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Update README.md
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README.md
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Here is how we can use the model to get the features of a given text in PyTorch:
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```python
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!pip install transformers
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from transformers import AutoTokenizer
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from transformers import AutoModelForSequenceClassification
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# predicting with the model
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# initializing our labels
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label_list = [
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"tourism"
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]
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batch = tokenizer(
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with torch.no_grad():
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outputs = model(**batch)
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## Training data
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This model is trained on 50,945 rows of Nepali language news grouped [dataset](https://www.kaggle.com/competitions/text-it-meet-22/data?select=train.csv) found on Kaggle which was also used in IT Meet 2022 Text challenge.
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##
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## Framework versions
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- Transformers 4.20.1
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Here is how we can use the model to get the features of a given text in PyTorch:
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```python
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!pip install transformers torch
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from transformers import AutoTokenizer
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from transformers import AutoModelForSequenceClassification
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# predicting with the model
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sequence_i_want_to_predict = "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन"
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# initializing our labels
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label_list = [
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"tourism"
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]
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batch = tokenizer(sequence_i_want_to_predict, padding=True, truncation=True, max_length=512, return_tensors='pt')
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with torch.no_grad():
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outputs = model(**batch)
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## Training data
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This model is trained on 50,945 rows of Nepali language news grouped [dataset](https://www.kaggle.com/competitions/text-it-meet-22/data?select=train.csv) found on Kaggle which was also used in IT Meet 2022 Text challenge.
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## Framework versions
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- Transformers 4.20.1
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