Instructions to use robingeibel/led-base-16384-finetuned-big_patent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robingeibel/led-base-16384-finetuned-big_patent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="robingeibel/led-base-16384-finetuned-big_patent")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("robingeibel/led-base-16384-finetuned-big_patent") model = AutoModel.from_pretrained("robingeibel/led-base-16384-finetuned-big_patent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
led-base-16384-finetuned-big_patent
This model was trained from scratch 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 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
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