Add SetFit model

2765549 verified 4 months ago

13.8 kB

	---
	base_model: BAAI/bge-m3
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: How does technology impact our daily lives and what benefits can it bring
	to various activities?
	- text: How do organizations effectively deploy and manage machine learning algorithms
	to drive business value?
	- text: What are the key considerations for organizing and managing computer lab resources
	and tracking their status?
	- text: How can batch processing improve the efficiency of data lake operations?
	- text: What is the purpose of setting up a CUPS on a server?
	inference: true
	model-index:
	- name: SetFit with BAAI/bge-m3
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.8947368421052632
	name: Accuracy
	---

	# SetFit with BAAI/bge-m3

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-m3](https://huggingface.co./BAAI/bge-m3) 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: [BAAI/bge-m3](https://huggingface.co./BAAI/bge-m3)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 8192 tokens
	- Number of Classes: 2 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>"How does Happeo's search AI work to provide answers to user queries?"</li><li>'What are the primary areas of focus in the domain of Data Science and Analysis?'</li><li>'How can one organize a running event in Belgium?'</li></ul> \|
	\| semantic \| <ul><li>'What changes can be made to a channel header?'</li><li>'How can hardware capabilities impact the accuracy of motion and object detections?'</li><li>'Who is responsible for managing guarantees and prolongations?'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8947 \|

