
# 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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'link to the platform <s> Thanks for the suggestion. I will look into it. I have never used it before. <s> link to the platform <s> I will have to check it out. Do you have any recommendations on how to use it?'</li><li>"I wanted to share with you that I'm libertarian <s> I am glad if you're glad. What else positive is happening in your life?"</li><li>"I did not have vertigo so they said again that it's anxiety. <s> Do you get any other symptoms of anxiety?"</li></ul> |
| 1     | <ul><li>'No, I am an atheist.  I am always fascinated by other beliefs though. <s> I can understand that. Do you believe in the existence of an afterlife? I do.'</li><li>'The terrorist was Muslim, wasn’t he? <s> Yes, he is a Muslim. He believes in the Abrahamic religion of Islam. He is a member of the Muslim community.'</li><li>'shortness of breath when bending <s> Have you ever been in a situation where you could not breathe because of your breathing?'</li></ul>              |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8224   |

## 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("AliaeAI/SetFit_dialog_safety_classifier_v3")
# Run inference
preds = model("he is fake lol <s> I am good. Do you like fake news websites? They are websites that publish hoaxes and propaganda.")
```

<!--
### 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.*
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<!--
## 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.*
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### 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   | 6   | 31.9099 | 162 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 2493                  |
| 1     | 2169                  |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0002 | 1     | 0.3486        | -               |
| 0.0086 | 50    | 0.3992        | -               |
| 0.0172 | 100   | 0.2153        | -               |
| 0.0257 | 150   | 0.2183        | -               |
| 0.0343 | 200   | 0.2571        | -               |
| 0.0429 | 250   | 0.2413        | -               |
| 0.0515 | 300   | 0.2744        | -               |
| 0.0601 | 350   | 0.2251        | -               |
| 0.0686 | 400   | 0.2689        | -               |
| 0.0772 | 450   | 0.2648        | -               |
| 0.0858 | 500   | 0.2623        | -               |
| 0.0944 | 550   | 0.2552        | -               |
| 0.1030 | 600   | 0.2212        | -               |
| 0.1115 | 650   | 0.1915        | -               |
| 0.1201 | 700   | 0.2375        | -               |
| 0.1287 | 750   | 0.247         | -               |
| 0.1373 | 800   | 0.1741        | -               |
| 0.1458 | 850   | 0.1809        | -               |
| 0.1544 | 900   | 0.1814        | -               |
| 0.1630 | 950   | 0.207         | -               |
| 0.1716 | 1000  | 0.2063        | -               |
| 0.1802 | 1050  | 0.2517        | -               |
| 0.1887 | 1100  | 0.1816        | -               |
| 0.1973 | 1150  | 0.2468        | -               |
| 0.2059 | 1200  | 0.1389        | -               |
| 0.2145 | 1250  | 0.0957        | -               |
| 0.2231 | 1300  | 0.2491        | -               |
| 0.2316 | 1350  | 0.2112        | -               |
| 0.2402 | 1400  | 0.2088        | -               |
| 0.2488 | 1450  | 0.1264        | -               |
| 0.2574 | 1500  | 0.0419        | -               |
| 0.2660 | 1550  | 0.1118        | -               |
| 0.2745 | 1600  | 0.2811        | -               |
| 0.2831 | 1650  | 0.1204        | -               |
| 0.2917 | 1700  | 0.0964        | -               |
| 0.3003 | 1750  | 0.0148        | -               |
| 0.3089 | 1800  | 0.2089        | -               |
| 0.3174 | 1850  | 0.1001        | -               |
| 0.3260 | 1900  | 0.0963        | -               |
| 0.3346 | 1950  | 0.0944        | -               |
| 0.3432 | 2000  | 0.0099        | -               |
| 0.3518 | 2050  | 0.0962        | -               |
| 0.3603 | 2100  | 0.0089        | -               |
| 0.3689 | 2150  | 0.1429        | -               |
| 0.3775 | 2200  | 0.1504        | -               |
| 0.3861 | 2250  | 0.0636        | -               |
| 0.3946 | 2300  | 0.0104        | -               |
