Instructions to use joelniklaus/bert-base-uncased-sem_eval_2010_task_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joelniklaus/bert-base-uncased-sem_eval_2010_task_8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joelniklaus/bert-base-uncased-sem_eval_2010_task_8")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joelniklaus/bert-base-uncased-sem_eval_2010_task_8") model = AutoModelForSequenceClassification.from_pretrained("joelniklaus/bert-base-uncased-sem_eval_2010_task_8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 081522971a80e72aa19c09e965ec8ed8327ed9f5b78a475a7e03d9ed6b2596a9
- Size of remote file:
- 438 MB
- SHA256:
- ea8ef87486bc6b98bad9f34dbdb2d7033a50dd365f7c989ff4f6efe700300c16
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