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classifier-260
gptmurdock/classifier-260
classifier-260 is a text classification model from gptmurdock. Use it when you need a label for a piece of text. It is set up for transformers.
This is a fine tuned roberta-base model for detecting whether paragraphs drawn from ethnographic source material classified under the main subject 'Economy, Food, and Resource Exploitation' is more specifically about…
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Sep 21, 2024
- Updated
- Sep 21, 2024
- SHA
- 07f7bc83
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Model card
From the Hugging Face model README
Fine-tuned roberta-base for detecting paragraphs with eHRAF-assigned two-digit id '260'
Description
This is a fine tuned roberta-base model for detecting whether paragraphs drawn from ethnographic source material classified under the main subject 'Economy, Food, and Resource Exploitation' is more specifically about '260'.
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-260")
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 | 92.2 |
| Recall | 92.3 |
| F1 | 92.3 |
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Sep 21, 2024
- Updated
- Sep 21, 2024