Instructions to use sayef/fsner-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sayef/fsner-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sayef/fsner-bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sayef/fsner-bert-base-uncased") model = AutoModel.from_pretrained("sayef/fsner-bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update README.md
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Update model by training for 25 epochs and two more datasets i.e. mit restaurant and mit movie trivia.
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add tokenizer
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Upload fsner base uncased model, pretrained using bert.
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initial commit
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