|
--- |
|
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 components involved in developing a deep learning model for |
|
handwritten digit recognition? |
|
- text: What is the purpose of the message posted by the CR? |
|
- text: How can researchers create and maintain public repositories for reproducible |
|
research? |
|
- text: What are the key components involved in developing a deep learning model for |
|
handwritten digit recognition? |
|
- text: How do you prioritize and delegate tasks to ensure efficient collaboration |
|
and feedback? |
|
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: 0.5 |
|
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 --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### 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 | |
|
|:--------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| lexical | <ul><li>'What are the key considerations when choosing an optimization method for a complex problem?'</li><li>'What are the challenges of being a remote mentor or sponsor?'</li><li>'How do researchers typically obtain information on the ranking of machine learning conferences?'</li></ul> | |
|
| semantic | <ul><li>'What are common issues that users may encounter when accessing a platform that uses JumpCloud for authentication?'</li><li>'What are the key components involved in developing a deep learning model for handwritten digit recognition?'</li><li>'How can machine learning and data enrichment be used to improve business outcomes in various industries?'</li></ul> | |
|
| very_semantic | <ul><li>"What are people's opinions on a particular topic?"</li><li>'What are the key considerations when proposing names for a project or initiative?'</li><li>'What are the key considerations for successful collaboration between industry and academia in research and development projects?'</li></ul> | |
|
| very_lexical | <ul><li>'How can one track and store keys in a Flink operator?'</li><li>'What role do companies like Solvay play in addressing key societal challenges through their business strategies and operations?'</li><li>'What is the purpose of the scoring methodology in determining RAI maturity?'</li></ul> | |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
| Label | Accuracy | |
|
|:--------|:---------| |
|
| **all** | 0.5 | |
|
|
|
## 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?") |
|
``` |
|
|
|
<!-- |
|
### Downstream Use |
|
|
|
*List how someone could finetune this model on their own dataset.* |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Set Metrics |
|
| Training set | Min | Median | Max | |
|
|:-------------|:----|:--------|:----| |
|
| Word count | 8 | 14.4138 | 24 | |
|
|
|
| Label | Training Sample Count | |
|
|:--------------|:----------------------| |
|
| lexical | 32 | |
|
| semantic | 21 | |
|
| very_lexical | 10 | |
|
| very_semantic | 24 | |
|
|
|
### Training Hyperparameters |
|
- batch_size: (8, 8) |
|
- num_epochs: (3, 3) |
|
- 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 | |
|
|:-------:|:--------:|:-------------:|:---------------:| |
|
| 0.0015 | 1 | 0.268 | - | |
|
| 0.0736 | 50 | 0.2649 | - | |
|
| 0.1473 | 100 | 0.3352 | - | |
|
| 0.2209 | 150 | 0.2516 | - | |
|
| 0.2946 | 200 | 0.2438 | - | |
|
| 0.3682 | 250 | 0.1808 | - | |
|
| 0.4418 | 300 | 0.2365 | - | |
|
| 0.5155 | 350 | 0.1337 | - | |
|
| 0.5891 | 400 | 0.2263 | - | |
|
| 0.6627 | 450 | 0.1936 | - | |
|
| 0.7364 | 500 | 0.0612 | - | |
|
| 0.8100 | 550 | 0.1664 | - | |
|
| 0.8837 | 600 | 0.0987 | - | |
|
| 0.9573 | 650 | 0.0736 | - | |
|
| 1.0 | 679 | - | 0.2288 | |
|
| 1.0309 | 700 | 0.0568 | - | |
|
| 1.1046 | 750 | 0.0765 | - | |
|
| 1.1782 | 800 | 0.1193 | - | |
|
| 1.2518 | 850 | 0.199 | - | |
|
| 1.3255 | 900 | 0.2734 | - | |
|
| 1.3991 | 950 | 0.194 | - | |
|
| 1.4728 | 1000 | 0.1085 | - | |
|
| 1.5464 | 1050 | 0.1496 | - | |
|
| 1.6200 | 1100 | 0.1673 | - | |
|
| 1.6937 | 1150 | 0.2225 | - | |
|
| 1.7673 | 1200 | 0.0503 | - | |
|
| 1.8409 | 1250 | 0.1531 | - | |
|
| 1.9146 | 1300 | 0.2287 | - | |
|
| 1.9882 | 1350 | 0.1187 | - | |
|
| **2.0** | **1358** | **-** | **0.2055** | |
|
| 2.0619 | 1400 | 0.0546 | - | |
|
| 2.1355 | 1450 | 0.2072 | - | |
|
| 2.2091 | 1500 | 0.1208 | - | |
|
| 2.2828 | 1550 | 0.0837 | - | |
|
| 2.3564 | 1600 | 0.0405 | - | |
|
| 2.4300 | 1650 | 0.1334 | - | |
|
| 2.5037 | 1700 | 0.1458 | - | |
|
| 2.5773 | 1750 | 0.2189 | - | |
|
| 2.6510 | 1800 | 0.0561 | - | |
|
| 2.7246 | 1850 | 0.1656 | - | |
|
| 2.7982 | 1900 | 0.1351 | - | |
|
| 2.8719 | 1950 | 0.1826 | - | |
|
| 2.9455 | 2000 | 0.1905 | - | |
|
| 3.0 | 2037 | - | 0.2273 | |
|
|
|
* 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
