Add SetFit model

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	---
	base_model: sentence-transformers/paraphrase-mpnet-base-v2
	datasets:
	- zeroshot/twitter-financial-news-sentiment
	library_name: setfit
	metrics:
	- f1
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 'Listen to our latest #RegionalView, where Regional Economist Alex Marre discusses
	economic conditions at a conferen… https://t.co/kPM1I5vMfE'
	- text: Peter Thiel Divides Facebook Internally Over Ad Policy (Radio)
	- text: '$SCANX: Mid cap notable movers of interest -- Kohl''s (KSS) advances off
	of recent lows https://t.co/ZM3fmCoLx5'
	- text: US wants China trade deal but won't turn blind eye to Hong Kong, Trump national
	security advisor says https://t.co/dvrewpls4T
	- text: Salarius Pharma files for equity offering
	inference: true
	model-index:
	- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: zeroshot/twitter-financial-news-sentiment
	type: zeroshot/twitter-financial-news-sentiment
	split: test
	metrics:
	- type: f1
	value: 0.6675041876046901
	name: F1
	---

	# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

	This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [zeroshot/twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
	- 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: [zeroshot/twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment)
	<!-- - 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 \|
	\|:--------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| Bullish \| <ul><li>'Energy Up As Exxon Cuts CapEx Spending -- Energy Roundup #economy #MarketScreener https://t.co/pZc2wlKsXZ https://t.co/TX2jWQyK1m'</li><li>"Fed's Mester sees U.S. economy performing well, coronavirus a 'big risk' #economy #MarketScreener… https://t.co/fHOfgB9n6R"</li><li>'Merck to raise quarterly dividend by 11% to 61 cents a share'</li></ul> \|
	\| Bearish \| <ul><li>'If your household has $250,000, you’re in the top 5%. https://t.co/VslRVqg5zP'</li><li>"$DTEGY $DTEGF - Hungary's 4iG calls off purchase of T-Systems unit https://t.co/mY43nNN45s"</li><li>"Here's what has $ZM stock down over 9% https://t.co/V4ikP0o8cl"</li></ul> \|
	\| Neutral \| <ul><li>"How is a bank's GSIB score calculated https://t.co/m7AIabn6U0"</li><li>'$GOOG $GOOGL - Google rivals want EU to investigate vacation rentals https://t.co/8nXAOxhcqG'</li><li>'EU goes into meeting frenzy ahead of February 20 summit on next seven-year budget'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| F1 \|
	\|:--------\|:-------\|
	\| all \| 0.6675 \|

	## 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("setfit_model_id")
	# Run inference
	preds = model("Salarius Pharma files for equity offering")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 3 \| 11.1429 \| 20 \|

	\| Label \| Training Sample Count \|
	\|:--------\|:----------------------\|
	\| Bearish \| 11 \|
	\| Bullish \| 16 \|
	\| Neutral \| 15 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (5, 5)
	- 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 \|
	\|:-------:\|:------:\|:-------------:\|:---------------:\|
	\| 0.0137 \| 1 \| 0.4046 \| - \|
	\| 0.6849 \| 50 \| 0.1465 \| - \|
	\| 1.0 \| 73 \| - \| 0.2203 \|
	\| 1.3699 \| 100 \| 0.002 \| - \|
	\| 2.0 \| 146 \| - \| 0.2563 \|
	\| 2.0548 \| 150 \| 0.0006 \| - \|
	\| 2.7397 \| 200 \| 0.0007 \| - \|
	\| 3.0 \| 219 \| - \| 0.2704 \|
	\| 3.4247 \| 250 \| 0.0006 \| - \|
	\| 4.0 \| 292 \| - \| 0.2813 \|
	\| 4.1096 \| 300 \| 0.0002 \| - \|
	\| 4.7945 \| 350 \| 0.0004 \| - \|
	\| 5.0 \| 365 \| - \| 0.2856 \|

	* The bold row denotes the saved checkpoint.
	### Framework Versions
	- Python: 3.9.19
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 3.0.1
	- Transformers: 4.39.0
	- PyTorch: 2.4.0
	- Datasets: 2.20.0
	- 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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