Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
text-embeddings-inference
Instructions to use POKWIR/Bert_sentiment_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use POKWIR/Bert_sentiment_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="POKWIR/Bert_sentiment_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("POKWIR/Bert_sentiment_classifier") model = AutoModelForSequenceClassification.from_pretrained("POKWIR/Bert_sentiment_classifier") - Notebooks
- Google Colab
- Kaggle
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## Try it out
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<div style="position:relative; width:100%; height:0; padding-bottom:75%;">
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<iframe
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src="https://huggingface.co/spaces/pokwir/bert-sentiment-demo"
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allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; clipboard-read; clipboard-write; display-capture; encrypted-media; fullscreen; geolocation; gyroscope; hid; identity-credentials-get; idle-detection; local-fonts; magnetometer; microphone; midi; payment; picture-in-picture; publickey-credentials-get; screen-wake-lock; serial; usb; web-share; xr-spatial-tracking"
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style="position:absolute; top:0; left:0; width:100%; height:100%; border:0; border-radius:12px;"
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## Try it out
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<iframe src = 'https://huggingface.co/spaces/POKWIR/bert-sentiment-demo' ></iframe>
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