Instructions to use dfafdsaf/bert_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dfafdsaf/bert_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dfafdsaf/bert_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dfafdsaf/bert_sentiment") model = AutoModelForSequenceClassification.from_pretrained("dfafdsaf/bert_sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac7d046b1aadcd8e4dfeaff454802f4538910c30a1d4d60b1b25d7c64789d2ff
- Size of remote file:
- 438 MB
- SHA256:
- af3432d2569dfbeeb708fc4acdf988c33949bf7148875650254db6797af33f97
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