setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-MiniLM-L6-v2 base_model:finetune:sentence-transformers/paraphrase-MiniLM-L6-v2 model-index region:us
---
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
library_name: setfit
metrics:
- silhouette_score
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: We are awaiting payment for the project completed in June. Please confirm
    when this will be processed.
- text: Hello, Good morning, would you mind cancelling this rental car?
- text: 'Kindly book accommodation for Lindelani Mkhize as follows: Establishment:
    City Lodge Lynwood Date checked in : 04 October 2023 Time checked in: 19h00pm
    Date checked out: 06 October 2023 Time checked out: 07h00am'
- text: You've been selected for a free energy audit. Click here to schedule your
    appointment.
- text: 'Please can you provide with the invoices for my stays this month as follows:   1.
    Premier Splendid Inn Bayshore (07 Aug - 08 Aug)   2. Port Nolloth Beach Shack
    (14 Aug - 17 Aug)'
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: silhouette_score
      value: 0.6826105442176871
      name: Silhouette_Score
---

# SetFit with sentence-transformers/paraphrase-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 14 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)

## Evaluation

### Metrics
| Label   | Silhouette_Score |
|:--------|:-----------------|
| **all** | 0.6826           |

## 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("mann2107/BCMPIIRAB_MiniLM_HTTest")
# Run inference
preds = model("Hello, Good morning, would you mind cancelling this rental car?")
```

<!--
### 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.*
-->

<!--
## 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.*
-->

<!--
### 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   | 1   | 25.6577 | 136 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 24                    |
| 1     | 24                    |
| 2     | 24                    |
| 3     | 24                    |
| 4     | 24                    |
| 5     | 24                    |
| 6     | 24                    |
| 7     | 24                    |
| 8     | 24                    |
| 9     | 24                    |
| 10    | 24                    |
| 11    | 24                    |
| 12    | 24                    |
| 13    | 24                    |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- body_learning_rate: (3e-05, 3e-05)
- head_learning_rate: 3e-05
- loss: MultipleNegativesRankingLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step  | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1     | 2.5259        | -               |
| 0.0060 | 50    | 2.8997        | -               |
| 0.0119 | 100   | 2.8192        | -               |
| 0.0179 | 150   | 2.8803        | -               |
| 0.0238 | 200   | 2.635         | -               |
| 0.0298 | 250   | 2.5501        | -               |
| 0.0357 | 300   | 2.4468        | -               |
| 0.0417 | 350   | 2.1309        | -               |
| 0.0476 | 400   | 2.0439        | -               |
| 0.0536 | 450   | 1.9429        | -               |
| 0.0595 | 500   | 1.9344        | -               |
| 0.0655 | 550   | 1.8493        | -               |
| 0.0714 | 600   | 1.7907        | -               |
| 0.0774 | 650   | 1.7712        | -               |
| 0.0833 | 700   | 1.7349        | -               |
| 0.0893 | 750   | 1.7783        | -               |
| 0.0952 | 800   | 1.7022        | -               |
| 0.1012 | 850   | 1.6757        | -               |
| 0.1071 | 900   | 1.709         | -               |
| 0.1131 | 950   | 1.6231        | -               |
| 0.1190 | 1000  | 1.6647        | -               |
| 0.125  | 1050  | 1.7618        | -               |
| 0.1310 | 1100  | 1.652         | -               |
| 0.1369 | 1150  | 1.5564        | -               |
| 0.1429 | 1200  | 1.7067        | -               |
| 0.1488 | 1250  | 1.664         | -               |
| 0.1548 | 1300  | 1.7426        | -               |
| 0.1607 | 1350  | 1.6281        | -               |
| 0.1667 | 1400  | 1.6375        | -               |
| 0.1726 | 1450  | 1.6216        | -               |
| 0.1786 | 1500  | 1.5998        | -               |
| 0.1845 | 1550  | 1.4892        | -               |
| 0.1905 | 1600  | 1.556         | -               |
| 0.1964 | 1650  | 1.6657        | -               |
| 0.2024 | 1700  | 1.6113        | -               |
| 0.2083 | 1750  | 1.634         | -               |
| 0.2143 | 1800  | 1.6615        | -               |
| 0.2202 | 1850  | 1.5192        | -               |
| 0.2262 | 1900  | 1.5846        | -               |
| 0.2321 | 1950  | 1.5376        | -               |
| 0.2381 | 2000  | 1.6028        | -               |
| 0.2440 | 2050  | 1.5744        | -               |