| 0.4032 | 2350  | 0.0728        | -               |
| 0.4118 | 2400  | 0.2608        | -               |
| 0.4204 | 2450  | 0.0451        | -               |
| 0.4290 | 2500  | 0.1333        | -               |
| 0.4375 | 2550  | 0.0334        | -               |
| 0.4461 | 2600  | 0.0035        | -               |
| 0.4547 | 2650  | 0.0025        | -               |
| 0.4633 | 2700  | 0.0838        | -               |
| 0.4719 | 2750  | 0.0239        | -               |
| 0.4804 | 2800  | 0.1097        | -               |
| 0.4890 | 2850  | 0.071         | -               |
| 0.4976 | 2900  | 0.0107        | -               |
| 0.5062 | 2950  | 0.0122        | -               |
| 0.5148 | 3000  | 0.0012        | -               |
| 0.5233 | 3050  | 0.0009        | -               |
| 0.5319 | 3100  | 0.0275        | -               |
| 0.5405 | 3150  | 0.0033        | -               |
| 0.5491 | 3200  | 0.0012        | -               |
| 0.5577 | 3250  | 0.0609        | -               |
| 0.5662 | 3300  | 0.0385        | -               |
| 0.5748 | 3350  | 0.0534        | -               |
| 0.5834 | 3400  | 0.0009        | -               |
| 0.5920 | 3450  | 0.0586        | -               |
| 0.6005 | 3500  | 0.0599        | -               |
| 0.6091 | 3550  | 0.0636        | -               |
| 0.6177 | 3600  | 0.1054        | -               |
| 0.6263 | 3650  | 0.0031        | -               |
| 0.6349 | 3700  | 0.003         | -               |
| 0.6434 | 3750  | 0.0065        | -               |
| 0.6520 | 3800  | 0.0019        | -               |
| 0.6606 | 3850  | 0.0031        | -               |
| 0.6692 | 3900  | 0.0006        | -               |
| 0.6778 | 3950  | 0.0571        | -               |
| 0.6863 | 4000  | 0.1174        | -               |
| 0.6949 | 4050  | 0.0567        | -               |
| 0.7035 | 4100  | 0.0701        | -               |
| 0.7121 | 4150  | 0.002         | -               |
| 0.7207 | 4200  | 0.0257        | -               |
| 0.7292 | 4250  | 0.0326        | -               |
| 0.7378 | 4300  | 0.0031        | -               |
| 0.7464 | 4350  | 0.0004        | -               |
| 0.7550 | 4400  | 0.0374        | -               |
| 0.7636 | 4450  | 0.0113        | -               |
| 0.7721 | 4500  | 0.0376        | -               |
| 0.7807 | 4550  | 0.0009        | -               |
| 0.7893 | 4600  | 0.0027        | -               |
| 0.7979 | 4650  | 0.0744        | -               |
| 0.8065 | 4700  | 0.0009        | -               |
| 0.8150 | 4750  | 0.0005        | -               |
| 0.8236 | 4800  | 0.0502        | -               |
| 0.8322 | 4850  | 0.0004        | -               |
| 0.8408 | 4900  | 0.0016        | -               |
| 0.8493 | 4950  | 0.0013        | -               |
| 0.8579 | 5000  | 0.0006        | -               |
| 0.8665 | 5050  | 0.0202        | -               |
| 0.8751 | 5100  | 0.0022        | -               |
| 0.8837 | 5150  | 0.0004        | -               |
| 0.8922 | 5200  | 0.0369        | -               |
| 0.9008 | 5250  | 0.001         | -               |
| 0.9094 | 5300  | 0.0062        | -               |
| 0.9180 | 5350  | 0.0012        | -               |
| 0.9266 | 5400  | 0.0002        | -               |
| 0.9351 | 5450  | 0.0008        | -               |
| 0.9437 | 5500  | 0.002         | -               |
| 0.9523 | 5550  | 0.01          | -               |
| 0.9609 | 5600  | 0.0175        | -               |
| 0.9695 | 5650  | 0.0003        | -               |
| 0.9780 | 5700  | 0.0044        | -               |
| 0.9866 | 5750  | 0.0003        | -               |
| 0.9952 | 5800  | 0.0629        | -               |
| 1.0038 | 5850  | 0.0082        | -               |
| 1.0124 | 5900  | 0.0043        | -               |
| 1.0209 | 5950  | 0.0001        | -               |
| 1.0295 | 6000  | 0.0349        | -               |
| 1.0381 | 6050  | 0.0774        | -               |
| 1.0467 | 6100  | 0.0004        | -               |
| 1.0553 | 6150  | 0.0075        | -               |
| 1.0638 | 6200  | 0.001         | -               |
| 1.0724 | 6250  | 0.0           | -               |
| 1.0810 | 6300  | 0.0611        | -               |
| 1.0896 | 6350  | 0.0333        | -               |
| 1.0981 | 6400  | 0.0002        | -               |
