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vikram71198
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distilroberta-base-finetuned-fake-news-detection

Text Classification
Transformers
PyTorch
roberta
Fake News Detection
Text Classification
Inference Endpoints
Model card Files Community
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@@ -55,7 +55,7 @@ I also trained for 3 full epochs on Colab's Tesla P100-PCIE-16GB GPU.
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch.nn as nn
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  tokenizer = AutoTokenizer.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection")
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- model = AutoModelForSequenceClassification.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection"
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  #Following the same truncation & padding strategy used while training
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  encoded_input = tokenizer("Enter any news article to be classified. Can be a list of articles too.", truncation = True, padding = "max_length", max_length = 512, return_tensors='pt')
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  output = model(**encoded_input)["logits"]
 
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch.nn as nn
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  tokenizer = AutoTokenizer.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection")
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+ model = AutoModelForSequenceClassification.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection")
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  #Following the same truncation & padding strategy used while training
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  encoded_input = tokenizer("Enter any news article to be classified. Can be a list of articles too.", truncation = True, padding = "max_length", max_length = 512, return_tensors='pt')
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  output = model(**encoded_input)["logits"]