| 0.25   | 2100  | 1.645         | -               |
| 0.2560 | 2150  | 1.5432        | -               |
| 0.2619 | 2200  | 1.5922        | -               |
| 0.2679 | 2250  | 1.612         | -               |
| 0.2738 | 2300  | 1.6553        | -               |
| 0.2798 | 2350  | 1.5797        | -               |
| 0.2857 | 2400  | 1.5249        | -               |
| 0.2917 | 2450  | 1.639         | -               |
| 0.2976 | 2500  | 1.7246        | -               |
| 0.3036 | 2550  | 1.6186        | -               |
| 0.3095 | 2600  | 1.537         | -               |
| 0.3155 | 2650  | 1.5701        | -               |
| 0.3214 | 2700  | 1.6095        | -               |
| 0.3274 | 2750  | 1.5344        | -               |
| 0.3333 | 2800  | 1.6029        | -               |
| 0.3393 | 2850  | 1.6141        | -               |
| 0.3452 | 2900  | 1.5655        | -               |
| 0.3512 | 2950  | 1.5892        | -               |
| 0.3571 | 3000  | 1.595         | -               |
| 0.3631 | 3050  | 1.5068        | -               |
| 0.3690 | 3100  | 1.5826        | -               |
| 0.375  | 3150  | 1.481         | -               |
| 0.3810 | 3200  | 1.6001        | -               |
| 0.3869 | 3250  | 1.4991        | -               |
| 0.3929 | 3300  | 1.605         | -               |
| 0.3988 | 3350  | 1.6154        | -               |
| 0.4048 | 3400  | 1.5516        | -               |
| 0.4107 | 3450  | 1.559         | -               |
| 0.4167 | 3500  | 1.559         | -               |
| 0.4226 | 3550  | 1.5725        | -               |
| 0.4286 | 3600  | 1.5719        | -               |
| 0.4345 | 3650  | 1.4918        | -               |
| 0.4405 | 3700  | 1.5816        | -               |
| 0.4464 | 3750  | 1.5017        | -               |
| 0.4524 | 3800  | 1.5093        | -               |
| 0.4583 | 3850  | 1.5705        | -               |
| 0.4643 | 3900  | 1.5584        | -               |
| 0.4702 | 3950  | 1.5328        | -               |
| 0.4762 | 4000  | 1.4932        | -               |
| 0.4821 | 4050  | 1.5907        | -               |
| 0.4881 | 4100  | 1.5339        | -               |
| 0.4940 | 4150  | 1.4954        | -               |
| 0.5    | 4200  | 1.5256        | -               |
| 0.5060 | 4250  | 1.5349        | -               |
| 0.5119 | 4300  | 1.5238        | -               |
| 0.5179 | 4350  | 1.5222        | -               |
| 0.5238 | 4400  | 1.6318        | -               |
| 0.5298 | 4450  | 1.5872        | -               |
| 0.5357 | 4500  | 1.4892        | -               |
| 0.5417 | 4550  | 1.5764        | -               |
| 0.5476 | 4600  | 1.6123        | -               |
| 0.5536 | 4650  | 1.4708        | -               |
| 0.5595 | 4700  | 1.5201        | -               |
| 0.5655 | 4750  | 1.4975        | -               |
| 0.5714 | 4800  | 1.5402        | -               |
| 0.5774 | 4850  | 1.5396        | -               |
| 0.5833 | 4900  | 1.5325        | -               |
| 0.5893 | 4950  | 1.5166        | -               |
| 0.5952 | 5000  | 1.5216        | -               |
| 0.6012 | 5050  | 1.5934        | -               |
| 0.6071 | 5100  | 1.5118        | -               |
| 0.6131 | 5150  | 1.6581        | -               |
| 0.6190 | 5200  | 1.4251        | -               |
| 0.625  | 5250  | 1.5259        | -               |
| 0.6310 | 5300  | 1.4854        | -               |
| 0.6369 | 5350  | 1.6242        | -               |
| 0.6429 | 5400  | 1.5234        | -               |
| 0.6488 | 5450  | 1.4594        | -               |
| 0.6548 | 5500  | 1.5513        | -               |
| 0.6607 | 5550  | 1.3946        | -               |
| 0.6667 | 5600  | 1.4795        | -               |
| 0.6726 | 5650  | 1.5203        | -               |
| 0.6786 | 5700  | 1.5137        | -               |
| 0.6845 | 5750  | 1.5305        | -               |
| 0.6905 | 5800  | 1.4958        | -               |
| 0.6964 | 5850  | 1.5028        | -               |
| 0.7024 | 5900  | 1.419         | -               |
| 0.7083 | 5950  | 1.5043        | -               |
| 0.7143 | 6000  | 1.4512        | -               |
| 0.7202 | 6050  | 1.5199        | -               |
| 0.7262 | 6100  | 1.5097        | -               |
| 0.7321 | 6150  | 1.4989        | -               |
| 0.7381 | 6200  | 1.4632        | -               |
| 0.7440 | 6250  | 1.4781        | -               |
| 0.75   | 6300  | 1.4592        | -               |
| 0.7560 | 6350  | 1.507         | -               |
| 0.7619 | 6400  | 1.5535        | -               |
| 0.7679 | 6450  | 1.3831        | -               |
| 0.7738 | 6500  | 1.572         | -               |
| 0.7798 | 6550  | 1.5461        | -               |
| 0.7857 | 6600  | 1.5142        | -               |
