setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer dataset:zeroshot/twitter-financial-news-sentiment arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us
---
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 -->
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### 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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