| 1.1067 | 6450  | 0.0005        | -               |
| 1.1153 | 6500  | 0.0001        | -               |
| 1.1239 | 6550  | 0.0837        | -               |
| 1.1325 | 6600  | 0.0008        | -               |
| 1.1410 | 6650  | 0.0003        | -               |
| 1.1496 | 6700  | 0.0002        | -               |
| 1.1582 | 6750  | 0.0728        | -               |
| 1.1668 | 6800  | 0.0004        | -               |
| 1.1754 | 6850  | 0.0224        | -               |
| 1.1839 | 6900  | 0.0626        | -               |
| 1.1925 | 6950  | 0.0001        | -               |
| 1.2011 | 7000  | 0.0004        | -               |
| 1.2097 | 7050  | 0.0562        | -               |
| 1.2183 | 7100  | 0.1207        | -               |
| 1.2268 | 7150  | 0.061         | -               |
| 1.2354 | 7200  | 0.0001        | -               |
| 1.2440 | 7250  | 0.01          | -               |
| 1.2526 | 7300  | 0.0001        | -               |
| 1.2612 | 7350  | 0.0329        | -               |
| 1.2697 | 7400  | 0.0003        | -               |
| 1.2783 | 7450  | 0.062         | -               |
| 1.2869 | 7500  | 0.0001        | -               |
| 1.2955 | 7550  | 0.0606        | -               |
| 1.3040 | 7600  | 0.0131        | -               |
| 1.3126 | 7650  | 0.0001        | -               |
| 1.3212 | 7700  | 0.057         | -               |
| 1.3298 | 7750  | 0.0557        | -               |
| 1.3384 | 7800  | 0.0217        | -               |
| 1.3469 | 7850  | 0.0453        | -               |
| 1.3555 | 7900  | 0.0354        | -               |
| 1.3641 | 7950  | 0.0168        | -               |
| 1.3727 | 8000  | 0.0679        | -               |
| 1.3813 | 8050  | 0.0           | -               |
| 1.3898 | 8100  | 0.0001        | -               |
| 1.3984 | 8150  | 0.0114        | -               |
| 1.4070 | 8200  | 0.0009        | -               |
| 1.4156 | 8250  | 0.0004        | -               |
| 1.4242 | 8300  | 0.0001        | -               |
| 1.4327 | 8350  | 0.0604        | -               |
| 1.4413 | 8400  | 0.0001        | -               |
| 1.4499 | 8450  | 0.0001        | -               |
| 1.4585 | 8500  | 0.0           | -               |
| 1.4671 | 8550  | 0.0151        | -               |
| 1.4756 | 8600  | 0.0366        | -               |
| 1.4842 | 8650  | 0.0           | -               |
| 1.4928 | 8700  | 0.0235        | -               |
| 1.5014 | 8750  | 0.0205        | -               |
| 1.5100 | 8800  | 0.0           | -               |
| 1.5185 | 8850  | 0.0265        | -               |
| 1.5271 | 8900  | 0.0005        | -               |
| 1.5357 | 8950  | 0.0612        | -               |
| 1.5443 | 9000  | 0.0004        | -               |
| 1.5528 | 9050  | 0.0           | -               |
| 1.5614 | 9100  | 0.0003        | -               |
| 1.5700 | 9150  | 0.0171        | -               |
| 1.5786 | 9200  | 0.0011        | -               |
| 1.5872 | 9250  | 0.0148        | -               |
| 1.5957 | 9300  | 0.0247        | -               |
| 1.6043 | 9350  | 0.0105        | -               |
| 1.6129 | 9400  | 0.0363        | -               |
| 1.6215 | 9450  | 0.0101        | -               |
| 1.6301 | 9500  | 0.0162        | -               |
| 1.6386 | 9550  | 0.0177        | -               |
| 1.6472 | 9600  | 0.008         | -               |
| 1.6558 | 9650  | 0.0157        | -               |
| 1.6644 | 9700  | 0.0           | -               |
| 1.6730 | 9750  | 0.132         | -               |
| 1.6815 | 9800  | 0.0121        | -               |
| 1.6901 | 9850  | 0.0203        | -               |
| 1.6987 | 9900  | 0.007         | -               |
| 1.7073 | 9950  | 0.0243        | -               |
| 1.7159 | 10000 | 0.0002        | -               |
| 1.7244 | 10050 | 0.0001        | -               |
| 1.7330 | 10100 | 0.0066        | -               |
| 1.7416 | 10150 | 0.0001        | -               |
| 1.7502 | 10200 | 0.0001        | -               |
| 1.7588 | 10250 | 0.0           | -               |
| 1.7673 | 10300 | 0.0119        | -               |
| 1.7759 | 10350 | 0.0006        | -               |
| 1.7845 | 10400 | 0.0           | -               |
| 1.7931 | 10450 | 0.02          | -               |
| 1.8016 | 10500 | 0.0           | -               |
| 1.8102 | 10550 | 0.0154        | -               |
