learning_rate: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 0.15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
3.3950	0.15	3	3.2323	0.1496	0.9156	0.2572

Framework versions

Transformers 5.9.0
Pytorch 2.7.1+cu126
Datasets 4.8.5
Tokenizers 0.22.2

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

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Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'narcolepticchicken/legal-pii-redactor-v3-contractbackbone-smoke'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.