Add SetFit model

d79d97b about 2 years ago

15.4 kB

	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: A traumatised dog that was found buried up to its head in dirt in France is
	now in safe hands. This is such a... http://t.co/AGQo1479xM
	- text: 'Hibernating pbx irrespective of pitch fatality careerism pan: crbZFZ'
	- text: Stuart Broad Takes Eight Before Joe Root Runs Riot Against Aussies
	- text: Maj Muzzamil Pilot Offr of MI-17 crashed near Mansehra today. http://t.co/kL4R1ccWct
	- text: '@AdriaSimon_: Hailstorm day 2.... #round2 #yyc #yycstorm http://t.co/FqQI8GVLQ4'
	pipeline_tag: text-classification
	inference: true
	base_model: sentence-transformers/all-mpnet-base-v2
	model-index:
	- name: SetFit with sentence-transformers/all-mpnet-base-v2
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.8172066549912435
	name: Accuracy
	---

	# SetFit with sentence-transformers/all-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 384 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 0 \| <ul><li>"Was '80s New #Wave a #Casualty of #AIDS?: Tweet And Since they\x89Ûªd grown up watching David\x89Û_ http://t.co/qBecjli7cx"</li><li>"@CharlesDagnall He's getting 50 here I think. Salt. Wounds. Rub. In."</li><li>'Navy sidelines 3 newest subs http://t.co/gpVZV0249Y'</li></ul> \|
	\| 1 \| <ul><li>'The Latest: More Homes Razed by Northern California Wildfire - ABC News http://t.co/bKsYymvIsg #GN'</li><li>'@Durban_Knight Rescuers are searching for hundreds of migrants in the Mediterranean after a boat carr... http://t.co/cWCVBuBs01 @Nosy_Be'</li><li>'NEMA Ekiti distributed relief materials to affected victims of Rain/Windstorm disaster at Ode-Ekiti in Gbonyin LGA.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8172 \|