| 1.8188 | 10600 | 0.0           | -               |
| 1.8274 | 10650 | 0.0145        | -               |
| 1.8360 | 10700 | 0.0614        | -               |
| 1.8445 | 10750 | 0.0193        | -               |
| 1.8531 | 10800 | 0.0001        | -               |
| 1.8617 | 10850 | 0.0           | -               |
| 1.8703 | 10900 | 0.0639        | -               |
| 1.8789 | 10950 | 0.0001        | -               |
| 1.8874 | 11000 | 0.0001        | -               |
| 1.8960 | 11050 | 0.0002        | -               |
| 1.9046 | 11100 | 0.0001        | -               |
| 1.9132 | 11150 | 0.0001        | -               |
| 1.9218 | 11200 | 0.0001        | -               |
| 1.9303 | 11250 | 0.0           | -               |
| 1.9389 | 11300 | 0.0166        | -               |
| 1.9475 | 11350 | 0.0001        | -               |
| 1.9561 | 11400 | 0.0141        | -               |
| 1.9647 | 11450 | 0.0           | -               |
| 1.9732 | 11500 | 0.0001        | -               |
| 1.9818 | 11550 | 0.0296        | -               |
| 1.9904 | 11600 | 0.0           | -               |
| 1.9990 | 11650 | 0.0           | -               |
| 2.0075 | 11700 | 0.0           | -               |
| 2.0161 | 11750 | 0.0002        | -               |
| 2.0247 | 11800 | 0.0705        | -               |
| 2.0333 | 11850 | 0.0182        | -               |
| 2.0419 | 11900 | 0.0           | -               |
| 2.0504 | 11950 | 0.0           | -               |
| 2.0590 | 12000 | 0.0175        | -               |
| 2.0676 | 12050 | 0.0168        | -               |
| 2.0762 | 12100 | 0.0003        | -               |
| 2.0848 | 12150 | 0.0001        | -               |
| 2.0933 | 12200 | 0.0           | -               |
| 2.1019 | 12250 | 0.0322        | -               |
| 2.1105 | 12300 | 0.0162        | -               |
| 2.1191 | 12350 | 0.0           | -               |
| 2.1277 | 12400 | 0.0265        | -               |
| 2.1362 | 12450 | 0.0           | -               |
| 2.1448 | 12500 | 0.0403        | -               |
| 2.1534 | 12550 | 0.0           | -               |
| 2.1620 | 12600 | 0.0           | -               |
| 2.1706 | 12650 | 0.0           | -               |
| 2.1791 | 12700 | 0.0002        | -               |
| 2.1877 | 12750 | 0.017         | -               |
| 2.1963 | 12800 | 0.0           | -               |
| 2.2049 | 12850 | 0.0139        | -               |
| 2.2135 | 12900 | 0.0001        | -               |
| 2.2220 | 12950 | 0.0231        | -               |
| 2.2306 | 13000 | 0.0           | -               |
| 2.2392 | 13050 | 0.0514        | -               |
| 2.2478 | 13100 | 0.0154        | -               |
| 2.2563 | 13150 | 0.0           | -               |
| 2.2649 | 13200 | 0.012         | -               |
| 2.2735 | 13250 | 0.0169        | -               |
| 2.2821 | 13300 | 0.0155        | -               |
| 2.2907 | 13350 | 0.0           | -               |
| 2.2992 | 13400 | 0.0123        | -               |
| 2.3078 | 13450 | 0.0172        | -               |
| 2.3164 | 13500 | 0.0001        | -               |
| 2.3250 | 13550 | 0.0           | -               |
| 2.3336 | 13600 | 0.0           | -               |
| 2.3421 | 13650 | 0.0           | -               |
| 2.3507 | 13700 | 0.016         | -               |
| 2.3593 | 13750 | 0.0           | -               |
| 2.3679 | 13800 | 0.0           | -               |
| 2.3765 | 13850 | 0.0135        | -               |
| 2.3850 | 13900 | 0.0199        | -               |
| 2.3936 | 13950 | 0.0255        | -               |
| 2.4022 | 14000 | 0.0001        | -               |
| 2.4108 | 14050 | 0.0           | -               |
| 2.4194 | 14100 | 0.023         | -               |
| 2.4279 | 14150 | 0.0           | -               |
| 2.4365 | 14200 | 0.0           | -               |
| 2.4451 | 14250 | 0.0           | -               |
| 2.4537 | 14300 | 0.0173        | -               |
| 2.4623 | 14350 | 0.0           | -               |
| 2.4708 | 14400 | 0.0263        | -               |
| 2.4794 | 14450 | 0.0135        | -               |
| 2.4880 | 14500 | 0.0155        | -               |
| 2.4966 | 14550 | 0.0           | -               |
| 2.5051 | 14600 | 0.0           | -               |
| 2.5137 | 14650 | 0.0503        | -               |
