725_model_v2 / README.md

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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	widget:
	- text: man, product/whatever is my new best friend. i like product but the integration
	of product into office and product is a lot of fun. i just spent the day feeding
	it my training presentation i'm preparing in my day job and it was very helpful.
	almost better than humans.
	- text: that's great news! product is the perfect platform to share these advanced
	product prompts and help more users get the most out of it!
	- text: after only one week's trial of the new product with brand enabled, i have
	replaced my default browser product that i was using for more than 7 years with
	new product. i no longer need to spend a lot of time finding answers from a bunch
	of search results and web pages. it's amazing
	- text: very impressive. brand is finally fighting back. i am just a little worried
	about the scalability of such a high context window size, since even in their
	demos it took quite a while to process everything. regardless, i am very interested
	in seeing what types of capabilities a >1m token size window can unleash.
	- text: product the way it shows the sources is so fucking cool, this new ai is amazing
	pipeline_tag: text-classification
	inference: true
	base_model: BAAI/bge-base-en-v1.5
	model-index:
	- name: SetFit with BAAI/bge-base-en-v1.5
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.8996138996138996
	name: Accuracy
	- type: f1
	value:
	- 0.5217391304347826
	- 0.5142857142857142
	- 0.9478260869565217
	name: F1
	- type: precision
	value:
	- 0.42857142857142855
	- 0.4090909090909091
	- 0.9775784753363229
	name: Precision
	- type: recall
	value:
	- 0.6666666666666666
	- 0.6923076923076923
	- 0.919831223628692
	name: Recall
	---

	# SetFit with BAAI/bge-base-en-v1.5

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) 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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 512 tokens
	- Number of Classes: 3 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 \|
	\|:--------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| neither \| <ul><li>'it might sound strange, but in my opinion, sams intelligence intimidates him from expressing himself and creating personal art. for example, since product is a masterpiece in the sense, the bar is set very high, so he might even subconsciously be unable to put anything out less'</li><li>'lately, i really enjoy the genre of joke that makes you say the punchline in your head.'</li><li>'any idea in regard to the product product not being seen? i have 1 device with it, the rest are missing it. same wufb policies.'</li></ul> \|
