
# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 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                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2     | <ul><li>'840989 otp proceed canara bank mobile banking . valid 15 minute . share otp . - canara bank . kbl8a1ju0mt'</li><li>'cheque . 000102 issue riya collection rs . 12,000.00 present / c xxxxx546157 return unpaid insufficient fund . team idfc bank'</li><li>'avl bal / c xxxx0959 10 - jul-2022 06:06:24 inr 0.00 . combine avl bal inr 0.00 . use mb app track / c - kotak bank'</li></ul>                                                       |
| 0     | <ul><li>'/ c . xxxxxxxx7146 debit   rs.11933.00 16 - 09 - 23 / c   xxxxxxxx4716 credit ( imp ref   325908759095 ) . warm regard , yes bank'</li><li>'send rs.290.00 kotak bank ac x4524 bharatpe90727843812@yesbankltd 13-10-23.upi ref 328684167136 . , kotak.com/fraud'</li><li>'rs.295 transfer / c ... 4322 : lien_marking_fo . total bal : rs.188.8cr . avlbl amt : rs.609.97(28 - 06 - 2022 16:39:53 ) - bank baroda'</li></ul>                     |
| 1     | <ul><li>'rs 15000credite / c xx4524via neft neofirst technology india private- utr ref hsbcn23276508097 ; avail . bal.:rs 215180.62kotak bank'</li><li>'/ c : xx6775 credit rs.60.00 14 - 11 - 2023 10:47:49 upi - id 8733076955@omni ( upi ref 331800008439).-canara bank'</li><li>'rs.28 credit / c ... 7783 upi/323962847509 kiwicashback_ax . total bal : rs.122751.36cr . avlbl amt : rs.94671.36(27 - 08 - 2023 15:37:01 ) - bank baroda'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9716   |

## 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("vipinbansal179/SetFit_sms_Analyzer1")
# Run inference
preds = model("472448 otp set hdfc bank 4 digit login pin . share otp you?call 18002586161")
```

<!--
### Downstream Use

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-->

<!--
### Out-of-Scope Use

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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

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 23.17  | 65  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 231                   |
| 1     | 131                   |
| 2     | 338                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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 |
|:-------:|:---------:|:-------------:|:---------------:|
| 0.0001  | 1         | 0.2945        | -               |
| 0.0026  | 50        | 0.3574        | -               |
| 0.0052  | 100       | 0.2512        | -               |
| 0.0079  | 150       | 0.2319        | -               |
| 0.0105  | 200       | 0.2787        | -               |
| 0.0131  | 250       | 0.2129        | -               |
| 0.0157  | 300       | 0.2189        | -               |
| 0.0183  | 350       | 0.0857        | -               |
| 0.0210  | 400       | 0.0932        | -               |
| 0.0236  | 450       | 0.065         | -               |
| 0.0262  | 500       | 0.0553        | -               |
| 0.0288  | 550       | 0.0674        | -               |
| 0.0314  | 600       | 0.0239        | -               |
| 0.0341  | 650       | 0.0054        | -               |
| 0.0367  | 700       | 0.0025        | -               |
| 0.0393  | 750       | 0.002         | -               |
| 0.0419  | 800       | 0.0007        | -               |
| 0.0446  | 850       | 0.001         | -               |
| 0.0472  | 900       | 0.0008        | -               |
| 0.0498  | 950       | 0.0008        | -               |
| 0.0524  | 1000      | 0.0003        | -               |
| 0.0550  | 1050      | 0.0012        | -               |
| 0.0577  | 1100      | 0.002         | -               |
| 0.0603  | 1150      | 0.0192        | -               |
| 0.0629  | 1200      | 0.0041        | -               |
| 0.0655  | 1250      | 0.0002        | -               |
| 0.0681  | 1300      | 0.0001        | -               |
| 0.0708  | 1350      | 0.0001        | -               |
