catastrophy8 / README.md

Add SetFit model

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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: "Rly tragedy in MP: Some live to recount horror: \x89ÛÏWhen I saw coaches\
	\ of my train plunging into water I called my daughters and said t..."
	- text: You must be annihilated!
	- text: 'Severe Thunderstorms and Flash Flooding Possible in the Mid-South and Midwest
	http://t.co/uAhIcWpIh4 #WEATHER #ENVIRONMENT #CLIMATE #NATURE'
	- text: 'everyone''s wonder who will win and I''m over here wondering are those grapes
	real ?????? #BB17'
	- text: i swea it feels like im about to explode ??
	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: 0.9203152364273205
	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>'To fight bioterrorism sir.'</li><li>'85V-265V 10W LED Warm White Light Motion Sensor Outdoor Flood Light PIR Lamp AUC http://t.co/NJVPXzMj5V http://t.co/Ijd7WzV5t9'</li><li>'Photo: referencereference: xekstrin: I THOUGHT THE NOSTRILS WERE EYES AND I ALMOST CRIED FROM FEAR partake... http://t.co/O7yYjLuKfJ'</li></ul> \|
	\| 1 \| <ul><li>'Police officer wounded suspect dead after exchanging shots: RICHMOND Va. (AP) \x89ÛÓ A Richmond police officer wa... http://t.co/Y0qQS2L7bS'</li><li>"There's a weird siren going off here...I hope Hunterston isn't in the process of blowing itself to smithereens..."</li><li>'Iranian warship points weapon at American helicopter... http://t.co/cgFZk8Ha1R'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9203 \|

	## 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/catastrophy8")
	# Run inference
	preds = model("You must be annihilated!")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 1 \| 14.5506 \| 54 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 438 \|
	\| 1 \| 323 \|

	### Training Hyperparameters
	- batch_size: (20, 20)
	- 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 \| 0.3847 \| - \|
	\| 0.0044 \| 50 \| 0.3738 \| - \|
	\| 0.0088 \| 100 \| 0.2274 \| - \|
	\| 0.0131 \| 150 \| 0.2747 \| - \|
	\| 0.0175 \| 200 \| 0.2251 \| - \|
	\| 0.0219 \| 250 \| 0.2562 \| - \|
	\| 0.0263 \| 300 \| 0.2623 \| - \|
	\| 0.0307 \| 350 \| 0.1904 \| - \|
	\| 0.0350 \| 400 \| 0.2314 \| - \|
	\| 0.0394 \| 450 \| 0.1669 \| - \|
