---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'fuel_network Fuel The worlds fastest modular execution layer Sway Language '
- text: 'enjin Enjin Enjin Blockchain allows seamless no code integration of NFTs
    in video games and other platforms with NFT functions at the protocol level '
- text: 'bobbyclee Bobby Lee  Ballet Worlds EASIEST Cold Storage Founder  CEO of was
    Board Member Cofounder BTCChina  BTCC Author of The Promise of Bitcoin  available
    on '
- text: 'tradermayne Mayne '
- text: 'novogratz Mike Novogratz CEO GLXY CN Early Investormushroom TheBailProject
    Disclaimer '
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.99
      name: Accuracy
---

# SetFit with BAAI/bge-small-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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:** 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                                                                                                                                                                                                                       |
|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ORGANIZATIONAL | <ul><li>'cryptonewton Shelby BitGet partner '</li><li>'trezor Trezor Crypto security made easy'</li><li>'forbes Forbes Sign up now for Forbes free daily newsletter for unmatched insights and exclusive reporting '</li></ul> |
| INDIVIDUAL     | <ul><li>'anbessa100 ANBESSA No paid service Never DM u'</li><li>'sbf_ftx SBF '</li><li>'machibigbrother Machi Big Brother '</li></ul>                                                                                          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.99     |

## 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("kasparas12/is_organizational_model")
# Run inference
preds = model("tradermayne Mayne ")
```

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### Out-of-Scope Use

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 15.7338 | 35  |

| Label          | Training Sample Count |
|:---------------|:----------------------|
| INDIVIDUAL     | 423                   |
| ORGANIZATIONAL | 377                   |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0016 | 1     | 0.2511        | -               |
| 0.0789 | 50    | 0.2505        | -               |
| 0.1577 | 100   | 0.2225        | -               |
| 0.2366 | 150   | 0.2103        | -               |
| 0.3155 | 200   | 0.1383        | -               |
| 0.3943 | 250   | 0.0329        | -               |
| 0.4732 | 300   | 0.0098        | -               |
| 0.5521 | 350   | 0.0034        | -               |
| 0.6309 | 400   | 0.0019        | -               |
| 0.7098 | 450   | 0.0015        | -               |
| 0.7886 | 500   | 0.0014        | -               |
| 0.8675 | 550   | 0.0012        | -               |
| 0.0001 | 1     | 0.2524        | -               |
| 0.0050 | 50    | 0.2115        | -               |
| 0.0099 | 100   | 0.193         | -               |
| 0.0001 | 1     | 0.2424        | -               |
| 0.0050 | 50    | 0.2038        | -               |
| 0.0099 | 100   | 0.1782        | -               |
| 0.0001 | 1     | 0.2208        | -               |
| 0.0050 | 50    | 0.1931        | -               |
| 0.0099 | 100   | 0.1629        | -               |
| 0.0149 | 150   | 0.2716        | -               |
| 0.0199 | 200   | 0.18          | -               |
| 0.0249 | 250   | 0.2504        | -               |
| 0.0298 | 300   | 0.1936        | -               |
| 0.0348 | 350   | 0.1764        | -               |
| 0.0398 | 400   | 0.1817        | -               |
| 0.0447 | 450   | 0.0624        | -               |
| 0.0497 | 500   | 0.1183        | -               |
| 0.0547 | 550   | 0.0793        | -               |
| 0.0596 | 600   | 0.0281        | -               |
| 0.0646 | 650   | 0.0876        | -               |
| 0.0696 | 700   | 0.1701        | -               |
| 0.0746 | 750   | 0.0468        | -               |
| 0.0795 | 800   | 0.0525        | -               |
| 0.0845 | 850   | 0.0783        | -               |
| 0.0895 | 900   | 0.0342        | -               |
| 0.0944 | 950   | 0.0158        | -               |
| 0.0994 | 1000  | 0.0286        | -               |
| 0.1044 | 1050  | 0.0016        | -               |
| 0.1094 | 1100  | 0.0014        | -               |
| 0.1143 | 1150  | 0.0298        | -               |
| 0.1193 | 1200  | 0.018         | -               |
| 0.1243 | 1250  | 0.0299        | -               |
| 0.1292 | 1300  | 0.0019        | -               |
| 0.1342 | 1350  | 0.0253        | -               |
