Instructions to use TingChenChang/sentiment-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TingChenChang/sentiment-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TingChenChang/sentiment-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TingChenChang/sentiment-classification") model = AutoModelForSequenceClassification.from_pretrained("TingChenChang/sentiment-classification") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c8e542e5586785610a5d70a9cc34dc03ef95f34623cb09b56cd9157c457107c
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size 409100240
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