	\| 0.0438 \| 500 \| 0.1135 \| - \|
	\| 0.0482 \| 550 \| 0.1489 \| - \|
	\| 0.0525 \| 600 \| 0.1907 \| - \|
	\| 0.0569 \| 650 \| 0.1728 \| - \|
	\| 0.0613 \| 700 \| 0.125 \| - \|
	\| 0.0657 \| 750 \| 0.109 \| - \|
	\| 0.0701 \| 800 \| 0.0968 \| - \|
	\| 0.0744 \| 850 \| 0.2101 \| - \|
	\| 0.0788 \| 900 \| 0.1974 \| - \|
	\| 0.0832 \| 950 \| 0.1986 \| - \|
	\| 0.0876 \| 1000 \| 0.0747 \| - \|
	\| 0.0920 \| 1050 \| 0.1117 \| - \|
	\| 0.0963 \| 1100 \| 0.1092 \| - \|
	\| 0.1007 \| 1150 \| 0.1582 \| - \|
	\| 0.1051 \| 1200 \| 0.1243 \| - \|
	\| 0.1095 \| 1250 \| 0.2873 \| - \|
	\| 0.1139 \| 1300 \| 0.2415 \| - \|
	\| 0.1182 \| 1350 \| 0.1264 \| - \|
	\| 0.1226 \| 1400 \| 0.127 \| - \|
	\| 0.1270 \| 1450 \| 0.1308 \| - \|
	\| 0.1314 \| 1500 \| 0.0669 \| - \|
	\| 0.1358 \| 1550 \| 0.1218 \| - \|
	\| 0.1401 \| 1600 \| 0.114 \| - \|
	\| 0.1445 \| 1650 \| 0.0612 \| - \|
	\| 0.1489 \| 1700 \| 0.0527 \| - \|
	\| 0.1533 \| 1750 \| 0.1421 \| - \|
	\| 0.1576 \| 1800 \| 0.0048 \| - \|
	\| 0.1620 \| 1850 \| 0.0141 \| - \|
	\| 0.1664 \| 1900 \| 0.0557 \| - \|
	\| 0.1708 \| 1950 \| 0.0206 \| - \|
	\| 0.1752 \| 2000 \| 0.1171 \| - \|
	\| 0.1795 \| 2050 \| 0.0968 \| - \|
	\| 0.1839 \| 2100 \| 0.0243 \| - \|
	\| 0.1883 \| 2150 \| 0.0233 \| - \|
	\| 0.1927 \| 2200 \| 0.0738 \| - \|
	\| 0.1971 \| 2250 \| 0.0071 \| - \|
	\| 0.2014 \| 2300 \| 0.0353 \| - \|
	\| 0.2058 \| 2350 \| 0.0602 \| - \|
	\| 0.2102 \| 2400 \| 0.003 \| - \|
	\| 0.2146 \| 2450 \| 0.0625 \| - \|
	\| 0.2190 \| 2500 \| 0.0173 \| - \|
	\| 0.2233 \| 2550 \| 0.1017 \| - \|
	\| 0.2277 \| 2600 \| 0.0582 \| - \|
	\| 0.2321 \| 2650 \| 0.0437 \| - \|
	\| 0.2365 \| 2700 \| 0.104 \| - \|
	\| 0.2408 \| 2750 \| 0.0156 \| - \|
	\| 0.2452 \| 2800 \| 0.0034 \| - \|
	\| 0.2496 \| 2850 \| 0.0343 \| - \|
	\| 0.2540 \| 2900 \| 0.1106 \| - \|
	\| 0.2584 \| 2950 \| 0.001 \| - \|
	\| 0.2627 \| 3000 \| 0.004 \| - \|
	\| 0.2671 \| 3050 \| 0.0074 \| - \|
	\| 0.2715 \| 3100 \| 0.0849 \| - \|
	\| 0.2759 \| 3150 \| 0.0009 \| - \|
	\| 0.2803 \| 3200 \| 0.0379 \| - \|
	\| 0.2846 \| 3250 \| 0.0109 \| - \|
	\| 0.2890 \| 3300 \| 0.0019 \| - \|
	\| 0.2934 \| 3350 \| 0.0154 \| - \|
	\| 0.2978 \| 3400 \| 0.0017 \| - \|
	\| 0.3022 \| 3450 \| 0.0003 \| - \|
	\| 0.3065 \| 3500 \| 0.0002 \| - \|
	\| 0.3109 \| 3550 \| 0.0025 \| - \|
	\| 0.3153 \| 3600 \| 0.0123 \| - \|