| 0.0734  | 1400      | 0.0001        | -               |
| 0.0760  | 1450      | 0.0004        | -               |
| 0.0786  | 1500      | 0.0003        | -               |
| 0.0812  | 1550      | 0.0002        | -               |
| 0.0839  | 1600      | 0.0004        | -               |
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| 0.0891  | 1700      | 0.0002        | -               |
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| 0.0943  | 1800      | 0.0001        | -               |
| 0.0970  | 1850      | 0.0001        | -               |
| 0.0996  | 1900      | 0.0001        | -               |
| 0.1022  | 1950      | 0.0001        | -               |
| 0.1048  | 2000      | 0.0001        | -               |
| 0.1075  | 2050      | 0.0015        | -               |
| 0.1101  | 2100      | 0.0001        | -               |
| 0.1127  | 2150      | 0.0001        | -               |
| 0.1153  | 2200      | 0.0001        | -               |
| 0.1179  | 2250      | 0.0001        | -               |
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| 0.8072  | 15400     | 0.0           | -               |
| 0.8098  | 15450     | 0.0           | -               |
| 0.8125  | 15500     | 0.0           | -               |
| 0.8151  | 15550     | 0.0           | -               |
| 0.8177  | 15600     | 0.0           | -               |
| 0.8203  | 15650     | 0.0           | -               |
| 0.8229  | 15700     | 0.0           | -               |
| 0.8256  | 15750     | 0.0           | -               |
| 0.8282  | 15800     | 0.0           | -               |
| 0.8308  | 15850     | 0.0           | -               |
| 0.8334  | 15900     | 0.0           | -               |
| 0.8360  | 15950     | 0.0           | -               |
| 0.8387  | 16000     | 0.0           | -               |
| 0.8413  | 16050     | 0.0           | -               |
| 0.8439  | 16100     | 0.0           | -               |
| 0.8465  | 16150     | 0.0           | -               |
| 0.8491  | 16200     | 0.0           | -               |
| 0.8518  | 16250     | 0.0           | -               |
| 0.8544  | 16300     | 0.0           | -               |
| 0.8570  | 16350     | 0.0           | -               |
| 0.8596  | 16400     | 0.0           | -               |
| 0.8622  | 16450     | 0.0           | -               |
| 0.8649  | 16500     | 0.0           | -               |
| 0.8675  | 16550     | 0.0           | -               |
| 0.8701  | 16600     | 0.0           | -               |
| 0.8727  | 16650     | 0.0           | -               |
| 0.8754  | 16700     | 0.0           | -               |
| 0.8780  | 16750     | 0.0           | -               |
| 0.8806  | 16800     | 0.0           | -               |
| 0.8832  | 16850     | 0.0           | -               |
| 0.8858  | 16900     | 0.0           | -               |
| 0.8885  | 16950     | 0.0           | -               |
| 0.8911  | 17000     | 0.0           | -               |
| 0.8937  | 17050     | 0.0           | -               |
| 0.8963  | 17100     | 0.0           | -               |
| 0.8989  | 17150     | 0.0           | -               |
| 0.9016  | 17200     | 0.0           | -               |
| 0.9042  | 17250     | 0.0           | -               |
| 0.9068  | 17300     | 0.0           | -               |
| 0.9094  | 17350     | 0.0           | -               |
| 0.9120  | 17400     | 0.0           | -               |
| 0.9147  | 17450     | 0.0           | -               |
| 0.9173  | 17500     | 0.0           | -               |
| 0.9199  | 17550     | 0.0           | -               |
| 0.9225  | 17600     | 0.0           | -               |
| 0.9251  | 17650     | 0.0           | -               |
| 0.9278  | 17700     | 0.0           | -               |
| 0.9304  | 17750     | 0.0           | -               |
| 0.9330  | 17800     | 0.0           | -               |
| 0.9356  | 17850     | 0.0           | -               |