	## 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")
	# Run inference
	preds = model("What is the purpose of setting up a CUPS on a server?")
	```

	<!--
	### 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 \| 4 \| 13.7407 \| 28 \|

	\| Label \| Training Sample Count \|
	\|:---------\|:----------------------\|
	\| lexical \| 44 \|
	\| semantic \| 118 \|

	### 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 \|
	\|:-------:\|:--------:\|:-------------:\|:---------------:\|
	\| 0.0005 \| 1 \| 0.257 \| - \|
	\| 0.0250 \| 50 \| 0.1944 \| - \|
	\| 0.0499 \| 100 \| 0.2383 \| - \|
	\| 0.0749 \| 150 \| 0.1279 \| - \|
	\| 0.0999 \| 200 \| 0.0033 \| - \|
	\| 0.1248 \| 250 \| 0.0021 \| - \|
	\| 0.1498 \| 300 \| 0.0012 \| - \|
	\| 0.1747 \| 350 \| 0.0008 \| - \|
	\| 0.1997 \| 400 \| 0.0004 \| - \|
	\| 0.2247 \| 450 \| 0.0006 \| - \|
	\| 0.2496 \| 500 \| 0.0005 \| - \|
	\| 0.2746 \| 550 \| 0.0003 \| - \|
	\| 0.2996 \| 600 \| 0.0003 \| - \|
	\| 0.3245 \| 650 \| 0.0003 \| - \|
	\| 0.3495 \| 700 \| 0.0004 \| - \|
	\| 0.3744 \| 750 \| 0.0005 \| - \|
	\| 0.3994 \| 800 \| 0.0003 \| - \|
	\| 0.4244 \| 850 \| 0.0002 \| - \|
	\| 0.4493 \| 900 \| 0.0002 \| - \|
	\| 0.4743 \| 950 \| 0.0002 \| - \|
	\| 0.4993 \| 1000 \| 0.0001 \| - \|
	\| 0.5242 \| 1050 \| 0.0001 \| - \|
	\| 0.5492 \| 1100 \| 0.0001 \| - \|
	\| 0.5741 \| 1150 \| 0.0002 \| - \|
	\| 0.5991 \| 1200 \| 0.0001 \| - \|
	\| 0.6241 \| 1250 \| 0.0003 \| - \|
	\| 0.6490 \| 1300 \| 0.0002 \| - \|
	\| 0.6740 \| 1350 \| 0.0001 \| - \|
	\| 0.6990 \| 1400 \| 0.0003 \| - \|
	\| 0.7239 \| 1450 \| 0.0001 \| - \|
	\| 0.7489 \| 1500 \| 0.0002 \| - \|
	\| 0.7738 \| 1550 \| 0.0001 \| - \|
	\| 0.7988 \| 1600 \| 0.0002 \| - \|
	\| 0.8238 \| 1650 \| 0.0002 \| - \|
	\| 0.8487 \| 1700 \| 0.0002 \| - \|
	\| 0.8737 \| 1750 \| 0.0002 \| - \|
	\| 0.8987 \| 1800 \| 0.0003 \| - \|
	\| 0.9236 \| 1850 \| 0.0001 \| - \|
	\| 0.9486 \| 1900 \| 0.0001 \| - \|
	\| 0.9735 \| 1950 \| 0.0001 \| - \|
	\| 0.9985 \| 2000 \| 0.0001 \| - \|
	\| 1.0 \| 2003 \| - \| 0.1735 \|
	\| 1.0235 \| 2050 \| 0.0001 \| - \|
	\| 1.0484 \| 2100 \| 0.0001 \| - \|
	\| 1.0734 \| 2150 \| 0.0001 \| - \|
	\| 1.0984 \| 2200 \| 0.0 \| - \|
	\| 1.1233 \| 2250 \| 0.0001 \| - \|
	\| 1.1483 \| 2300 \| 0.0001 \| - \|
	\| 1.1732 \| 2350 \| 0.0001 \| - \|
	\| 1.1982 \| 2400 \| 0.0002 \| - \|
	\| 1.2232 \| 2450 \| 0.0001 \| - \|
	\| 1.2481 \| 2500 \| 0.0 \| - \|
	\| 1.2731 \| 2550 \| 0.0001 \| - \|
	\| 1.2981 \| 2600 \| 0.0001 \| - \|
	\| 1.3230 \| 2650 \| 0.0 \| - \|
	\| 1.3480 \| 2700 \| 0.0001 \| - \|
	\| 1.3729 \| 2750 \| 0.0001 \| - \|
	\| 1.3979 \| 2800 \| 0.0001 \| - \|
	\| 1.4229 \| 2850 \| 0.0 \| - \|
	\| 1.4478 \| 2900 \| 0.0001 \| - \|
	\| 1.4728 \| 2950 \| 0.0001 \| - \|
	\| 1.4978 \| 3000 \| 0.0001 \| - \|
	\| 1.5227 \| 3050 \| 0.0001 \| - \|
	\| 1.5477 \| 3100 \| 0.0 \| - \|
	\| 1.5726 \| 3150 \| 0.0 \| - \|
	\| 1.5976 \| 3200 \| 0.0001 \| - \|
	\| 1.6226 \| 3250 \| 0.0001 \| - \|
	\| 1.6475 \| 3300 \| 0.0001 \| - \|
	\| 1.6725 \| 3350 \| 0.0001 \| - \|
	\| 1.6975 \| 3400 \| 0.0001 \| - \|
	\| 1.7224 \| 3450 \| 0.0 \| - \|
	\| 1.7474 \| 3500 \| 0.0002 \| - \|
	\| 1.7723 \| 3550 \| 0.0001 \| - \|
	\| 1.7973 \| 3600 \| 0.0 \| - \|
	\| 1.8223 \| 3650 \| 0.0 \| - \|
	\| 1.8472 \| 3700 \| 0.0001 \| - \|
	\| 1.8722 \| 3750 \| 0.0 \| - \|
	\| 1.8972 \| 3800 \| 0.0001 \| - \|
	\| 1.9221 \| 3850 \| 0.0 \| - \|
	\| 1.9471 \| 3900 \| 0.0 \| - \|
	\| 1.9720 \| 3950 \| 0.0001 \| - \|
	\| 1.9970 \| 4000 \| 0.0 \| - \|
	\| 2.0 \| 4006 \| - \| 0.2593 \|
	\| 2.0220 \| 4050 \| 0.0001 \| - \|
	\| 2.0469 \| 4100 \| 0.0001 \| - \|
	\| 2.0719 \| 4150 \| 0.0 \| - \|
	\| 2.0969 \| 4200 \| 0.0001 \| - \|
	\| 2.1218 \| 4250 \| 0.0 \| - \|
	\| 2.1468 \| 4300 \| 0.0001 \| - \|
	\| 2.1717 \| 4350 \| 0.0001 \| - \|
	\| 2.1967 \| 4400 \| 0.0001 \| - \|
	\| 2.2217 \| 4450 \| 0.0001 \| - \|
	\| 2.2466 \| 4500 \| 0.0001 \| - \|
	\| 2.2716 \| 4550 \| 0.0 \| - \|
	\| 2.2966 \| 4600 \| 0.0 \| - \|
	\| 2.3215 \| 4650 \| 0.0 \| - \|
	\| 2.3465 \| 4700 \| 0.0001 \| - \|
	\| 2.3714 \| 4750 \| 0.0001 \| - \|
	\| 2.3964 \| 4800 \| 0.0002 \| - \|
	\| 2.4214 \| 4850 \| 0.0001 \| - \|
	\| 2.4463 \| 4900 \| 0.0001 \| - \|
	\| 2.4713 \| 4950 \| 0.0 \| - \|
	\| 2.4963 \| 5000 \| 0.0001 \| - \|
	\| 2.5212 \| 5050 \| 0.0001 \| - \|
	\| 2.5462 \| 5100 \| 0.0 \| - \|
	\| 2.5711 \| 5150 \| 0.0001 \| - \|
	\| 2.5961 \| 5200 \| 0.0 \| - \|
	\| 2.6211 \| 5250 \| 0.0 \| - \|
	\| 2.6460 \| 5300 \| 0.0 \| - \|
	\| 2.6710 \| 5350 \| 0.0 \| - \|
	\| 2.6960 \| 5400 \| 0.0 \| - \|
	\| 2.7209 \| 5450 \| 0.0 \| - \|
	\| 2.7459 \| 5500 \| 0.0 \| - \|
	\| 2.7708 \| 5550 \| 0.0 \| - \|
	\| 2.7958 \| 5600 \| 0.0001 \| - \|
	\| 2.8208 \| 5650 \| 0.0 \| - \|
	\| 2.8457 \| 5700 \| 0.0 \| - \|
	\| 2.8707 \| 5750 \| 0.0 \| - \|
	\| 2.8957 \| 5800 \| 0.0 \| - \|
	\| 2.9206 \| 5850 \| 0.0 \| - \|
	\| 2.9456 \| 5900 \| 0.0001 \| - \|
	\| 2.9705 \| 5950 \| 0.0 \| - \|
	\| 2.9955 \| 6000 \| 0.0 \| - \|
	\| 3.0 \| 6009 \| - \| 0.2738 \|

	* 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.
	-->