	\| 0.3197 \| 3650 \| 0.0007 \| - \|
	\| 0.3240 \| 3700 \| 0.0534 \| - \|
	\| 0.3284 \| 3750 \| 0.0004 \| - \|
	\| 0.3328 \| 3800 \| 0.0084 \| - \|
	\| 0.3372 \| 3850 \| 0.0088 \| - \|
	\| 0.3416 \| 3900 \| 0.0201 \| - \|
	\| 0.3459 \| 3950 \| 0.0002 \| - \|
	\| 0.3503 \| 4000 \| 0.0102 \| - \|
	\| 0.3547 \| 4050 \| 0.0043 \| - \|
	\| 0.3591 \| 4100 \| 0.0124 \| - \|
	\| 0.3635 \| 4150 \| 0.0845 \| - \|
	\| 0.3678 \| 4200 \| 0.0002 \| - \|
	\| 0.3722 \| 4250 \| 0.0014 \| - \|
	\| 0.3766 \| 4300 \| 0.1131 \| - \|
	\| 0.3810 \| 4350 \| 0.0612 \| - \|
	\| 0.3854 \| 4400 \| 0.0577 \| - \|
	\| 0.3897 \| 4450 \| 0.0235 \| - \|
	\| 0.3941 \| 4500 \| 0.0156 \| - \|
	\| 0.3985 \| 4550 \| 0.0078 \| - \|
	\| 0.4029 \| 4600 \| 0.0356 \| - \|
	\| 0.4073 \| 4650 \| 0.0595 \| - \|
	\| 0.4116 \| 4700 \| 0.0001 \| - \|
	\| 0.4160 \| 4750 \| 0.0018 \| - \|
	\| 0.4204 \| 4800 \| 0.0013 \| - \|
	\| 0.4248 \| 4850 \| 0.0008 \| - \|
	\| 0.4291 \| 4900 \| 0.0832 \| - \|
	\| 0.4335 \| 4950 \| 0.0083 \| - \|
	\| 0.4379 \| 5000 \| 0.0007 \| - \|
	\| 0.4423 \| 5050 \| 0.0417 \| - \|
	\| 0.4467 \| 5100 \| 0.0001 \| - \|
	\| 0.4510 \| 5150 \| 0.0218 \| - \|
	\| 0.4554 \| 5200 \| 0.0001 \| - \|
	\| 0.4598 \| 5250 \| 0.0012 \| - \|
	\| 0.4642 \| 5300 \| 0.0002 \| - \|
	\| 0.4686 \| 5350 \| 0.0006 \| - \|
	\| 0.4729 \| 5400 \| 0.0223 \| - \|
	\| 0.4773 \| 5450 \| 0.0612 \| - \|
	\| 0.4817 \| 5500 \| 0.0004 \| - \|
	\| 0.4861 \| 5550 \| 0.0 \| - \|
	\| 0.4905 \| 5600 \| 0.0007 \| - \|
	\| 0.4948 \| 5650 \| 0.0007 \| - \|
	\| 0.4992 \| 5700 \| 0.0116 \| - \|
	\| 0.5036 \| 5750 \| 0.0262 \| - \|
	\| 0.5080 \| 5800 \| 0.0336 \| - \|
	\| 0.5123 \| 5850 \| 0.026 \| - \|
	\| 0.5167 \| 5900 \| 0.0004 \| - \|
	\| 0.5211 \| 5950 \| 0.0001 \| - \|
	\| 0.5255 \| 6000 \| 0.0001 \| - \|
	\| 0.5299 \| 6050 \| 0.0001 \| - \|
	\| 0.5342 \| 6100 \| 0.0029 \| - \|
	\| 0.5386 \| 6150 \| 0.0001 \| - \|
	\| 0.5430 \| 6200 \| 0.0699 \| - \|
	\| 0.5474 \| 6250 \| 0.0262 \| - \|
	\| 0.5518 \| 6300 \| 0.0269 \| - \|
	\| 0.5561 \| 6350 \| 0.0002 \| - \|
	\| 0.5605 \| 6400 \| 0.0666 \| - \|
	\| 0.5649 \| 6450 \| 0.0209 \| - \|
	\| 0.5693 \| 6500 \| 0.0003 \| - \|
	\| 0.5737 \| 6550 \| 0.0001 \| - \|
	\| 0.5780 \| 6600 \| 0.0115 \| - \|
	\| 0.5824 \| 6650 \| 0.0003 \| - \|