	\| pit \| <ul><li>"brand or brand are behaving like lazy interns. when you need something useful from them like researching and consolidating a large bunch of information they'll just tell you to look it up yourself or right away refuse to do the work. useless."</li><li>'the moment i found out what exactly product does i just uninstalled product and went back to 10'</li><li>"at least 80% of the product stuff posted here has produced erroneous results, and many have utilized ip theft/copyright infringement in informing the model. we're not going to spend community time on it at this point."</li></ul> \|
	\| peak \| <ul><li>"man, product/whatever is my new best friend. i like product but the integration of product into office and product is a lot of fun. i just spent the day feeding it my training presentation i'm preparing in my day job and it was very helpful. almost better than humans."</li><li>"excited to share my experience with product, an incredible language model by brand! from answering questions to creative writing, it's a powerful tool that amazes me every time."</li><li>'product in product is a game changer!! here is a list of things it can do: it can answer your questions in natural language. it can summarize content to give you a brief overview it can adjust your pcs settings it can help troubleshoot issues. 1/2'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \| F1 \| Precision \| Recall \|
	\|:--------\|:---------\|:-------------------------------------------------------------\|:--------------------------------------------------------------\|:------------------------------------------------------------\|
	\| all \| 0.8996 \| [0.5217391304347826, 0.5142857142857142, 0.9478260869565217] \| [0.42857142857142855, 0.4090909090909091, 0.9775784753363229] \| [0.6666666666666666, 0.6923076923076923, 0.919831223628692] \|

	## 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("jamiehudson/725_model_v2")
	# Run inference
	preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")
	```

	<!--
	### 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 \| 5 \| 29.1484 \| 90 \|

	\| Label \| Training Sample Count \|
	\|:--------\|:----------------------\|
	\| pit \| 44 \|
	\| peak \| 62 \|
	\| neither \| 150 \|

	### Training Hyperparameters
	- batch_size: (32, 32)
	- 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: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0000 \| 1 \| 0.2383 \| - \|
	\| 0.0119 \| 50 \| 0.2395 \| - \|
	\| 0.0237 \| 100 \| 0.2129 \| - \|
	\| 0.0356 \| 150 \| 0.1317 \| - \|
	\| 0.0474 \| 200 \| 0.0695 \| - \|
	\| 0.0593 \| 250 \| 0.01 \| - \|
	\| 0.0711 \| 300 \| 0.0063 \| - \|
	\| 0.0830 \| 350 \| 0.0028 \| - \|
	\| 0.0948 \| 400 \| 0.0026 \| - \|
	\| 0.1067 \| 450 \| 0.0021 \| - \|
	\| 0.1185 \| 500 \| 0.0018 \| - \|
	\| 0.1304 \| 550 \| 0.0016 \| - \|
	\| 0.1422 \| 600 \| 0.0014 \| - \|
	\| 0.1541 \| 650 \| 0.0015 \| - \|
	\| 0.1659 \| 700 \| 0.0013 \| - \|
	\| 0.1778 \| 750 \| 0.0012 \| - \|
	\| 0.1896 \| 800 \| 0.0012 \| - \|
	\| 0.2015 \| 850 \| 0.0012 \| - \|
	\| 0.2133 \| 900 \| 0.0011 \| - \|
	\| 0.2252 \| 950 \| 0.0011 \| - \|
	\| 0.2370 \| 1000 \| 0.0009 \| - \|