| 0.1392 | 1400  | 0.0009        | -               |
| 0.1441 | 1450  | 0.0009        | -               |
| 0.1491 | 1500  | 0.0011        | -               |
| 0.1541 | 1550  | 0.0006        | -               |
| 0.1591 | 1600  | 0.0006        | -               |
| 0.1640 | 1650  | 0.0008        | -               |
| 0.1690 | 1700  | 0.0005        | -               |
| 0.1740 | 1750  | 0.0007        | -               |
| 0.1789 | 1800  | 0.0006        | -               |
| 0.1839 | 1850  | 0.0006        | -               |
| 0.1889 | 1900  | 0.0006        | -               |
| 0.1939 | 1950  | 0.0012        | -               |
| 0.1988 | 2000  | 0.0004        | -               |
| 0.2038 | 2050  | 0.0006        | -               |
| 0.2088 | 2100  | 0.0005        | -               |
| 0.2137 | 2150  | 0.0005        | -               |
| 0.2187 | 2200  | 0.0005        | -               |
| 0.2237 | 2250  | 0.0004        | -               |
| 0.2287 | 2300  | 0.0005        | -               |
| 0.2336 | 2350  | 0.0004        | -               |
| 0.2386 | 2400  | 0.0004        | -               |
| 0.2436 | 2450  | 0.0003        | -               |
| 0.2485 | 2500  | 0.0004        | -               |
| 0.2535 | 2550  | 0.0004        | -               |
| 0.2585 | 2600  | 0.0004        | -               |
| 0.2634 | 2650  | 0.0004        | -               |
| 0.2684 | 2700  | 0.0004        | -               |
| 0.2734 | 2750  | 0.0004        | -               |
| 0.2784 | 2800  | 0.0056        | -               |
| 0.2833 | 2850  | 0.0004        | -               |
| 0.2883 | 2900  | 0.0003        | -               |
| 0.2933 | 2950  | 0.0003        | -               |
| 0.2982 | 3000  | 0.0004        | -               |
| 0.3032 | 3050  | 0.0003        | -               |
| 0.3082 | 3100  | 0.0003        | -               |
| 0.3132 | 3150  | 0.0003        | -               |
| 0.3181 | 3200  | 0.0003        | -               |
| 0.3231 | 3250  | 0.0004        | -               |
| 0.3281 | 3300  | 0.0003        | -               |
| 0.3330 | 3350  | 0.0003        | -               |
| 0.3380 | 3400  | 0.0003        | -               |
| 0.3430 | 3450  | 0.0003        | -               |
| 0.3479 | 3500  | 0.0003        | -               |
| 0.3529 | 3550  | 0.0003        | -               |
| 0.3579 | 3600  | 0.0003        | -               |
| 0.3629 | 3650  | 0.0003        | -               |
| 0.3678 | 3700  | 0.0003        | -               |
| 0.3728 | 3750  | 0.0004        | -               |
| 0.3778 | 3800  | 0.0004        | -               |
| 0.3827 | 3850  | 0.0003        | -               |
| 0.3877 | 3900  | 0.0003        | -               |
| 0.3927 | 3950  | 0.0003        | -               |
| 0.3977 | 4000  | 0.0003        | -               |
| 0.4026 | 4050  | 0.0003        | -               |
| 0.4076 | 4100  | 0.0003        | -               |
| 0.4126 | 4150  | 0.0003        | -               |
| 0.4175 | 4200  | 0.0003        | -               |
| 0.4225 | 4250  | 0.0003        | -               |
| 0.4275 | 4300  | 0.0003        | -               |
| 0.4324 | 4350  | 0.0003        | -               |
| 0.4374 | 4400  | 0.0002        | -               |
| 0.4424 | 4450  | 0.0003        | -               |
| 0.4474 | 4500  | 0.0003        | -               |
| 0.4523 | 4550  | 0.0003        | -               |
| 0.4573 | 4600  | 0.0003        | -               |
| 0.4623 | 4650  | 0.0003        | -               |
| 0.4672 | 4700  | 0.0002        | -               |
| 0.4722 | 4750  | 0.0002        | -               |
| 0.4772 | 4800  | 0.0003        | -               |
| 0.4822 | 4850  | 0.0002        | -               |
| 0.4871 | 4900  | 0.0002        | -               |
| 0.4921 | 4950  | 0.0002        | -               |
| 0.4971 | 5000  | 0.0003        | -               |
| 0.5020 | 5050  | 0.0003        | -               |
| 0.5070 | 5100  | 0.0002        | -               |
| 0.5120 | 5150  | 0.0003        | -               |
| 0.5169 | 5200  | 0.0002        | -               |
| 0.5219 | 5250  | 0.0002        | -               |
| 0.5269 | 5300  | 0.0002        | -               |
| 0.5319 | 5350  | 0.0002        | -               |
| 0.5368 | 5400  | 0.0003        | -               |
| 0.5418 | 5450  | 0.0002        | -               |
| 0.5468 | 5500  | 0.0002        | -               |
| 0.5517 | 5550  | 0.0002        | -               |
| 0.5567 | 5600  | 0.0002        | -               |
| 0.5617 | 5650  | 0.0002        | -               |
| 0.5667 | 5700  | 0.0002        | -               |
| 0.5716 | 5750  | 0.0002        | -               |
| 0.5766 | 5800  | 0.0002        | -               |