	\| 0.5868 \| 6700 \| 0.0001 \| - \|
	\| 0.5912 \| 6750 \| 0.0056 \| - \|
	\| 0.5956 \| 6800 \| 0.0603 \| - \|
	\| 0.5999 \| 6850 \| 0.0002 \| - \|
	\| 0.6043 \| 6900 \| 0.0003 \| - \|
	\| 0.6087 \| 6950 \| 0.0092 \| - \|
	\| 0.6131 \| 7000 \| 0.0562 \| - \|
	\| 0.6174 \| 7050 \| 0.0408 \| - \|
	\| 0.6218 \| 7100 \| 0.0001 \| - \|
	\| 0.6262 \| 7150 \| 0.0035 \| - \|
	\| 0.6306 \| 7200 \| 0.0337 \| - \|
	\| 0.6350 \| 7250 \| 0.0024 \| - \|
	\| 0.6393 \| 7300 \| 0.0005 \| - \|
	\| 0.6437 \| 7350 \| 0.0001 \| - \|
	\| 0.6481 \| 7400 \| 0.0 \| - \|
	\| 0.6525 \| 7450 \| 0.0001 \| - \|
	\| 0.6569 \| 7500 \| 0.0002 \| - \|
	\| 0.6612 \| 7550 \| 0.0004 \| - \|
	\| 0.6656 \| 7600 \| 0.0125 \| - \|
	\| 0.6700 \| 7650 \| 0.0005 \| - \|
	\| 0.6744 \| 7700 \| 0.0157 \| - \|
	\| 0.6788 \| 7750 \| 0.0055 \| - \|
	\| 0.6831 \| 7800 \| 0.0 \| - \|
	\| 0.6875 \| 7850 \| 0.0053 \| - \|
	\| 0.6919 \| 7900 \| 0.0 \| - \|
	\| 0.6963 \| 7950 \| 0.0002 \| - \|
	\| 0.7006 \| 8000 \| 0.0002 \| - \|
	\| 0.7050 \| 8050 \| 0.0001 \| - \|
	\| 0.7094 \| 8100 \| 0.0001 \| - \|
	\| 0.7138 \| 8150 \| 0.0001 \| - \|
	\| 0.7182 \| 8200 \| 0.0007 \| - \|
	\| 0.7225 \| 8250 \| 0.0002 \| - \|
	\| 0.7269 \| 8300 \| 0.0001 \| - \|
	\| 0.7313 \| 8350 \| 0.0 \| - \|
	\| 0.7357 \| 8400 \| 0.0156 \| - \|
	\| 0.7401 \| 8450 \| 0.0098 \| - \|
	\| 0.7444 \| 8500 \| 0.0 \| - \|
	\| 0.7488 \| 8550 \| 0.0001 \| - \|
	\| 0.7532 \| 8600 \| 0.0042 \| - \|
	\| 0.7576 \| 8650 \| 0.0 \| - \|
	\| 0.7620 \| 8700 \| 0.0 \| - \|
	\| 0.7663 \| 8750 \| 0.0056 \| - \|
	\| 0.7707 \| 8800 \| 0.0 \| - \|
	\| 0.7751 \| 8850 \| 0.0 \| - \|
	\| 0.7795 \| 8900 \| 0.013 \| - \|
	\| 0.7839 \| 8950 \| 0.0 \| - \|
	\| 0.7882 \| 9000 \| 0.0001 \| - \|
	\| 0.7926 \| 9050 \| 0.0 \| - \|
	\| 0.7970 \| 9100 \| 0.0 \| - \|
	\| 0.8014 \| 9150 \| 0.0 \| - \|
	\| 0.8057 \| 9200 \| 0.0 \| - \|
	\| 0.8101 \| 9250 \| 0.0 \| - \|
	\| 0.8145 \| 9300 \| 0.0007 \| - \|
	\| 0.8189 \| 9350 \| 0.0 \| - \|
	\| 0.8233 \| 9400 \| 0.0002 \| - \|
	\| 0.8276 \| 9450 \| 0.0 \| - \|
	\| 0.8320 \| 9500 \| 0.0 \| - \|
	\| 0.8364 \| 9550 \| 0.0089 \| - \|
	\| 0.8408 \| 9600 \| 0.0001 \| - \|
	\| 0.8452 \| 9650 \| 0.0 \| - \|
	\| 0.8495 \| 9700 \| 0.0 \| - \|
	\| 0.8539 \| 9750 \| 0.0 \| - \|
	\| 0.8583 \| 9800 \| 0.0565 \| - \|
	\| 0.8627 \| 9850 \| 0.0161 \| - \|