	\| 0.2489 \| 1050 \| 0.001 \| - \|
	\| 0.2607 \| 1100 \| 0.0009 \| - \|
	\| 0.2726 \| 1150 \| 0.0008 \| - \|
	\| 0.2844 \| 1200 \| 0.0008 \| - \|
	\| 0.2963 \| 1250 \| 0.0009 \| - \|
	\| 0.3081 \| 1300 \| 0.0008 \| - \|
	\| 0.3200 \| 1350 \| 0.0007 \| - \|
	\| 0.3318 \| 1400 \| 0.0007 \| - \|
	\| 0.3437 \| 1450 \| 0.0007 \| - \|
	\| 0.3555 \| 1500 \| 0.0006 \| - \|
	\| 0.3674 \| 1550 \| 0.0007 \| - \|
	\| 0.3792 \| 1600 \| 0.0007 \| - \|
	\| 0.3911 \| 1650 \| 0.0008 \| - \|
	\| 0.4029 \| 1700 \| 0.0006 \| - \|
	\| 0.4148 \| 1750 \| 0.0006 \| - \|
	\| 0.4266 \| 1800 \| 0.0006 \| - \|
	\| 0.4385 \| 1850 \| 0.0006 \| - \|
	\| 0.4503 \| 1900 \| 0.0006 \| - \|
	\| 0.4622 \| 1950 \| 0.0006 \| - \|
	\| 0.4740 \| 2000 \| 0.0006 \| - \|
	\| 0.4859 \| 2050 \| 0.0005 \| - \|
	\| 0.4977 \| 2100 \| 0.0006 \| - \|
	\| 0.5096 \| 2150 \| 0.0006 \| - \|
	\| 0.5215 \| 2200 \| 0.0005 \| - \|
	\| 0.5333 \| 2250 \| 0.0005 \| - \|
	\| 0.5452 \| 2300 \| 0.0005 \| - \|
	\| 0.5570 \| 2350 \| 0.0006 \| - \|
	\| 0.5689 \| 2400 \| 0.0005 \| - \|
	\| 0.5807 \| 2450 \| 0.0005 \| - \|
	\| 0.5926 \| 2500 \| 0.0006 \| - \|
	\| 0.6044 \| 2550 \| 0.0006 \| - \|
	\| 0.6163 \| 2600 \| 0.0005 \| - \|
	\| 0.6281 \| 2650 \| 0.0005 \| - \|
	\| 0.6400 \| 2700 \| 0.0005 \| - \|
	\| 0.6518 \| 2750 \| 0.0005 \| - \|
	\| 0.6637 \| 2800 \| 0.0005 \| - \|
	\| 0.6755 \| 2850 \| 0.0005 \| - \|
	\| 0.6874 \| 2900 \| 0.0005 \| - \|
	\| 0.6992 \| 2950 \| 0.0004 \| - \|
	\| 0.7111 \| 3000 \| 0.0004 \| - \|
	\| 0.7229 \| 3050 \| 0.0004 \| - \|
	\| 0.7348 \| 3100 \| 0.0005 \| - \|
	\| 0.7466 \| 3150 \| 0.0005 \| - \|
	\| 0.7585 \| 3200 \| 0.0005 \| - \|
	\| 0.7703 \| 3250 \| 0.0004 \| - \|
	\| 0.7822 \| 3300 \| 0.0004 \| - \|
	\| 0.7940 \| 3350 \| 0.0004 \| - \|
	\| 0.8059 \| 3400 \| 0.0004 \| - \|
	\| 0.8177 \| 3450 \| 0.0004 \| - \|
	\| 0.8296 \| 3500 \| 0.0004 \| - \|
	\| 0.8414 \| 3550 \| 0.0004 \| - \|
	\| 0.8533 \| 3600 \| 0.0004 \| - \|
	\| 0.8651 \| 3650 \| 0.0004 \| - \|
	\| 0.8770 \| 3700 \| 0.0004 \| - \|
	\| 0.8888 \| 3750 \| 0.0004 \| - \|
	\| 0.9007 \| 3800 \| 0.0004 \| - \|
	\| 0.9125 \| 3850 \| 0.0004 \| - \|
	\| 0.9244 \| 3900 \| 0.0005 \| - \|
	\| 0.9362 \| 3950 \| 0.0004 \| - \|
	\| 0.9481 \| 4000 \| 0.0004 \| - \|
	\| 0.9599 \| 4050 \| 0.0004 \| - \|
	\| 0.9718 \| 4100 \| 0.0004 \| - \|
	\| 0.9836 \| 4150 \| 0.0004 \| - \|
	\| 0.9955 \| 4200 \| 0.0004 \| - \|
	\| 0.0000 \| 1 \| 0.2717 \| - \|
	\| 0.0013 \| 50 \| 0.0686 \| - \|
	\| 0.0026 \| 100 \| 0.088 \| - \|
	\| 0.0000 \| 1 \| 0.1796 \| - \|
	\| 0.0013 \| 50 \| 0.0584 \| - \|
	\| 0.0026 \| 100 \| 0.1018 \| - \|
	\| 0.0039 \| 150 \| 0.128 \| - \|
	\| 0.0052 \| 200 \| 0.0761 \| - \|
	\| 0.0065 \| 250 \| 0.0216 \| - \|
	\| 0.0078 \| 300 \| 0.1652 \| - \|
	\| 0.0091 \| 350 \| 0.0384 \| - \|
	\| 0.0104 \| 400 \| 0.0062 \| - \|
	\| 0.0117 \| 450 \| 0.0442 \| - \|
	\| 0.0130 \| 500 \| 0.0452 \| - \|
	\| 0.0143 \| 550 \| 0.0081 \| - \|
	\| 0.0156 \| 600 \| 0.0205 \| - \|
