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z-dickson
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multilingual_sentiment_newspaper_headlines

Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
Inference Endpoints
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@@ -30,10 +30,10 @@ It achieves the following results on the evaluation set:
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  ```python
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  import torch
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- from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline,TFAutoModelForSequenceClassification, AutoTokenizer
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  tokenizer = AutoTokenizer.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines")
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- m1 = TFAutoModelForSequenceClassification.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines")
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  sentiment_classifier = TextClassificationPipeline(tokenizer=tokenizer, model=m1)
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  sentiment_classifier('Brazylia: Bolsonaro wci±ż nie uznał porażki. Jego zwolennicy blokuj± autostrady')
 
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  ```python
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  import torch
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+ from transformers import AutoTokenizer, TextClassificationPipeline,TFAutoModelForSequenceClassification
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  tokenizer = AutoTokenizer.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines")
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+ m1 = TFAutoModelForSequenceClassification.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines", from_tf=True)
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  sentiment_classifier = TextClassificationPipeline(tokenizer=tokenizer, model=m1)
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  sentiment_classifier('Brazylia: Bolsonaro wci±ż nie uznał porażki. Jego zwolennicy blokuj± autostrady')