	\| 0.8671 \| 9900 \| 0.0 \| - \|
	\| 0.8714 \| 9950 \| 0.0246 \| - \|
	\| 0.8758 \| 10000 \| 0.0 \| - \|
	\| 0.8802 \| 10050 \| 0.0 \| - \|
	\| 0.8846 \| 10100 \| 0.012 \| - \|
	\| 0.8889 \| 10150 \| 0.0 \| - \|
	\| 0.8933 \| 10200 \| 0.0 \| - \|
	\| 0.8977 \| 10250 \| 0.0 \| - \|
	\| 0.9021 \| 10300 \| 0.0 \| - \|
	\| 0.9065 \| 10350 \| 0.0 \| - \|
	\| 0.9108 \| 10400 \| 0.0 \| - \|
	\| 0.9152 \| 10450 \| 0.0 \| - \|
	\| 0.9196 \| 10500 \| 0.0 \| - \|
	\| 0.9240 \| 10550 \| 0.0023 \| - \|
	\| 0.9284 \| 10600 \| 0.0 \| - \|
	\| 0.9327 \| 10650 \| 0.0006 \| - \|
	\| 0.9371 \| 10700 \| 0.0 \| - \|
	\| 0.9415 \| 10750 \| 0.0 \| - \|
	\| 0.9459 \| 10800 \| 0.0 \| - \|
	\| 0.9503 \| 10850 \| 0.0 \| - \|
	\| 0.9546 \| 10900 \| 0.0 \| - \|
	\| 0.9590 \| 10950 \| 0.0243 \| - \|
	\| 0.9634 \| 11000 \| 0.0107 \| - \|
	\| 0.9678 \| 11050 \| 0.0001 \| - \|
	\| 0.9721 \| 11100 \| 0.0 \| - \|
	\| 0.9765 \| 11150 \| 0.0 \| - \|
	\| 0.9809 \| 11200 \| 0.0274 \| - \|
	\| 0.9853 \| 11250 \| 0.0 \| - \|
	\| 0.9897 \| 11300 \| 0.0 \| - \|
	\| 0.9940 \| 11350 \| 0.0 \| - \|
	\| 0.9984 \| 11400 \| 0.0 \| - \|
	\| 0.0007 \| 1 \| 0.2021 \| - \|
	\| 0.0329 \| 50 \| 0.1003 \| - \|
	\| 0.0657 \| 100 \| 0.2282 \| - \|
	\| 0.0986 \| 150 \| 0.0507 \| - \|
	\| 0.1314 \| 200 \| 0.046 \| - \|
	\| 0.1643 \| 250 \| 0.0001 \| - \|
	\| 0.1971 \| 300 \| 0.0495 \| - \|
	\| 0.2300 \| 350 \| 0.0031 \| - \|
	\| 0.2628 \| 400 \| 0.0004 \| - \|
	\| 0.2957 \| 450 \| 0.0002 \| - \|
	\| 0.3285 \| 500 \| 0.0 \| - \|
	\| 0.3614 \| 550 \| 0.0 \| - \|
	\| 0.3942 \| 600 \| 0.0 \| - \|
	\| 0.4271 \| 650 \| 0.0001 \| - \|
	\| 0.4599 \| 700 \| 0.0 \| - \|
	\| 0.4928 \| 750 \| 0.0 \| - \|
	\| 0.5256 \| 800 \| 0.0 \| - \|
	\| 0.5585 \| 850 \| 0.0 \| - \|
	\| 0.5913 \| 900 \| 0.0001 \| - \|
	\| 0.6242 \| 950 \| 0.0 \| - \|
	\| 0.6570 \| 1000 \| 0.0001 \| - \|
	\| 0.6899 \| 1050 \| 0.0 \| - \|
	\| 0.7227 \| 1100 \| 0.0 \| - \|
	\| 0.7556 \| 1150 \| 0.0 \| - \|
	\| 0.7884 \| 1200 \| 0.0 \| - \|
	\| 0.8213 \| 1250 \| 0.0 \| - \|
	\| 0.8541 \| 1300 \| 0.0 \| - \|
	\| 0.8870 \| 1350 \| 0.0 \| - \|
	\| 0.9198 \| 1400 \| 0.0 \| - \|
	\| 0.9527 \| 1450 \| 0.0001 \| - \|
	\| 0.9855 \| 1500 \| 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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