---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- f1
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'Inflation is out of control! Just got my electricity bill and it''s up 25%
    from last year. No wonder the Fed is raising rates, but will it be enough to stop
    the bleeding? #inflation #economy'
- text: The Federal Reserve's decision to raise interest rates by 0.75% has sent shockwaves
    through the financial markets, with the Dow Jones plummeting by over 300 points.
    Analysts warn that this could be the start of a prolonged bear market, as higher
    borrowing costs weigh on consumer spending and business investment. The move is
    seen as a bid to combat inflation, but critics argue that it will only exacerbate
    the economic slowdown.
- text: Alphabet Inc. (GOOGL) shares are trading higher after the tech giant reported
    a 32% surge in quarterly profits, exceeding analyst estimates. The company's revenue
    also rose 13% year-over-year, driven by growth in its cloud computing business.
    Google's parent company is now guiding for even stronger growth in the coming
    quarters, sending its stock price up 5% in pre-market trading.
- text: I'm extremely disappointed in the latest quarterly earnings report from Apple.
    The company's guidance for the next quarter is way off and it's clear they're
    not taking the necessary steps to address their declining iPhone sales. This is
    a major red flag for investors and I'm selling all my shares. The bearish trend
    is clear and I'm not convinced they'll be able to turn things around anytime soon.
- text: 'Just going over the latest quarterly earnings reports and the numbers are
    looking decent. Not a lot of surprises, but overall a stable market. No major
    red flags or green lights, just a steady as she goes kind of day. #marketanalysis
    #finance'
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1
      value: 0.6268844221105527
      name: F1
---

# SetFit with sentence-transformers/paraphrase-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/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:** [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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
|:--------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Neutral | <ul><li>"I'm trying to optimize my investment portfolio and was wondering if anyone has any tips on how to maximize tax efficiency in a taxable brokerage account. I've heard that tax-loss harvesting can be a good strategy, but I'm not sure how to implement it or if it's worth the effort."</li><li>"I've been following the trend of the S&P 500 and it seems like it's consolidating within a tight range. I'm not seeing any strong buy or sell signals, so I'm going to hold off on making any trades for now. Anyone else noticing this? I'm thinking of waiting for a breakout or a clear reversal before entering a position."</li><li>"I've been using Fidelity for my brokerage needs and I'm generally happy with their services. They have a user-friendly interface and their customer support is responsive. That being said, I do wish they had more investment options available, but overall I'd say they're a solid choice for beginners and experienced investors alike."</li></ul>                                                                                                                                   |
| Bullish | <ul><li>'The US labor market continues to show signs of strength, with the latest jobs report revealing a 3.5% unemployment rate, the lowest in nearly 50 years. This is a major boost for the economy, and investors are taking notice. The Dow Jones surged 200 points in response, with many analysts attributing the gains to the improving job market. As a result, stocks in the tech and healthcare sectors are seeing significant gains, with many experts predicting a continued upward trend in the coming weeks. The low unemployment rate is a clear indication that the economy is on the right track, and investors are feeling optimistic about the future.'</li><li>"Just closed out my Q2 with a 20% gain on my portfolio! The market is on fire and I'm loving every minute of it. Stocks are soaring and I'm feeling bullish about the future. #stockmarket #investing #bullrun"</li><li>"Just heard that the new government is planning to reduce corporate taxes to 20% from 30%! This is a huge boost for the economy and I'm feeling very bullish on the stock market right now. #Bullish #Finance #Economy"</li></ul> |
| Bearish | <ul><li>'Economic growth is slowing down and the Fed is raising interest rates again. This is a recipe for disaster. The market is going to tank soon. #BearMarket #EconomicDownturn'</li><li>"Just got my latest paycheck and I'm shocked to see how much of it is going towards groceries and rent due to this OUT. OF. CONTROL inflation. The economy is a joke. #inflation #bearmarket"</li><li>'The latest inflation rate data has sent shockwaves through the market, with the Consumer Price Index (CPI) rising 3.5% in the past 12 months. This is the highest rate in nearly a decade, and economists are warning that it could lead to a recession. The Federal Reserve is expected to raise interest rates again in an effort to combat inflation, but this could have a negative impact on the stock market. As a result, investors are bracing for a potential bear market, with many analysts predicting a 20% drop in the S&P 500 by the end of the year.'</li></ul>                                                                                                                                                           |

## Evaluation

### Metrics
| Label   | F1     |
|:--------|:-------|
| **all** | 0.6269 |

## 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("Inflation is out of control! Just got my electricity bill and it's up 25% from last year. No wonder the Fed is raising rates, but will it be enough to stop the bleeding? #inflation #economy")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 17  | 62.6531 | 119 |

| Label   | Training Sample Count |
|:--------|:----------------------|
| Bearish | 16                    |
| Bullish | 18                    |
| 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.01    | 1       | 0.235         | -               |
| 0.5     | 50      | 0.0307        | -               |
| 1.0     | 100     | 0.0008        | 0.0357          |
| 1.5     | 150     | 0.0006        | -               |
| 2.0     | 200     | 0.0002        | 0.0303          |
| 2.5     | 250     | 0.0001        | -               |
| 3.0     | 300     | 0.0001        | 0.0295          |
| 3.5     | 350     | 0.0001        | -               |
| **4.0** | **400** | **0.0001**    | **0.0281**      |
| 4.5     | 450     | 0.0001        | -               |
| 5.0     | 500     | 0.0001        | 0.0287          |

* 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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