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---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: What are the key situations that require the preparation of a mission order?
- text: How can audio data be used to improve speaker identification using neural
networks?
- text: How can organizations balance the need for data privacy with the benefits
of involving interns in data-related projects?
- text: What is the purpose of the message posted by the CR?
- text: What are the consequences of adopting a 'if not broken, don't fix' attitude
towards data monitoring?
inference: true
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.3076923076923077
name: Accuracy
---
# SetFit with sentence-transformers/all-MiniLM-L6-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co./datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co./blog/setfit)
### Model Labels
| Label | Examples |
|:--------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| very_semantic | <ul><li>'What are the key considerations when proposing names for a project or initiative?'</li><li>'What are the key aspects of team life and events in a company?'</li><li>'What is being asked for or sought in this conversation?'</li></ul> |
| lexical | <ul><li>'Who is responsible for reviewing and signing documents related to conference submissions?'</li><li>'How do data architecture and management systems enable digital transformation and address its associated challenges?'</li><li>'How do keys or access credentials get shared or transferred among team members in a workplace?'</li></ul> |
| very_lexical | <ul><li>'What are some of the key challenges associated with handling and storing large amounts of genomic data?'</li><li>"What is the focus of Eurobiomed's partnership with Digital113?"</li><li>'What are the key considerations for generating well-formatted JSON instances that conform to a given schema?'</li></ul> |
| semantic | <ul><li>'How can visualizations be used to enhance documentation and collaboration in software development?'</li><li>'What are the key considerations when choosing a distance metric for a vector database?'</li><li>'How can AI be leveraged to support HR departments in detecting and addressing gender bias?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.3077 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("yaniseuranova/setfit-rag-hybrid-search-query-router-test")
# Run inference
preds = model("What is the purpose of the message posted by the CR?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 7 | 14.1913 | 24 |
| Label | Training Sample Count |
|:--------------|:----------------------|
| lexical | 41 |
| semantic | 24 |
| very_lexical | 17 |
| very_semantic | 33 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (2, 2)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0004 | 1 | 0.4883 | - |
| 0.0209 | 50 | 0.3738 | - |
| 0.0417 | 100 | 0.2192 | - |
| 0.0626 | 150 | 0.1503 | - |
| 0.0834 | 200 | 0.1514 | - |
| 0.1043 | 250 | 0.1829 | - |
| 0.1251 | 300 | 0.4191 | - |
| 0.1460 | 350 | 0.2136 | - |
| 0.1668 | 400 | 0.1847 | - |
| 0.1877 | 450 | 0.1681 | - |
| 0.2085 | 500 | 0.222 | - |
| 0.2294 | 550 | 0.0397 | - |
| 0.2502 | 600 | 0.2626 | - |
| 0.2711 | 650 | 0.1343 | - |
| 0.2919 | 700 | 0.1769 | - |
| 0.3128 | 750 | 0.1704 | - |
| 0.3336 | 800 | 0.401 | - |
| 0.3545 | 850 | 0.1405 | - |
| 0.3753 | 900 | 0.1892 | - |
| 0.3962 | 950 | 0.1444 | - |
| 0.4170 | 1000 | 0.2337 | - |
| 0.4379 | 1050 | 0.1848 | - |
| 0.4587 | 1100 | 0.0601 | - |
| 0.4796 | 1150 | 0.2467 | - |
| 0.5004 | 1200 | 0.1829 | - |
| 0.5213 | 1250 | 0.1695 | - |
| 0.5421 | 1300 | 0.3892 | - |
| 0.5630 | 1350 | 0.1408 | - |
| 0.5838 | 1400 | 0.0506 | - |
| 0.6047 | 1450 | 0.1835 | - |
| 0.6255 | 1500 | 0.3284 | - |
| 0.6464 | 1550 | 0.1797 | - |
| 0.6672 | 1600 | 0.1118 | - |
| 0.6881 | 1650 | 0.1502 | - |
| 0.7089 | 1700 | 0.112 | - |
| 0.7298 | 1750 | 0.0401 | - |
| 0.7506 | 1800 | 0.117 | - |
| 0.7715 | 1850 | 0.1287 | - |
| 0.7923 | 1900 | 0.0623 | - |
| 0.8132 | 1950 | 0.2128 | - |
| 0.8340 | 2000 | 0.1542 | - |
| 0.8549 | 2050 | 0.1774 | - |
| 0.8757 | 2100 | 0.3252 | - |
| 0.8966 | 2150 | 0.0152 | - |
| 0.9174 | 2200 | 0.0539 | - |
| 0.9383 | 2250 | 0.0047 | - |
| 0.9591 | 2300 | 0.1232 | - |
| 0.9800 | 2350 | 0.3466 | - |
| **1.0** | **2398** | **-** | **0.3644** |
| 1.0008 | 2400 | 0.0296 | - |
| 1.0217 | 2450 | 0.3459 | - |
| 1.0425 | 2500 | 0.0867 | - |
| 1.0634 | 2550 | 0.1343 | - |
| 1.0842 | 2600 | 0.2074 | - |
| 1.1051 | 2650 | 0.0052 | - |
| 1.1259 | 2700 | 0.0548 | - |
| 1.1468 | 2750 | 0.0441 | - |
| 1.1676 | 2800 | 0.0821 | - |
| 1.1885 | 2850 | 0.0546 | - |
| 1.2093 | 2900 | 0.1286 | - |
| 1.2302 | 2950 | 0.1222 | - |
| 1.2510 | 3000 | 0.0227 | - |
| 1.2719 | 3050 | 0.3011 | - |
| 1.2927 | 3100 | 0.018 | - |
| 1.3136 | 3150 | 0.0581 | - |
| 1.3344 | 3200 | 0.0485 | - |
| 1.3553 | 3250 | 0.2369 | - |
| 1.3761 | 3300 | 0.1681 | - |
| 1.3970 | 3350 | 0.1289 | - |
| 1.4178 | 3400 | 0.1664 | - |
| 1.4387 | 3450 | 0.1467 | - |
| 1.4595 | 3500 | 0.1399 | - |
| 1.4804 | 3550 | 0.3045 | - |
| 1.5013 | 3600 | 0.2155 | - |
| 1.5221 | 3650 | 0.061 | - |
| 1.5430 | 3700 | 0.0787 | - |
| 1.5638 | 3750 | 0.3649 | - |
| 1.5847 | 3800 | 0.1202 | - |
| 1.6055 | 3850 | 0.1004 | - |
| 1.6264 | 3900 | 0.154 | - |
| 1.6472 | 3950 | 0.0944 | - |
| 1.6681 | 4000 | 0.0004 | - |
| 1.6889 | 4050 | 0.1843 | - |
| 1.7098 | 4100 | 0.2233 | - |
| 1.7306 | 4150 | 0.2203 | - |
| 1.7515 | 4200 | 0.0986 | - |
| 1.7723 | 4250 | 0.2295 | - |
| 1.7932 | 4300 | 0.1763 | - |
| 1.8140 | 4350 | 0.3487 | - |
| 1.8349 | 4400 | 0.3285 | - |
| 1.8557 | 4450 | 0.0152 | - |
| 1.8766 | 4500 | 0.1108 | - |
| 1.8974 | 4550 | 0.2416 | - |
| 1.9183 | 4600 | 0.0476 | - |
| 1.9391 | 4650 | 0.2929 | - |
| 1.9600 | 4700 | 0.1006 | - |
| 1.9808 | 4750 | 0.0925 | - |
| 2.0 | 4796 | - | 0.3669 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```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}
}
```
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