Text Classification
Transformers
Safetensors
English
bert
sentiment analysis
text classification
news
reviews
text-embeddings-inference
Instructions to use mervp/SentimentBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mervp/SentimentBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mervp/SentimentBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mervp/SentimentBERT") model = AutoModelForSequenceClassification.from_pretrained("mervp/SentimentBERT") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -68,3 +68,10 @@ print(predict_sentiment("Everything went wrong.")) # negative
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print(predict_sentiment("He opened the door and walked in.")) # neutral
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print(predict_sentiment("They are meeting at 5 PM.")) # neutral
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print(predict_sentiment("She has a cat.")) # neutral
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print(predict_sentiment("He opened the door and walked in.")) # neutral
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print(predict_sentiment("They are meeting at 5 PM.")) # neutral
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print(predict_sentiment("She has a cat.")) # neutral
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Thanks for visiting and downloading this model!
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If this model helped you, please consider leaving a 👍 like. Your support helps this model reach more developers and encourages further improvements if any.
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