Instructions to use pixelsandpointers/bert-base-uncased-sentiment-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pixelsandpointers/bert-base-uncased-sentiment-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pixelsandpointers/bert-base-uncased-sentiment-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pixelsandpointers/bert-base-uncased-sentiment-classifier") model = AutoModelForSequenceClassification.from_pretrained("pixelsandpointers/bert-base-uncased-sentiment-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 102a50277e739281053a2f397a9fbb86b6c647b054ab3f99b2d11635ea173ca1
- Size of remote file:
- 438 MB
- SHA256:
- 38e04475852b2993574925a21baad6c6a35f64536e9905c59f311b778e67ab13
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