arkiven4/phobert-sentiment-v2

Model Card for Model ID

Training Accuracy Epoch: 88.21% Validation Accuracy Epoch: 93.26%

Usage

import torch
from transformers import AutoModel, AutoTokenizer, RobertaModel, RobertaPreTrainedModel

class RobertaClassClassifier(torch.nn.Module):
    def __init__(self):
        super(RobertaClassClassifier, self).__init__()
        self.pre_classifier = torch.nn.Linear(768, 768)
        self.dropout = torch.nn.Dropout(0.3)
        self.classifier = torch.nn.Linear(768, 2)

    def forward(self, hidden_state):
        pooler = hidden_state[:, 0]
        pooler = self.pre_classifier(pooler)
        pooler = torch.nn.ReLU()(pooler)
        pooler = self.dropout(pooler)
        output = self.classifier(pooler)
        return output

class MainModel(RobertaPreTrainedModel):
    def __init__(self, config):

        super().__init__(config)
        self.config = config
        self.roberta = RobertaModel(config)
        self.classifier = RobertaClassClassifier()
        self.init_weights()

    def forward(self, input_ids, attention_mask, token_type_ids):
        hidden_states = self.roberta(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
        logits = self.classifier(hidden_states[0])

        return logits

model = MainModel.from_pretrained("arkiven4/phobert-sentiment-v2")
tokenizer = AutoTokenizer.from_pretrained("arkiven4/phobert-sentiment-v2")

text = str('một mớ lỗi : không xem_lại được bài test , không hiện link ( ví_dụ như là youtube khi chia_sẻ ) không copy được từ không biết để dịch . làm_ơn cho về bản web đi')
text = " ".join(text.split())

inputs = tokenizer.encode_plus(
    text,
    None,
    add_special_tokens=True,
    max_length=256,
    pad_to_max_length=True,
    return_token_type_ids=True,
    return_tensors="pt"
)

with torch.no_grad():
  predicts = model(inputs['input_ids'], inputs['attention_mask'], inputs['token_type_ids'])

torch.argmax(predicts, dim=1)
# tensor([1])

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