Upload model
Browse files- config.json +8 -0
- configuration_ESGBertReddit.py +13 -0
- modeling_ESGBertReddit.py +34 -0
- pytorch_model.bin +3 -0
config.json
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{
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"id2label": {
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"0": "D",
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"1": "C",
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},
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"model_type": "ESGBertReddit",
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"num_classes": 4,
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"transformers_version": "4.24.0"
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}
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{
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"architectures": [
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"BertModelForESGClassification"
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],
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"auto_map": {
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"AutoConfig": "configuration_ESGBertReddit.ESGRedditConfig",
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"AutoModelForSequenceClassification": "modeling_ESGBertReddit.BertModelForESGClassification"
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},
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"id2label": {
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"0": "D",
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"1": "C",
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},
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"model_type": "ESGBertReddit",
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"num_classes": 4,
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"torch_dtype": "float32",
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"transformers_version": "4.24.0"
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}
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configuration_ESGBertReddit.py
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from transformers import PretrainedConfig
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class ESGRedditConfig(PretrainedConfig):
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model_type = "ESGBertReddit"
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def __init__(
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self,
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architectures = ["BertForSequenceClassification"],
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num_classes: int = 4,
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**kwargs
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):
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self.architectures = architectures
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self.num_classes = num_classes
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super().__init__(**kwargs)
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modeling_ESGBertReddit.py
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import torch
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import torch.nn as nn
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from transformers import PreTrainedModel,BertModel
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from ESGBertReddit_model.configuration_ESGBertReddit import ESGRedditConfig
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class ClassificationModel(PreTrainedModel):
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config_class = ESGRedditConfig
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def __init__(self,config):
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super().__init__(config)
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self.bert = BertModel.from_pretrained('yiyanghkust/finbert-esg',output_attentions=True)
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self.W = nn.Linear(self.bert.config.hidden_size, config.num_classes)
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self.num_classes = config.num_classes
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def forward(self, input_ids, attention_mask, token_type_ids):
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h, _, attn = self.bert(input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids).values()
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h_cls = h[:,0,:]
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output = self.W(h_cls)
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return output, attn
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class BertModelForESGClassification(PreTrainedModel):
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config_class = ESGRedditConfig
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def __init__(self, config):
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super().__init__(config)
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self.model = ClassificationModel(config)
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def forward(self, inputs, labels=None):
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logits,_ = self.model(**inputs)
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if labels is not None:
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loss = torch.nn.cross_entropy(logits, labels)
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return {"loss": loss, "logits": logits}
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return {"logits": logits}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3cfd0f21b11a64ad7bcaa71932f94a52b5fb2feb5efeb4b50f35f6dda8d9a81e
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size 439088877
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