| 0.9383  | 17900     | 0.0           | -               |
| 0.9409  | 17950     | 0.0           | -               |
| 0.9435  | 18000     | 0.0           | -               |
| 0.9461  | 18050     | 0.0           | -               |
| 0.9487  | 18100     | 0.0           | -               |
| 0.9514  | 18150     | 0.0           | -               |
| 0.9540  | 18200     | 0.0           | -               |
| 0.9566  | 18250     | 0.0           | -               |
| 0.9592  | 18300     | 0.0           | -               |
| 0.9618  | 18350     | 0.0           | -               |
| 0.9645  | 18400     | 0.0           | -               |
| 0.9671  | 18450     | 0.0           | -               |
| 0.9697  | 18500     | 0.0           | -               |
| 0.9723  | 18550     | 0.0           | -               |
| 0.9749  | 18600     | 0.0           | -               |
| 0.9776  | 18650     | 0.0           | -               |
| 0.9802  | 18700     | 0.0           | -               |
| 0.9828  | 18750     | 0.0           | -               |
| 0.9854  | 18800     | 0.0           | -               |
| 0.9880  | 18850     | 0.0           | -               |
| 0.9907  | 18900     | 0.0           | -               |
| 0.9933  | 18950     | 0.0           | -               |
| 0.9959  | 19000     | 0.0           | -               |
| 0.9985  | 19050     | 0.0           | -               |
| **1.0** | **19078** | **-**         | **0.0437**      |
| 1.0012  | 19100     | 0.0           | -               |
| 1.0038  | 19150     | 0.0           | -               |
| 1.0064  | 19200     | 0.0           | -               |
| 1.0090  | 19250     | 0.0           | -               |
| 1.0116  | 19300     | 0.0           | -               |
| 1.0143  | 19350     | 0.0           | -               |
| 1.0169  | 19400     | 0.0           | -               |
| 1.0195  | 19450     | 0.3698        | -               |
| 1.0221  | 19500     | 0.1546        | -               |
| 1.0247  | 19550     | 0.0179        | -               |
| 1.0274  | 19600     | 0.0004        | -               |
| 1.0300  | 19650     | 0.0005        | -               |
| 1.0326  | 19700     | 0.0           | -               |
| 1.0352  | 19750     | 0.0002        | -               |
| 1.0378  | 19800     | 0.0           | -               |
| 1.0405  | 19850     | 0.0           | -               |
| 1.0431  | 19900     | 0.0           | -               |
| 1.0457  | 19950     | 0.0002        | -               |
| 1.0483  | 20000     | 0.0011        | -               |
| 1.0509  | 20050     | 0.0           | -               |
| 1.0536  | 20100     | 0.0           | -               |
| 1.0562  | 20150     | 0.0           | -               |
| 1.0588  | 20200     | 0.0003        | -               |
| 1.0614  | 20250     | 0.0           | -               |
| 1.0641  | 20300     | 0.0003        | -               |
| 1.0667  | 20350     | 0.0003        | -               |
| 1.0693  | 20400     | 0.0           | -               |
| 1.0719  | 20450     | 0.0           | -               |
| 1.0745  | 20500     | 0.0           | -               |
| 1.0772  | 20550     | 0.0           | -               |
| 1.0798  | 20600     | 0.0           | -               |
| 1.0824  | 20650     | 0.0           | -               |
| 1.0850  | 20700     | 0.0           | -               |
| 1.0876  | 20750     | 0.0           | -               |
| 1.0903  | 20800     | 0.0           | -               |
| 1.0929  | 20850     | 0.0           | -               |
| 1.0955  | 20900     | 0.0           | -               |
| 1.0981  | 20950     | 0.0           | -               |
| 1.1007  | 21000     | 0.0           | -               |
| 1.1034  | 21050     | 0.0           | -               |
| 1.1060  | 21100     | 0.0           | -               |
| 1.1086  | 21150     | 0.0           | -               |
| 1.1112  | 21200     | 0.0           | -               |
| 1.1138  | 21250     | 0.0           | -               |
