dict:
output = (logits,)
if loss is not None:
output = (loss,) + output
return output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=backbone_outputs.hidden_states,
attentions=backbone_outputs.attentions,
)
def predict(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> Tuple[str, str]:
"""
Hierarchical prediction method compatible with original implementation
Returns:
Tuple of (main_esg_factor, sub_factor)
"""
self.eval()
with torch.no_grad():
outputs = self.forward(input_ids=input_ids, attention_mask=attention_mask)
logits = outputs.logits
# Get ESG factor prediction
prob_esg = torch.softmax(logits['esg'], dim=1)
pred_esg_idx = torch.argmax(prob_esg, dim=1).item()
pred_esg_label = self.config.esg_id2label[pred_esg_idx]
# Default sub-factor to "Others"
sub_factor_label = self.config.final_others_label
# Get sub-factor prediction based on ESG factor
if pred_esg_label == "E":
prob_e_sub = torch.softmax(logits['e_sub'][0], dim=0)
pred_e_sub_idx = torch.argmax(prob_e_sub).item()
sub_factor_label = self.config.e_sub_id2label[pred_e_sub_idx]
elif pred_esg_label == "S":
prob_s_sub = torch.softmax(logits['s_sub'][0], dim=0)
pred_s_sub_idx = torch.argmax(prob_s_sub).item()
sub_factor_label = self.config.s_sub_id2label[pred_s_sub_idx]
elif pred_esg_label == "G":
prob_g_sub = torch.softmax(logits['g_sub'][0], dim=0)
pred_g_sub_idx = torch.argmax(prob_g_sub).item()
sub_factor_label = self.config.g_sub_id2label[pred_g_sub_idx]
return pred_esg_label, sub_factor_label
# Register the model
ESGHierarchicalConfig.register_for_auto_class()
ESGHierarchicalForSequenceClassification.register_for_auto_class("AutoModelForSequenceClassification")
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