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kemonito233/intentino
intentino is a text classification model from kemonito233. Use it when you need a label for a piece of text. It is set up for setfit.
This is a SetFit model that can be used for Text Classification. This SetFit model uses hiiamsid/sentencesimilarityspanishes as the Sentence Transformer embedding model. A LogisticRegression instance is used for class…
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From the Hugging Face model README
This is a SetFit model that can be used for Text Classification. This SetFit model uses hiiamsid/sentence_similarity_spanish_es as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
| Label | Examples |
|---|---|
| 14 | <ul><li>'tengo otro prestamo activo'</li><li>'mi historial esta mal'</li><li>'tengo credito con otra financiera'</li></ul> |
| 11 | <ul><li>'hable mas fuerte'</li><li>'se oye muy lejos'</li><li>'se corta'</li></ul> |
| 15 | <ul><li>'ahorita voy manejando, hablame luego'</li><li>'ahorita no puedo atenderte, estoy ocupado'</li><li>'voy manejando'</li></ul> |
| 7 | <ul><li>'ya fallecio'</li><li>'ya no esta con nosotros'</li><li>'el ya no vive'</li></ul> |
| 4 | <ul><li>'adios, buenas noches'</li><li>'bueno, gracias, adios'</li><li>'listo, hasta luego'</li></ul> |
| 10 | <ul><li>'si, quiero saber'</li><li>'si, digame rapido'</li><li>'te escucho'</li></ul> |
| 12 | <ul><li>'no, joven, muchas gracias'</li><li>'no, oiga, gracias'</li><li>'no, por ahora paso, gracias'</li></ul> |
| 17 | <ul><li>'bueno, diga'</li><li>'si'</li><li>'si, diga'</li></ul> |
| 3 | <ul><li>'si, a ver de que se trata'</li><li>'tal vez si'</li><li>'esta bien, envialo'</li></ul> |
| 5 | <ul><li>'no corresponde ese numero'</li><li>'esta llamando al numero equivocado'</li><li>'aqui no vive esa persona'</li></ul> |
| 8 | <ul><li>'¿me da la direccion de sus oficinas?'</li><li>'yo no les di mi telefono'</li><li>'yo no le di mis datos a nadie'</li></ul> |
| 0 | <ul><li>'soy su hermana'</li><li>'esta bajo tratamiento'</li><li>'se siente mal'</li></ul> |
| 16 | <ul><li>'¿quien me llama?'</li><li>'¿de que empresa llaman?'</li><li>'¿quien es?'</li></ul> |
| 1 | <ul><li>'habla el senor'</li><li>'con ella habla'</li><li>'si aqui habla'</li></ul> |
| 6 | <ul><li>'un momento por favor'</li><li>'deja le hablo'</li><li>'permiteme un segundo, no me cuelgues'</li></ul> |
| 2 | <ul><li>'¿con quien quiere hablar?'</li><li>'¿quien busca?'</li><li>'¿a quien esta buscando?'</li></ul> |
| 9 | <ul><li>'no esten chingando'</li><li>'es la quinta vez que me marcan hoy'</li><li>'¡que no entiendes que no!'</li></ul> |
| 13 | <ul><li>'salio a la tienda, no tarda'</li><li>'ahorita no esta, anda de viaje'</li><li>'anda trabajando'</li></ul> |
| Label | Accuracy |
|---|---|
| all | 0.9111 |
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("soy quien busca")
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| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 1 | 3.9018 | 11 |
| Label | Training Sample Count |
|---|---|
| 0 | 32 |
| 1 | 18 |
| 2 | 11 |
| 3 | 18 |
| 4 | 18 |
| 5 | 22 |
| 6 | 9 |
| 7 | 12 |
| 8 | 40 |
| 9 | 11 |
| 10 | 33 |
| 11 | 13 |
| 12 | 48 |
| 13 | 8 |
| 14 | 36 |
| 15 | 13 |
| 16 | 18 |
| 17 | 37 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0010 | 1 | 0.3888 | - |
| 0.0504 | 50 | 0.211 | - |
| 0.1007 | 100 | 0.1344 | - |
| 0.1511 | 150 | 0.0742 | - |
| 0.2014 | 200 | 0.0484 | - |
| 0.2518 | 250 | 0.0387 | - |
| 0.3021 | 300 | 0.0264 | - |
| 0.3525 | 350 | 0.0183 | - |
| 0.4028 | 400 | 0.0135 | - |
| 0.4532 | 450 | 0.0115 | - |
| 0.5035 | 500 | 0.0082 | - |
| 0.5539 | 550 | 0.0083 | - |
| 0.6042 | 600 | 0.0073 | - |
| 0.6546 | 650 | 0.009 | - |
| 0.7049 | 700 | 0.0067 | - |
| 0.7553 | 750 | 0.0075 | - |
| 0.8056 | 800 | 0.0085 | - |
| 0.8560 | 850 | 0.0073 | - |
| 0.9063 | 900 | 0.0065 | - |
| 0.9567 | 950 | 0.0076 | - |
| 1.0 | 993 | - | 0.0437 |
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
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