| 1.1165  | 21300     | 0.0           | -               |
| 1.1191  | 21350     | 0.0           | -               |
| 1.1217  | 21400     | 0.0           | -               |
| 1.1243  | 21450     | 0.0           | -               |
| 1.1270  | 21500     | 0.0           | -               |
| 1.1296  | 21550     | 0.0           | -               |
| 1.1322  | 21600     | 0.0           | -               |
| 1.1348  | 21650     | 0.0           | -               |
| 1.1374  | 21700     | 0.0           | -               |
| 1.1401  | 21750     | 0.0           | -               |
| 1.1427  | 21800     | 0.0           | -               |
| 1.1453  | 21850     | 0.0           | -               |
| 1.1479  | 21900     | 0.0           | -               |
| 1.1505  | 21950     | 0.0           | -               |
| 1.1532  | 22000     | 0.0           | -               |
| 1.1558  | 22050     | 0.0           | -               |
| 1.1584  | 22100     | 0.0           | -               |
| 1.1610  | 22150     | 0.0           | -               |
| 1.1636  | 22200     | 0.0           | -               |
| 1.1663  | 22250     | 0.0           | -               |
| 1.1689  | 22300     | 0.0           | -               |
| 1.1715  | 22350     | 0.0           | -               |
| 1.1741  | 22400     | 0.0           | -               |
| 1.1767  | 22450     | 0.0           | -               |
| 1.1794  | 22500     | 0.0           | -               |
| 1.1820  | 22550     | 0.0           | -               |
| 1.1846  | 22600     | 0.0           | -               |
| 1.1872  | 22650     | 0.0           | -               |
| 1.1899  | 22700     | 0.0           | -               |
| 1.1925  | 22750     | 0.0           | -               |
| 1.1951  | 22800     | 0.0           | -               |
| 1.1977  | 22850     | 0.0           | -               |
| 1.2003  | 22900     | 0.0           | -               |
| 1.2030  | 22950     | 0.0           | -               |
| 1.2056  | 23000     | 0.0           | -               |
| 1.2082  | 23050     | 0.0           | -               |
| 1.2108  | 23100     | 0.0           | -               |
| 1.2134  | 23150     | 0.0           | -               |
| 1.2161  | 23200     | 0.0           | -               |
| 1.2187  | 23250     | 0.0           | -               |
| 1.2213  | 23300     | 0.0           | -               |
| 1.2239  | 23350     | 0.0           | -               |
| 1.2265  | 23400     | 0.0           | -               |
| 1.2292  | 23450     | 0.0           | -               |
| 1.2318  | 23500     | 0.0           | -               |
| 1.2344  | 23550     | 0.0           | -               |
| 1.2370  | 23600     | 0.0           | -               |
| 1.2396  | 23650     | 0.0           | -               |
| 1.2423  | 23700     | 0.0           | -               |
| 1.2449  | 23750     | 0.0           | -               |
| 1.2475  | 23800     | 0.0           | -               |
| 1.2501  | 23850     | 0.0           | -               |
| 1.2528  | 23900     | 0.0           | -               |
| 1.2554  | 23950     | 0.0           | -               |
| 1.2580  | 24000     | 0.0           | -               |
| 1.2606  | 24050     | 0.0           | -               |
| 1.2632  | 24100     | 0.0           | -               |
| 1.2659  | 24150     | 0.0           | -               |
| 1.2685  | 24200     | 0.0           | -               |
| 1.2711  | 24250     | 0.0           | -               |
| 1.2737  | 24300     | 0.0           | -               |
| 1.2763  | 24350     | 0.0           | -               |
| 1.2790  | 24400     | 0.0           | -               |
| 1.2816  | 24450     | 0.0           | -               |
| 1.2842  | 24500     | 0.0           | -               |
| 1.2868  | 24550     | 0.0           | -               |
| 1.2894  | 24600     | 0.0           | -               |
| 1.2921  | 24650     | 0.0           | -               |
| 1.2947  | 24700     | 0.0           | -               |
