Instructions to use abshetty/sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abshetty/sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="abshetty/sample")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("abshetty/sample") model = AutoModel.from_pretrained("abshetty/sample") - Notebooks
- Google Colab
- Kaggle
sample
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.46.2
- TensorFlow 2.17.1
- Tokenizers 0.20.3
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Model tree for abshetty/sample
Base model
google-bert/bert-base-cased