| library_name: transformers | |
| tags: [] | |
| ## Fine-tuned roberta-base for detecting paragraphs with eHRAF-assigned two-digit id '610' | |
| ## Description | |
| This is a fine tuned roberta-base model for detecting whether paragraphs drawn from ethnographic source material classified under the main subject 'Marriage, Family, Kinship and Social Organization' is more specifically about '610'. | |
| ## Usage | |
| The easiest way to use this model at inference time is with the HF pipelines API. | |
| ```python | |
| from transformers import pipeline | |
| classifier = pipeline("text-classification", model="gptmurdock/classifier-610") | |
| classifier("Example text to classify") | |
| ``` | |
| ## Training data | |
| ... | |
| ## Training procedure | |
| ... | |
| We use a 60-20-20 train-val-test split, and fine-tuned roberta-base for 5 epochs (lr = 2e-5, batch size = 40). | |
| ## Evaluation | |
| Evals on the test set are reported below. | |
| | Metric | Value | | |
| |-----------|-------| | |
| | Precision | 91.2 | | |
| | Recall | 91.3 | | |
| | F1 | 91.2 | | |