| 1.2973  | 24750     | 0.0           | -               |
| 1.2999  | 24800     | 0.0           | -               |
| 1.3025  | 24850     | 0.0           | -               |
| 1.3052  | 24900     | 0.0           | -               |
| 1.3078  | 24950     | 0.0           | -               |
| 1.3104  | 25000     | 0.0           | -               |
| 1.3130  | 25050     | 0.0           | -               |
| 1.3157  | 25100     | 0.0           | -               |
| 1.3183  | 25150     | 0.0           | -               |
| 1.3209  | 25200     | 0.0           | -               |
| 1.3235  | 25250     | 0.0           | -               |
| 1.3261  | 25300     | 0.0           | -               |
| 1.3288  | 25350     | 0.0           | -               |
| 1.3314  | 25400     | 0.0           | -               |
| 1.3340  | 25450     | 0.0           | -               |
| 1.3366  | 25500     | 0.0           | -               |
| 1.3392  | 25550     | 0.0           | -               |
| 1.3419  | 25600     | 0.0           | -               |
| 1.3445  | 25650     | 0.0           | -               |
| 1.3471  | 25700     | 0.0           | -               |
| 1.3497  | 25750     | 0.0           | -               |
| 1.3523  | 25800     | 0.0           | -               |
| 1.3550  | 25850     | 0.0           | -               |
| 1.3576  | 25900     | 0.0           | -               |
| 1.3602  | 25950     | 0.0           | -               |
| 1.3628  | 26000     | 0.0           | -               |
| 1.3654  | 26050     | 0.0           | -               |
| 1.3681  | 26100     | 0.0           | -               |
| 1.3707  | 26150     | 0.0           | -               |
| 1.3733  | 26200     | 0.0           | -               |
| 1.3759  | 26250     | 0.0           | -               |
| 1.3786  | 26300     | 0.0           | -               |
| 1.3812  | 26350     | 0.0           | -               |
| 1.3838  | 26400     | 0.0           | -               |
| 1.3864  | 26450     | 0.0           | -               |
| 1.3890  | 26500     | 0.0           | -               |
| 1.3917  | 26550     | 0.0           | -               |
| 1.3943  | 26600     | 0.0           | -               |
| 1.3969  | 26650     | 0.0           | -               |
| 1.3995  | 26700     | 0.0           | -               |
| 1.4021  | 26750     | 0.0           | -               |
| 1.4048  | 26800     | 0.0           | -               |
| 1.4074  | 26850     | 0.0           | -               |
| 1.4100  | 26900     | 0.0           | -               |
| 1.4126  | 26950     | 0.0           | -               |
| 1.4152  | 27000     | 0.0           | -               |
| 1.4179  | 27050     | 0.0           | -               |
| 1.4205  | 27100     | 0.0           | -               |
| 1.4231  | 27150     | 0.0           | -               |
| 1.4257  | 27200     | 0.0           | -               |
| 1.4283  | 27250     | 0.0           | -               |
| 1.4310  | 27300     | 0.0           | -               |
| 1.4336  | 27350     | 0.0           | -               |
| 1.4362  | 27400     | 0.0           | -               |
| 1.4388  | 27450     | 0.0           | -               |
| 1.4415  | 27500     | 0.0           | -               |
| 1.4441  | 27550     | 0.0           | -               |
| 1.4467  | 27600     | 0.0           | -               |
| 1.4493  | 27650     | 0.0           | -               |
| 1.4519  | 27700     | 0.0           | -               |
| 1.4546  | 27750     | 0.0           | -               |
| 1.4572  | 27800     | 0.0           | -               |
| 1.4598  | 27850     | 0.0           | -               |
| 1.4624  | 27900     | 0.0           | -               |
| 1.4650  | 27950     | 0.0           | -               |
| 1.4677  | 28000     | 0.0           | -               |
| 1.4703  | 28050     | 0.0           | -               |
| 1.4729  | 28100     | 0.0           | -               |
| 1.4755  | 28150     | 0.0           | -               |
