Instructions to use buschbd7/legal-bert-base-uncased-from-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buschbd7/legal-bert-base-uncased-from-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="buschbd7/legal-bert-base-uncased-from-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("buschbd7/legal-bert-base-uncased-from-scratch") model = AutoModelForMaskedLM.from_pretrained("buschbd7/legal-bert-base-uncased-from-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
legal-bert-base-uncased-from-scratch
This model is a fine-tuned version of buschbd7/legal-bert-base-uncased-from-scratch on the None dataset.
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:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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