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")# 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") - Notebooks
- Google Colab
- Kaggle
saif
Update model by training for 25 epochs and two more datasets i.e. mit restaurant and mit movie trivia.
ba83523 - Xet hash:
- f1763f5eac4e74d3ea007f2809be854bfde0a2452a95dd8c2f64dd0e3220829f
- Size of remote file:
- 438 MB
- SHA256:
- c2a2401a91d2bf80826341c52a0c1f8b6814f36c1b7852d4c93482a13041260f
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