| 1.4781  | 28200     | 0.0           | -               |
| 1.4808  | 28250     | 0.0           | -               |
| 1.4834  | 28300     | 0.0           | -               |
| 1.4860  | 28350     | 0.0           | -               |
| 1.4886  | 28400     | 0.0           | -               |
| 1.4912  | 28450     | 0.0           | -               |
| 1.4939  | 28500     | 0.0           | -               |
| 1.4965  | 28550     | 0.0           | -               |
| 1.4991  | 28600     | 0.0           | -               |
| 1.5017  | 28650     | 0.0           | -               |
| 1.5044  | 28700     | 0.0           | -               |
| 1.5070  | 28750     | 0.0           | -               |
| 1.5096  | 28800     | 0.0           | -               |
| 1.5122  | 28850     | 0.0           | -               |
| 1.5148  | 28900     | 0.0           | -               |
| 1.5175  | 28950     | 0.0           | -               |
| 1.5201  | 29000     | 0.0           | -               |
| 1.5227  | 29050     | 0.0           | -               |
| 1.5253  | 29100     | 0.0           | -               |
| 1.5279  | 29150     | 0.0           | -               |
| 1.5306  | 29200     | 0.0           | -               |
| 1.5332  | 29250     | 0.0           | -               |
| 1.5358  | 29300     | 0.0           | -               |
| 1.5384  | 29350     | 0.0           | -               |
| 1.5410  | 29400     | 0.0           | -               |
| 1.5437  | 29450     | 0.0           | -               |
| 1.5463  | 29500     | 0.0           | -               |
| 1.5489  | 29550     | 0.0           | -               |
| 1.5515  | 29600     | 0.0           | -               |
| 1.5541  | 29650     | 0.0           | -               |
| 1.5568  | 29700     | 0.0           | -               |
| 1.5594  | 29750     | 0.0           | -               |
| 1.5620  | 29800     | 0.0           | -               |
| 1.5646  | 29850     | 0.0           | -               |
| 1.5673  | 29900     | 0.0           | -               |
| 1.5699  | 29950     | 0.0           | -               |
| 1.5725  | 30000     | 0.0           | -               |
| 1.5751  | 30050     | 0.0           | -               |
| 1.5777  | 30100     | 0.0           | -               |
| 1.5804  | 30150     | 0.0           | -               |
| 1.5830  | 30200     | 0.0           | -               |
| 1.5856  | 30250     | 0.0           | -               |
| 1.5882  | 30300     | 0.0           | -               |
| 1.5908  | 30350     | 0.0           | -               |
| 1.5935  | 30400     | 0.0           | -               |
| 1.5961  | 30450     | 0.0           | -               |
| 1.5987  | 30500     | 0.0           | -               |
| 1.6013  | 30550     | 0.0           | -               |
| 1.6039  | 30600     | 0.0           | -               |
| 1.6066  | 30650     | 0.0           | -               |
| 1.6092  | 30700     | 0.0           | -               |
| 1.6118  | 30750     | 0.0           | -               |
| 1.6144  | 30800     | 0.0           | -               |
| 1.6170  | 30850     | 0.0           | -               |
| 1.6197  | 30900     | 0.0           | -               |
| 1.6223  | 30950     | 0.0           | -               |
| 1.6249  | 31000     | 0.0           | -               |
| 1.6275  | 31050     | 0.0           | -               |
| 1.6301  | 31100     | 0.0           | -               |
| 1.6328  | 31150     | 0.0           | -               |
| 1.6354  | 31200     | 0.0           | -               |
| 1.6380  | 31250     | 0.0           | -               |
| 1.6406  | 31300     | 0.0           | -               |
| 1.6433  | 31350     | 0.0           | -               |
| 1.6459  | 31400     | 0.0           | -               |
| 1.6485  | 31450     | 0.0           | -               |
| 1.6511  | 31500     | 0.0           | -               |
| 1.6537  | 31550     | 0.0           | -               |