	## 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("pEpOo/catastrophy5")
	# Run inference
	preds = model("Stuart Broad Takes Eight Before Joe Root Runs Riot Against Aussies")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 1 \| 14.9796 \| 54 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 1732 \|
	\| 1 \| 1313 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (1, 1)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 20
	- body_learning_rate: (2e-05, 2e-05)
	- head_learning_rate: 2e-05
	- 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: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0001 \| 1 \| 0.3383 \| - \|
	\| 0.0066 \| 50 \| 0.352 \| - \|
	\| 0.0131 \| 100 \| 0.3529 \| - \|
	\| 0.0197 \| 150 \| 0.2286 \| - \|
	\| 0.0263 \| 200 \| 0.2654 \| - \|
	\| 0.0328 \| 250 \| 0.2892 \| - \|
	\| 0.0394 \| 300 \| 0.1808 \| - \|
	\| 0.0460 \| 350 \| 0.2056 \| - \|
	\| 0.0525 \| 400 \| 0.0863 \| - \|
	\| 0.0591 \| 450 \| 0.2034 \| - \|
	\| 0.0657 \| 500 \| 0.1339 \| - \|
	\| 0.0722 \| 550 \| 0.1022 \| - \|
	\| 0.0788 \| 600 \| 0.1083 \| - \|
	\| 0.0854 \| 650 \| 0.1035 \| - \|
	\| 0.0919 \| 700 \| 0.1201 \| - \|
	\| 0.0985 \| 750 \| 0.0626 \| - \|
	\| 0.1051 \| 800 \| 0.1257 \| - \|
	\| 0.1117 \| 850 \| 0.1543 \| - \|
	\| 0.1182 \| 900 \| 0.0367 \| - \|
	\| 0.1248 \| 950 \| 0.1749 \| - \|
	\| 0.1314 \| 1000 \| 0.0553 \| - \|
	\| 0.1379 \| 1050 \| 0.0836 \| - \|
	\| 0.1445 \| 1100 \| 0.0161 \| - \|
	\| 0.1511 \| 1150 \| 0.1149 \| - \|
	\| 0.1576 \| 1200 \| 0.1144 \| - \|
	\| 0.1642 \| 1250 \| 0.0028 \| - \|
	\| 0.1708 \| 1300 \| 0.0037 \| - \|
	\| 0.1773 \| 1350 \| 0.1769 \| - \|
	\| 0.1839 \| 1400 \| 0.0172 \| - \|
	\| 0.1905 \| 1450 \| 0.0397 \| - \|
	\| 0.1970 \| 1500 \| 0.0645 \| - \|
	\| 0.2036 \| 1550 \| 0.0659 \| - \|
	\| 0.2102 \| 1600 \| 0.0014 \| - \|
	\| 0.2167 \| 1650 \| 0.0016 \| - \|
	\| 0.2233 \| 1700 \| 0.0729 \| - \|
	\| 0.2299 \| 1750 \| 0.0072 \| - \|
	\| 0.2364 \| 1800 \| 0.0175 \| - \|
	\| 0.2430 \| 1850 \| 0.0278 \| - \|
	\| 0.2496 \| 1900 \| 0.0537 \| - \|
	\| 0.2561 \| 1950 \| 0.0038 \| - \|
	\| 0.2627 \| 2000 \| 0.087 \| - \|
	\| 0.2693 \| 2050 \| 0.0459 \| - \|
	\| 0.2758 \| 2100 \| 0.0169 \| - \|
	\| 0.2824 \| 2150 \| 0.0112 \| - \|
	\| 0.2890 \| 2200 \| 0.001 \| - \|
	\| 0.2955 \| 2250 \| 0.0204 \| - \|
	\| 0.3021 \| 2300 \| 0.0796 \| - \|
	\| 0.3087 \| 2350 \| 0.0592 \| - \|
	\| 0.3153 \| 2400 \| 0.0003 \| - \|
	\| 0.3218 \| 2450 \| 0.0033 \| - \|
	\| 0.3284 \| 2500 \| 0.0309 \| - \|
	\| 0.3350 \| 2550 \| 0.0065 \| - \|
	\| 0.3415 \| 2600 \| 0.002 \| - \|
	\| 0.3481 \| 2650 \| 0.0076 \| - \|
	\| 0.3547 \| 2700 \| 0.0008 \| - \|
	\| 0.3612 \| 2750 \| 0.0023 \| - \|
	\| 0.3678 \| 2800 \| 0.0028 \| - \|
	\| 0.3744 \| 2850 \| 0.0171 \| - \|
	\| 0.3809 \| 2900 \| 0.0011 \| - \|
	\| 0.3875 \| 2950 \| 0.0015 \| - \|
	\| 0.3941 \| 3000 \| 0.0468 \| - \|
	\| 0.4006 \| 3050 \| 0.0075 \| - \|
	\| 0.4072 \| 3100 \| 0.0009 \| - \|
	\| 0.4138 \| 3150 \| 0.0334 \| - \|
	\| 0.4203 \| 3200 \| 0.0002 \| - \|
	\| 0.4269 \| 3250 \| 0.0001 \| - \|
	\| 0.4335 \| 3300 \| 0.0002 \| - \|
	\| 0.4400 \| 3350 \| 0.0001 \| - \|
	\| 0.4466 \| 3400 \| 0.021 \| - \|
	\| 0.4532 \| 3450 \| 0.0043 \| - \|
	\| 0.4597 \| 3500 \| 0.0084 \| - \|
