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sumtxt/paraphrase-MiniLM-L3-v2_immig
paraphrase-MiniLM-L3-v2_immig is a text classification model from sumtxt. Use it when you need a label for a piece of text. It is set up for setfit. The card lists the license as apache-2.0.
This SetFit model was trained on 48 title-abstracts samples (24 per class) to differeniate between published studies related to immigration/migration research and those that are not.
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From the Hugging Face model README
This SetFit model was trained on 48 title-abstracts samples (24 per class) to differeniate between published studies related to immigration/migration research and those that are not.
| Label | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|
| all | 0.9812 | 0.9934 | 0.9868 | 0.9901 |
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
model = SetFitModel.from_pretrained("mmarbach/paraphrase-MiniLM-L3-v2_immig")
preds = model("TITLE: ... ABSTRACT: ....")
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 97 | 155.6458 | 262 |
| Label | Training Sample Count |
|---|---|
| immigration_topic | 24 |
| other_topic | 24 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0133 | 1 | 0.288 | - |
| 0.6667 | 50 | 0.1935 | - |
| 1.0 | 75 | - | 0.0980 |
| 1.3333 | 100 | 0.0472 | - |
| 2.0 | 150 | 0.0118 | 0.0767 |
| 2.6667 | 200 | 0.0057 | - |
| 3.0 | 225 | - | 0.0719 |
| 3.3333 | 250 | 0.0047 | - |
| 4.0 | 300 | 0.0039 | 0.0718 |