	\| 0.0169 \| 650 \| 0.0125 \| - \|
	\| 0.0182 \| 700 \| 0.0012 \| - \|
	\| 0.0195 \| 750 \| 0.0011 \| - \|
	\| 0.0208 \| 800 \| 0.0315 \| - \|
	\| 0.0221 \| 850 \| 0.0009 \| - \|
	\| 0.0009 \| 1 \| 0.0006 \| - \|
	\| 0.0429 \| 50 \| 0.0008 \| - \|
	\| 0.0858 \| 100 \| 0.0005 \| - \|
	\| 0.1288 \| 150 \| 0.0015 \| - \|
	\| 0.1717 \| 200 \| 0.0013 \| - \|
	\| 0.2146 \| 250 \| 0.0237 \| - \|
	\| 0.2575 \| 300 \| 0.0304 \| - \|
	\| 0.3004 \| 350 \| 0.0005 \| - \|
	\| 0.3433 \| 400 \| 0.0013 \| - \|
	\| 0.3863 \| 450 \| 0.03 \| - \|
	\| 0.4292 \| 500 \| 0.0005 \| - \|
	\| 0.4721 \| 550 \| 0.0006 \| - \|
	\| 0.5150 \| 600 \| 0.0005 \| - \|
	\| 0.5579 \| 650 \| 0.0005 \| - \|
	\| 0.6009 \| 700 \| 0.0004 \| - \|
	\| 0.6438 \| 750 \| 0.0004 \| - \|
	\| 0.6867 \| 800 \| 0.0004 \| - \|
	\| 0.7296 \| 850 \| 0.0004 \| - \|
	\| 0.7725 \| 900 \| 0.0004 \| - \|
	\| 0.8155 \| 950 \| 0.0003 \| - \|
	\| 0.8584 \| 1000 \| 0.0004 \| - \|
	\| 0.9013 \| 1050 \| 0.0003 \| - \|
	\| 0.9442 \| 1100 \| 0.0004 \| - \|
	\| 0.9871 \| 1150 \| 0.0003 \| - \|
	\| 1.0300 \| 1200 \| 0.0003 \| - \|
	\| 1.0730 \| 1250 \| 0.0004 \| - \|
	\| 1.1159 \| 1300 \| 0.0003 \| - \|
	\| 1.1588 \| 1350 \| 0.0005 \| - \|
	\| 1.2017 \| 1400 \| 0.0003 \| - \|
	\| 1.2446 \| 1450 \| 0.0003 \| - \|
	\| 1.2876 \| 1500 \| 0.0003 \| - \|
	\| 1.3305 \| 1550 \| 0.0003 \| - \|
	\| 1.3734 \| 1600 \| 0.0003 \| - \|
	\| 1.4163 \| 1650 \| 0.0003 \| - \|
	\| 1.4592 \| 1700 \| 0.0003 \| - \|
	\| 1.5021 \| 1750 \| 0.0005 \| - \|
	\| 1.5451 \| 1800 \| 0.0003 \| - \|
	\| 1.5880 \| 1850 \| 0.0003 \| - \|
	\| 1.6309 \| 1900 \| 0.0003 \| - \|
	\| 1.6738 \| 1950 \| 0.0005 \| - \|
	\| 1.7167 \| 2000 \| 0.0003 \| - \|
	\| 1.7597 \| 2050 \| 0.0007 \| - \|
	\| 1.8026 \| 2100 \| 0.0003 \| - \|
	\| 1.8455 \| 2150 \| 0.0003 \| - \|
	\| 1.8884 \| 2200 \| 0.0003 \| - \|
	\| 1.9313 \| 2250 \| 0.0003 \| - \|
	\| 1.9742 \| 2300 \| 0.0003 \| - \|
	\| 2.0172 \| 2350 \| 0.0003 \| - \|
	\| 2.0601 \| 2400 \| 0.0003 \| - \|
	\| 2.1030 \| 2450 \| 0.0003 \| - \|
	\| 2.1459 \| 2500 \| 0.0003 \| - \|
	\| 2.1888 \| 2550 \| 0.0002 \| - \|
	\| 2.2318 \| 2600 \| 0.0003 \| - \|
	\| 2.2747 \| 2650 \| 0.0004 \| - \|
	\| 2.3176 \| 2700 \| 0.0002 \| - \|
	\| 2.3605 \| 2750 \| 0.0003 \| - \|
	\| 2.4034 \| 2800 \| 0.0002 \| - \|
	\| 2.4464 \| 2850 \| 0.0002 \| - \|
	\| 2.4893 \| 2900 \| 0.0002 \| - \|
	\| 2.5322 \| 2950 \| 0.0002 \| - \|
	\| 2.5751 \| 3000 \| 0.0002 \| - \|
	\| 2.6180 \| 3050 \| 0.0004 \| - \|
	\| 2.6609 \| 3100 \| 0.0004 \| - \|
	\| 2.7039 \| 3150 \| 0.0003 \| - \|
	\| 2.7468 \| 3200 \| 0.0003 \| - \|
	\| 2.7897 \| 3250 \| 0.0003 \| - \|
	\| 2.8326 \| 3300 \| 0.0003 \| - \|
	\| 2.8755 \| 3350 \| 0.0003 \| - \|
	\| 2.9185 \| 3400 \| 0.0003 \| - \|
	\| 2.9614 \| 3450 \| 0.0005 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 2.5.1
	- Transformers: 4.38.1
	- PyTorch: 2.1.0+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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