| 1.6564  | 31600     | 0.0           | -               |
| 1.6590  | 31650     | 0.0           | -               |
| 1.6616  | 31700     | 0.0           | -               |
| 1.6642  | 31750     | 0.0           | -               |
| 1.6668  | 31800     | 0.0           | -               |
| 1.6695  | 31850     | 0.0           | -               |
| 1.6721  | 31900     | 0.0           | -               |
| 1.6747  | 31950     | 0.0           | -               |
| 1.6773  | 32000     | 0.0           | -               |
| 1.6799  | 32050     | 0.0           | -               |
| 1.6826  | 32100     | 0.0           | -               |
| 1.6852  | 32150     | 0.0           | -               |
| 1.6878  | 32200     | 0.0           | -               |
| 1.6904  | 32250     | 0.0           | -               |
| 1.6930  | 32300     | 0.0           | -               |
| 1.6957  | 32350     | 0.0           | -               |
| 1.6983  | 32400     | 0.0           | -               |
| 1.7009  | 32450     | 0.0           | -               |
| 1.7035  | 32500     | 0.0           | -               |
| 1.7062  | 32550     | 0.0           | -               |
| 1.7088  | 32600     | 0.0           | -               |
| 1.7114  | 32650     | 0.0           | -               |
| 1.7140  | 32700     | 0.0           | -               |
| 1.7166  | 32750     | 0.0           | -               |
| 1.7193  | 32800     | 0.0           | -               |
| 1.7219  | 32850     | 0.0           | -               |
| 1.7245  | 32900     | 0.0           | -               |
| 1.7271  | 32950     | 0.0           | -               |
| 1.7297  | 33000     | 0.0           | -               |
| 1.7324  | 33050     | 0.0           | -               |
| 1.7350  | 33100     | 0.0           | -               |
| 1.7376  | 33150     | 0.0           | -               |
| 1.7402  | 33200     | 0.0           | -               |
| 1.7428  | 33250     | 0.0           | -               |
| 1.7455  | 33300     | 0.0           | -               |
| 1.7481  | 33350     | 0.0           | -               |
| 1.7507  | 33400     | 0.0           | -               |
| 1.7533  | 33450     | 0.0           | -               |
| 1.7559  | 33500     | 0.0           | -               |
| 1.7586  | 33550     | 0.0           | -               |
| 1.7612  | 33600     | 0.0           | -               |
| 1.7638  | 33650     | 0.0           | -               |
| 1.7664  | 33700     | 0.0           | -               |
| 1.7691  | 33750     | 0.0           | -               |
| 1.7717  | 33800     | 0.0           | -               |
| 1.7743  | 33850     | 0.0           | -               |
| 1.7769  | 33900     | 0.0           | -               |
| 1.7795  | 33950     | 0.0           | -               |
| 1.7822  | 34000     | 0.0           | -               |
| 1.7848  | 34050     | 0.0           | -               |
| 1.7874  | 34100     | 0.0           | -               |
| 1.7900  | 34150     | 0.0           | -               |
| 1.7926  | 34200     | 0.0           | -               |
| 1.7953  | 34250     | 0.0           | -               |
| 1.7979  | 34300     | 0.0           | -               |
| 1.8005  | 34350     | 0.0           | -               |
| 1.8031  | 34400     | 0.0           | -               |
| 1.8057  | 34450     | 0.0           | -               |
| 1.8084  | 34500     | 0.0           | -               |
| 1.8110  | 34550     | 0.0           | -               |
| 1.8136  | 34600     | 0.0           | -               |
| 1.8162  | 34650     | 0.0           | -               |
| 1.8188  | 34700     | 0.0           | -               |
| 1.8215  | 34750     | 0.0           | -               |
| 1.8241  | 34800     | 0.0           | -               |
| 1.8267  | 34850     | 0.0           | -               |
| 1.8293  | 34900     | 0.0           | -               |
| 1.8320  | 34950     | 0.0           | -               |
| 1.8346  | 35000     | 0.0           | -               |