	\| 0.4663 \| 3550 \| 0.0009 \| - \|
	\| 0.4729 \| 3600 \| 0.0033 \| - \|
	\| 0.4794 \| 3650 \| 0.0035 \| - \|
	\| 0.4860 \| 3700 \| 0.0004 \| - \|
	\| 0.4926 \| 3750 \| 0.0297 \| - \|
	\| 0.4991 \| 3800 \| 0.0004 \| - \|
	\| 0.5057 \| 3850 \| 0.0011 \| - \|
	\| 0.5123 \| 3900 \| 0.0238 \| - \|
	\| 0.5188 \| 3950 \| 0.0248 \| - \|
	\| 0.5254 \| 4000 \| 0.0293 \| - \|
	\| 0.5320 \| 4050 \| 0.0365 \| - \|
	\| 0.5386 \| 4100 \| 0.0261 \| - \|
	\| 0.5451 \| 4150 \| 0.0469 \| - \|
	\| 0.5517 \| 4200 \| 0.0098 \| - \|
	\| 0.5583 \| 4250 \| 0.0002 \| - \|
	\| 0.5648 \| 4300 \| 0.0236 \| - \|
	\| 0.5714 \| 4350 \| 0.0001 \| - \|
	\| 0.5780 \| 4400 \| 0.0001 \| - \|
	\| 0.5845 \| 4450 \| 0.0001 \| - \|
	\| 0.5911 \| 4500 \| 0.0138 \| - \|
	\| 0.5977 \| 4550 \| 0.0116 \| - \|
	\| 0.6042 \| 4600 \| 0.0003 \| - \|
	\| 0.6108 \| 4650 \| 0.0003 \| - \|
	\| 0.6174 \| 4700 \| 0.0001 \| - \|
	\| 0.6239 \| 4750 \| 0.0 \| - \|
	\| 0.6305 \| 4800 \| 0.0246 \| - \|
	\| 0.6371 \| 4850 \| 0.0001 \| - \|
	\| 0.6436 \| 4900 \| 0.0543 \| - \|
	\| 0.6502 \| 4950 \| 0.0001 \| - \|
	\| 0.6568 \| 5000 \| 0.0093 \| - \|
	\| 0.6633 \| 5050 \| 0.0001 \| - \|
	\| 0.6699 \| 5100 \| 0.0 \| - \|
	\| 0.6765 \| 5150 \| 0.0002 \| - \|
	\| 0.6830 \| 5200 \| 0.0001 \| - \|
	\| 0.6896 \| 5250 \| 0.0372 \| - \|
	\| 0.6962 \| 5300 \| 0.0 \| - \|
	\| 0.7027 \| 5350 \| 0.0001 \| - \|
	\| 0.7093 \| 5400 \| 0.0001 \| - \|
	\| 0.7159 \| 5450 \| 0.0003 \| - \|
	\| 0.7224 \| 5500 \| 0.0004 \| - \|
	\| 0.7290 \| 5550 \| 0.0001 \| - \|
	\| 0.7356 \| 5600 \| 0.0 \| - \|
	\| 0.7422 \| 5650 \| 0.0 \| - \|
	\| 0.7487 \| 5700 \| 0.0001 \| - \|
	\| 0.7553 \| 5750 \| 0.0001 \| - \|
	\| 0.7619 \| 5800 \| 0.0 \| - \|
	\| 0.7684 \| 5850 \| 0.0 \| - \|
	\| 0.7750 \| 5900 \| 0.0 \| - \|
	\| 0.7816 \| 5950 \| 0.0 \| - \|
	\| 0.7881 \| 6000 \| 0.0 \| - \|
	\| 0.7947 \| 6050 \| 0.0 \| - \|
	\| 0.8013 \| 6100 \| 0.0 \| - \|
	\| 0.8078 \| 6150 \| 0.0001 \| - \|
	\| 0.8144 \| 6200 \| 0.0001 \| - \|
	\| 0.8210 \| 6250 \| 0.0 \| - \|
	\| 0.8275 \| 6300 \| 0.0 \| - \|
	\| 0.8341 \| 6350 \| 0.0 \| - \|
	\| 0.8407 \| 6400 \| 0.0002 \| - \|
	\| 0.8472 \| 6450 \| 0.0 \| - \|
	\| 0.8538 \| 6500 \| 0.0001 \| - \|
	\| 0.8604 \| 6550 \| 0.0 \| - \|
	\| 0.8669 \| 6600 \| 0.0001 \| - \|
	\| 0.8735 \| 6650 \| 0.0001 \| - \|
	\| 0.8801 \| 6700 \| 0.0 \| - \|
	\| 0.8866 \| 6750 \| 0.0 \| - \|
	\| 0.8932 \| 6800 \| 0.0373 \| - \|
	\| 0.8998 \| 6850 \| 0.0 \| - \|
	\| 0.9063 \| 6900 \| 0.0 \| - \|
	\| 0.9129 \| 6950 \| 0.0272 \| - \|
	\| 0.9195 \| 7000 \| 0.0 \| - \|
	\| 0.9260 \| 7050 \| 0.0 \| - \|
	\| 0.9326 \| 7100 \| 0.0001 \| - \|
	\| 0.9392 \| 7150 \| 0.0 \| - \|
	\| 0.9458 \| 7200 \| 0.0002 \| - \|
	\| 0.9523 \| 7250 \| 0.0001 \| - \|
	\| 0.9589 \| 7300 \| 0.0 \| - \|
	\| 0.9655 \| 7350 \| 0.0 \| - \|
	\| 0.9720 \| 7400 \| 0.0 \| - \|
	\| 0.9786 \| 7450 \| 0.0001 \| - \|
	\| 0.9852 \| 7500 \| 0.0 \| - \|
	\| 0.9917 \| 7550 \| 0.0 \| - \|
	\| 0.9983 \| 7600 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.1
	- Sentence Transformers: 2.2.2
	- Transformers: 4.35.2
	- PyTorch: 2.1.0+cu121
	- Datasets: 2.15.0
	- Tokenizers: 0.15.0

	## 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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