# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("matthewleechen/capital-saving_stated_aim_classifier")
model = AutoModelForSequenceClassification.from_pretrained("matthewleechen/capital-saving_stated_aim_classifier", device_map="auto")Quick Links
Capital Saving Stated Aim Classifier
This is a roberta-base model that is trained to classify whether an explicit set of stated aims extracted from a British historical patent includes a capital-saving objective.
Labels were generated manually and checked with Gemini 2.0 Flash using the attached prompt.
Hyperparameters: lr = 3e-5 batch size = 128
Test set results:
{'eval_loss': 0.32574835419654846,
'eval_accuracy': 0.89,
'eval_precision': 0.8916686674669867,
'eval_recall': 0.89,
'eval_f1': 0.89003300330033,
'eval_runtime': 0.4104,
'eval_samples_per_second': 243.688,
'eval_steps_per_second': 2.437}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="matthewleechen/capital-saving_stated_aim_classifier")