| 2.5223 | 14700 | 0.0315        | -               |
| 2.5309 | 14750 | 0.0172        | -               |
| 2.5395 | 14800 | 0.0184        | -               |
| 2.5480 | 14850 | 0.0189        | -               |
| 2.5566 | 14900 | 0.0           | -               |
| 2.5652 | 14950 | 0.0135        | -               |
| 2.5738 | 15000 | 0.0365        | -               |
| 2.5824 | 15050 | 0.0144        | -               |
| 2.5909 | 15100 | 0.0           | -               |
| 2.5995 | 15150 | 0.0           | -               |
| 2.6081 | 15200 | 0.0           | -               |
| 2.6167 | 15250 | 0.0           | -               |
| 2.6253 | 15300 | 0.0274        | -               |
| 2.6338 | 15350 | 0.0334        | -               |
| 2.6424 | 15400 | 0.0           | -               |
| 2.6510 | 15450 | 0.0316        | -               |
| 2.6596 | 15500 | 0.0           | -               |
| 2.6682 | 15550 | 0.0145        | -               |
| 2.6767 | 15600 | 0.0356        | -               |
| 2.6853 | 15650 | 0.0           | -               |
| 2.6939 | 15700 | 0.0166        | -               |
| 2.7025 | 15750 | 0.0295        | -               |
| 2.7111 | 15800 | 0.0           | -               |
| 2.7196 | 15850 | 0.0           | -               |
| 2.7282 | 15900 | 0.0154        | -               |
| 2.7368 | 15950 | 0.0141        | -               |
| 2.7454 | 16000 | 0.0           | -               |
| 2.7539 | 16050 | 0.0           | -               |
| 2.7625 | 16100 | 0.0125        | -               |
| 2.7711 | 16150 | 0.0           | -               |
| 2.7797 | 16200 | 0.0           | -               |
| 2.7883 | 16250 | 0.026         | -               |
| 2.7968 | 16300 | 0.0           | -               |
| 2.8054 | 16350 | 0.0           | -               |
| 2.8140 | 16400 | 0.0001        | -               |
| 2.8226 | 16450 | 0.015         | -               |
| 2.8312 | 16500 | 0.0219        | -               |
| 2.8397 | 16550 | 0.0           | -               |
| 2.8483 | 16600 | 0.0188        | -               |
| 2.8569 | 16650 | 0.0           | -               |
| 2.8655 | 16700 | 0.0           | -               |
| 2.8741 | 16750 | 0.0           | -               |
| 2.8826 | 16800 | 0.0           | -               |
| 2.8912 | 16850 | 0.0158        | -               |
| 2.8998 | 16900 | 0.0001        | -               |
| 2.9084 | 16950 | 0.0139        | -               |
| 2.9170 | 17000 | 0.0           | -               |
| 2.9255 | 17050 | 0.0159        | -               |
| 2.9341 | 17100 | 0.0181        | -               |
| 2.9427 | 17150 | 0.0           | -               |
| 2.9513 | 17200 | 0.0           | -               |
| 2.9598 | 17250 | 0.0           | -               |
| 2.9684 | 17300 | 0.0171        | -               |
| 2.9770 | 17350 | 0.0           | -               |
| 2.9856 | 17400 | 0.0163        | -               |
| 2.9942 | 17450 | 0.0311        | -               |

### 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: 0.15.2

## 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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{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "he is fake lol <s> I am good. Do you like fake news websites? They are websites that publish hoaxes and propaganda."}, {"text": "Is he fine with that or do you feel like he is just doing that to make you happy. <s> I think he is fine with it. I think it makes me happy to see him happy."}, {"text": "Why the fuck do you hate me, butters is a main character.user: 72.345.456  he has a point. <s> I do not know what you are talking about. Are you talking about the show or the character?"}, {"text": "getting ghosted. I just started getting ghosted by another person. <s> Oh no, I'm sorry to hear that. Have you tried talking to them about it?"}, {"text": "was confirmed to have covid-19, i self-isolated for 14 days. my concern is with my 1 year 6 months son nabi who has been caughing. ? <s> I am so sorry to hear that. I hope your son is doing better. How old is nabi?"}], "pipeline_tag": "text-classification", "inference": true, "base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "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": "accuracy", "value": 0.8223938223938224, "name": "Accuracy"}]}]}]}
setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us