| 0.5816 | 5850  | 0.0002        | -               |
| 0.5865 | 5900  | 0.0002        | -               |
| 0.5915 | 5950  | 0.0002        | -               |
| 0.5965 | 6000  | 0.0002        | -               |
| 0.6015 | 6050  | 0.0002        | -               |
| 0.6064 | 6100  | 0.0002        | -               |
| 0.6114 | 6150  | 0.0002        | -               |
| 0.6164 | 6200  | 0.0002        | -               |
| 0.6213 | 6250  | 0.0002        | -               |
| 0.6263 | 6300  | 0.0002        | -               |
| 0.6313 | 6350  | 0.0002        | -               |
| 0.6362 | 6400  | 0.0002        | -               |
| 0.6412 | 6450  | 0.0002        | -               |
| 0.6462 | 6500  | 0.0002        | -               |
| 0.6512 | 6550  | 0.0002        | -               |
| 0.6561 | 6600  | 0.0002        | -               |
| 0.6611 | 6650  | 0.0002        | -               |
| 0.6661 | 6700  | 0.0002        | -               |
| 0.6710 | 6750  | 0.0002        | -               |
| 0.6760 | 6800  | 0.0002        | -               |
| 0.6810 | 6850  | 0.0002        | -               |
| 0.6860 | 6900  | 0.0002        | -               |
| 0.6909 | 6950  | 0.0002        | -               |
| 0.6959 | 7000  | 0.0002        | -               |
| 0.7009 | 7050  | 0.0002        | -               |
| 0.7058 | 7100  | 0.0002        | -               |
| 0.7108 | 7150  | 0.0002        | -               |
| 0.7158 | 7200  | 0.0002        | -               |
| 0.7207 | 7250  | 0.0002        | -               |
| 0.7257 | 7300  | 0.0002        | -               |
| 0.7307 | 7350  | 0.0002        | -               |
| 0.7357 | 7400  | 0.0002        | -               |
| 0.7406 | 7450  | 0.0002        | -               |
| 0.7456 | 7500  | 0.0002        | -               |
| 0.7506 | 7550  | 0.0002        | -               |
| 0.7555 | 7600  | 0.0002        | -               |
| 0.7605 | 7650  | 0.0002        | -               |
| 0.7655 | 7700  | 0.0248        | -               |
| 0.7705 | 7750  | 0.0002        | -               |
| 0.7754 | 7800  | 0.0002        | -               |
| 0.7804 | 7850  | 0.0002        | -               |
| 0.7854 | 7900  | 0.0002        | -               |
| 0.7903 | 7950  | 0.0002        | -               |
| 0.7953 | 8000  | 0.0002        | -               |
| 0.8003 | 8050  | 0.0002        | -               |
| 0.8052 | 8100  | 0.0002        | -               |
| 0.8102 | 8150  | 0.0002        | -               |
| 0.8152 | 8200  | 0.0002        | -               |
| 0.8202 | 8250  | 0.0002        | -               |
| 0.8251 | 8300  | 0.0002        | -               |
| 0.8301 | 8350  | 0.0002        | -               |
| 0.8351 | 8400  | 0.0002        | -               |
| 0.8400 | 8450  | 0.0001        | -               |
| 0.8450 | 8500  | 0.0002        | -               |
| 0.8500 | 8550  | 0.0002        | -               |
| 0.8550 | 8600  | 0.0001        | -               |
| 0.8599 | 8650  | 0.0002        | -               |
| 0.8649 | 8700  | 0.0002        | -               |
| 0.8699 | 8750  | 0.0002        | -               |
| 0.8748 | 8800  | 0.0002        | -               |
| 0.8798 | 8850  | 0.0002        | -               |
| 0.8848 | 8900  | 0.0002        | -               |
| 0.8898 | 8950  | 0.0003        | -               |
| 0.8947 | 9000  | 0.0002        | -               |
| 0.8997 | 9050  | 0.0001        | -               |
| 0.9047 | 9100  | 0.0002        | -               |
| 0.9096 | 9150  | 0.0002        | -               |
| 0.9146 | 9200  | 0.0002        | -               |
| 0.9196 | 9250  | 0.0002        | -               |
| 0.9245 | 9300  | 0.0002        | -               |
| 0.9295 | 9350  | 0.0002        | -               |
| 0.9345 | 9400  | 0.0002        | -               |
| 0.9395 | 9450  | 0.0002        | -               |
| 0.9444 | 9500  | 0.0002        | -               |
| 0.9494 | 9550  | 0.0001        | -               |
| 0.9544 | 9600  | 0.0001        | -               |
| 0.9593 | 9650  | 0.0002        | -               |
| 0.9643 | 9700  | 0.0002        | -               |
| 0.9693 | 9750  | 0.0002        | -               |
| 0.9743 | 9800  | 0.0001        | -               |
| 0.9792 | 9850  | 0.0002        | -               |
| 0.9842 | 9900  | 0.0002        | -               |
| 0.9892 | 9950  | 0.0002        | -               |
| 0.9941 | 10000 | 0.0002        | -               |
| 0.9991 | 10050 | 0.0002        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1

## 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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