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README.md
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This is the first classification of sentiment analysis for (redacted) task
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### How to
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```python
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import torch
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from transformers import BertForSequenceClassification, BertTokenizer, BertConfig
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tokenizer = BertTokenizer.from_pretrained("nfhakim/sentiment-analysis-c1")
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config = BertConfig.from_pretrained("nfhakim/sentiment-analysis-c1")
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model = BertForSequenceClassification.from_pretrained("nfhakim/sentiment-analysis-c1", config=config)
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i2w = {0: 'positive', 1: 'non-positive'}
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logits = model(subwords)[0]
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label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()
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```
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This is the first classification of sentiment analysis for (redacted) task
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### How to import
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```python
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import torch
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from transformers import BertForSequenceClassification, BertTokenizer, BertConfig
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tokenizer = BertTokenizer.from_pretrained("nfhakim/sentiment-analysis-c1")
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config = BertConfig.from_pretrained("nfhakim/sentiment-analysis-c1")
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model = BertForSequenceClassification.from_pretrained("nfhakim/sentiment-analysis-c1", config=config)
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```
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### How to use
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```python
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from transformers import pipeline
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nlp = pipeline("text-classification", model="nfhakim/sentiment-analysis-c1")
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results = nlp("Your input text here")
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```
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