metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Quiero un programador para mantenimiento regular de mi e-commerce.
- text: Quiero contratar un ilustrador para un proyecto puntual.
- text: >-
Requiero un consultor en agronomía para optimizar el rendimiento de mis
cultivos.
- text: ¿Podrían darme ejemplos de perfiles con experiencia en marketing B2B?
- text: Busco a alguien que realice un análisis mensual de mi estrategia SEO.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: hiiamsid/sentence_similarity_spanish_es
model-index:
- name: SetFit with hiiamsid/sentence_similarity_spanish_es
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.6086956521739131
name: Accuracy
SetFit with hiiamsid/sentence_similarity_spanish_es
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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: hiiamsid/sentence_similarity_spanish_es
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
low |
|
medium |
|
high |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.6087 |
Uses
Direct Use for Inference
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("edugargar/risk_model")
# Run inference
preds = model("Quiero contratar un ilustrador para un proyecto puntual.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 7 | 11.0 | 17 |
Label | Training Sample Count |
---|---|
high | 27 |
low | 42 |
medium | 9 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0045 | 1 | 0.376 | - |
0.2273 | 50 | 0.1977 | - |
0.4545 | 100 | 0.0502 | - |
0.6818 | 150 | 0.0018 | - |
0.9091 | 200 | 0.0006 | - |
1.1364 | 250 | 0.0005 | - |
1.3636 | 300 | 0.0003 | - |
1.5909 | 350 | 0.0003 | - |
1.8182 | 400 | 0.0002 | - |
2.0455 | 450 | 0.0002 | - |
2.2727 | 500 | 0.0002 | - |
2.5 | 550 | 0.0002 | - |
2.7273 | 600 | 0.0002 | - |
2.9545 | 650 | 0.0002 | - |
3.1818 | 700 | 0.0002 | - |
3.4091 | 750 | 0.0002 | - |
3.6364 | 800 | 0.0002 | - |
3.8636 | 850 | 0.0001 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.19.1
Citation
BibTeX
@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}
}