Instructions to use cl-trier/gbert-base_sosec-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cl-trier/gbert-base_sosec-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cl-trier/gbert-base_sosec-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cl-trier/gbert-base_sosec-sentiment") model = AutoModelForSequenceClassification.from_pretrained("cl-trier/gbert-base_sosec-sentiment") - Notebooks
- Google Colab
- Kaggle
Department of Computational Linguistics - University of Trier
Upload BertForSequenceClassification
b8969a6 - Xet hash:
- a5b44f60883880896f06325a3aa830798c8d307e6b7ac3f67a143c3042897770
- Size of remote file:
- 440 MB
- SHA256:
- 4254a9771bfecc20bef0dc09d503810778933ca49d1316873ca347790f2c5717
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