# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("matthewleechen/labor-augmenting_stated_aim_classifier")
model = AutoModelForSequenceClassification.from_pretrained("matthewleechen/labor-augmenting_stated_aim_classifier", device_map="auto")Quick Links
Labor Augmenting Stated Aim Classifier
This is a roberta-base model that is trained to classify whether a set of explicit stated aims extracted from a British historical patent includes a labor-augmenting objective.
Labels were manually generated and then checked with Gemini 2.0 Flash with the attached prompt.
Hyperparameters: lr = 7e-5 batch size = 128
Test set results:
{'eval_loss': 0.2932845950126648,
'eval_accuracy': 0.9,
'eval_precision': 0.9,
'eval_recall': 0.9,
'eval_f1': 0.9,
'eval_runtime': 0.3819,
'eval_samples_per_second': 157.109,
'eval_steps_per_second': 2.618}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="matthewleechen/labor-augmenting_stated_aim_classifier")