Instructions to use gptmurdock/classifier-640 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gptmurdock/classifier-640 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gptmurdock/classifier-640")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gptmurdock/classifier-640") model = AutoModelForSequenceClassification.from_pretrained("gptmurdock/classifier-640", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fine-tuned roberta-base for detecting paragraphs with eHRAF-assigned two-digit id '640'
Description
This is a fine tuned roberta-base model for detecting whether paragraphs drawn from ethnographic source material classified under the main subject 'Political Organization and Behavior' is more specifically about '640'.
Usage
The easiest way to use this model at inference time is with the HF pipelines API.
from transformers import pipeline
classifier = pipeline("text-classification", model="gptmurdock/classifier-640")
classifier("Example text to classify")
Training data
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Training procedure
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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 | 84.4 |
| Recall | 84.4 |
| F1 | 84.4 |
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