| 1.8372  | 35050     | 0.0           | -               |
| 1.8398  | 35100     | 0.0           | -               |
| 1.8424  | 35150     | 0.0           | -               |
| 1.8451  | 35200     | 0.0           | -               |
| 1.8477  | 35250     | 0.0           | -               |
| 1.8503  | 35300     | 0.0           | -               |
| 1.8529  | 35350     | 0.0           | -               |
| 1.8555  | 35400     | 0.0           | -               |
| 1.8582  | 35450     | 0.0           | -               |
| 1.8608  | 35500     | 0.0           | -               |
| 1.8634  | 35550     | 0.0           | -               |
| 1.8660  | 35600     | 0.0           | -               |
| 1.8686  | 35650     | 0.0           | -               |
| 1.8713  | 35700     | 0.0           | -               |
| 1.8739  | 35750     | 0.0           | -               |
| 1.8765  | 35800     | 0.0           | -               |
| 1.8791  | 35850     | 0.0           | -               |
| 1.8817  | 35900     | 0.0           | -               |
| 1.8844  | 35950     | 0.0           | -               |
| 1.8870  | 36000     | 0.0           | -               |
| 1.8896  | 36050     | 0.0           | -               |
| 1.8922  | 36100     | 0.0           | -               |
| 1.8949  | 36150     | 0.0           | -               |
| 1.8975  | 36200     | 0.0           | -               |
| 1.9001  | 36250     | 0.0           | -               |
| 1.9027  | 36300     | 0.0           | -               |
| 1.9053  | 36350     | 0.0           | -               |
| 1.9080  | 36400     | 0.0           | -               |
| 1.9106  | 36450     | 0.0           | -               |
| 1.9132  | 36500     | 0.0           | -               |
| 1.9158  | 36550     | 0.0           | -               |
| 1.9184  | 36600     | 0.0           | -               |
| 1.9211  | 36650     | 0.0           | -               |
| 1.9237  | 36700     | 0.0           | -               |
| 1.9263  | 36750     | 0.0           | -               |
| 1.9289  | 36800     | 0.0           | -               |
| 1.9315  | 36850     | 0.0           | -               |
| 1.9342  | 36900     | 0.0           | -               |
| 1.9368  | 36950     | 0.0           | -               |
| 1.9394  | 37000     | 0.0           | -               |
| 1.9420  | 37050     | 0.0           | -               |
| 1.9446  | 37100     | 0.0           | -               |
| 1.9473  | 37150     | 0.0           | -               |
| 1.9499  | 37200     | 0.0           | -               |
| 1.9525  | 37250     | 0.0           | -               |
| 1.9551  | 37300     | 0.0           | -               |
| 1.9578  | 37350     | 0.0           | -               |
| 1.9604  | 37400     | 0.0           | -               |
| 1.9630  | 37450     | 0.0           | -               |
| 1.9656  | 37500     | 0.0           | -               |
| 1.9682  | 37550     | 0.0           | -               |
| 1.9709  | 37600     | 0.0           | -               |
| 1.9735  | 37650     | 0.0           | -               |
| 1.9761  | 37700     | 0.0           | -               |
| 1.9787  | 37750     | 0.0           | -               |
| 1.9813  | 37800     | 0.0           | -               |
| 1.9840  | 37850     | 0.0           | -               |
| 1.9866  | 37900     | 0.0           | -               |
| 1.9892  | 37950     | 0.0           | -               |
| 1.9918  | 38000     | 0.0           | -               |
| 1.9944  | 38050     | 0.0           | -               |
| 1.9971  | 38100     | 0.0           | -               |
| 1.9997  | 38150     | 0.0           | -               |
| 2.0     | 38156     | -             | 0.0438          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.0.0
- Datasets: 2.16.1
- Tokenizers: 0.15.0

## 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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setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/all-mpnet-base-v2 model-index region:us