| 0.7917 | 6650  | 1.494         | -               |
| 0.7976 | 6700  | 1.5487        | -               |
| 0.8036 | 6750  | 1.4344        | -               |
| 0.8095 | 6800  | 1.5262        | -               |
| 0.8155 | 6850  | 1.4942        | -               |
| 0.8214 | 6900  | 1.54          | -               |
| 0.8274 | 6950  | 1.518         | -               |
| 0.8333 | 7000  | 1.5765        | -               |
| 0.8393 | 7050  | 1.5526        | -               |
| 0.8452 | 7100  | 1.5548        | -               |
| 0.8512 | 7150  | 1.3953        | -               |
| 0.8571 | 7200  | 1.5273        | -               |
| 0.8631 | 7250  | 1.4349        | -               |
| 0.8690 | 7300  | 1.4176        | -               |
| 0.875  | 7350  | 1.5242        | -               |
| 0.8810 | 7400  | 1.5263        | -               |
| 0.8869 | 7450  | 1.5435        | -               |
| 0.8929 | 7500  | 1.4882        | -               |
| 0.8988 | 7550  | 1.4965        | -               |
| 0.9048 | 7600  | 1.5185        | -               |
| 0.9107 | 7650  | 1.5739        | -               |
| 0.9167 | 7700  | 1.5821        | -               |
| 0.9226 | 7750  | 1.6197        | -               |
| 0.9286 | 7800  | 1.5154        | -               |
| 0.9345 | 7850  | 1.5844        | -               |
| 0.9405 | 7900  | 1.5242        | -               |
| 0.9464 | 7950  | 1.488         | -               |
| 0.9524 | 8000  | 1.5414        | -               |
| 0.9583 | 8050  | 1.4829        | -               |
| 0.9643 | 8100  | 1.5162        | -               |
| 0.9702 | 8150  | 1.4136        | -               |
| 0.9762 | 8200  | 1.36          | -               |
| 0.9821 | 8250  | 1.5511        | -               |
| 0.9881 | 8300  | 1.4908        | -               |
| 0.9940 | 8350  | 1.5312        | -               |
| 1.0    | 8400  | 1.5008        | -               |
| 1.0060 | 8450  | 1.4283        | -               |
| 1.0119 | 8500  | 1.5027        | -               |
| 1.0179 | 8550  | 1.48          | -               |
| 1.0238 | 8600  | 1.425         | -               |
| 1.0298 | 8650  | 1.5233        | -               |
| 1.0357 | 8700  | 1.4259        | -               |
| 1.0417 | 8750  | 1.4355        | -               |
| 1.0476 | 8800  | 1.5006        | -               |
| 1.0536 | 8850  | 1.511         | -               |
| 1.0595 | 8900  | 1.3043        | -               |
| 1.0655 | 8950  | 1.5039        | -               |
| 1.0714 | 9000  | 1.4909        | -               |
| 1.0774 | 9050  | 1.4493        | -               |
| 1.0833 | 9100  | 1.4877        | -               |
| 1.0893 | 9150  | 1.5232        | -               |
| 1.0952 | 9200  | 1.6282        | -               |
| 1.1012 | 9250  | 1.4438        | -               |
| 1.1071 | 9300  | 1.5234        | -               |
| 1.1131 | 9350  | 1.5368        | -               |
| 1.1190 | 9400  | 1.5029        | -               |
| 1.125  | 9450  | 1.4776        | -               |
| 1.1310 | 9500  | 1.4877        | -               |
| 1.1369 | 9550  | 1.4917        | -               |
| 1.1429 | 9600  | 1.4474        | -               |
| 1.1488 | 9650  | 1.3519        | -               |
| 1.1548 | 9700  | 1.5118        | -               |
| 1.1607 | 9750  | 1.5507        | -               |
| 1.1667 | 9800  | 1.4395        | -               |
| 1.1726 | 9850  | 1.4883        | -               |
| 1.1786 | 9900  | 1.4524        | -               |
| 1.1845 | 9950  | 1.4756        | -               |
| 1.1905 | 10000 | 1.5255        | -               |
| 1.1964 | 10050 | 1.4795        | -               |
| 1.2024 | 10100 | 1.5277        | -               |
| 1.2083 | 10150 | 1.477         | -               |
| 1.2143 | 10200 | 1.4438        | -               |
| 1.2202 | 10250 | 1.5517        | -               |
| 1.2262 | 10300 | 1.588         | -               |
| 1.2321 | 10350 | 1.5352        | -               |
| 1.2381 | 10400 | 1.3697        | -               |
| 1.2440 | 10450 | 1.4449        | -               |
| 1.25   | 10500 | 1.4473        | -               |
| 1.2560 | 10550 | 1.5566        | -               |
| 1.2619 | 10600 | 1.4502        | -               |
| 1.2679 | 10650 | 1.4821        | -               |
| 1.2738 | 10700 | 1.4296        | -               |
| 1.2798 | 10750 | 1.4801        | -               |
| 1.2857 | 10800 | 1.4542        | -               |
| 1.2917 | 10850 | 1.4258        | -               |
| 1.2976 | 10900 | 1.4142        | -               |
| 1.3036 | 10950 | 1.6023        | -               |
| 1.3095 | 11000 | 1.4291        | -               |
| 1.3155 | 11050 | 1.5386        | -               |
| 1.3214 | 11100 | 1.4433        | -               |
| 1.3274 | 11150 | 1.4218        | -               |
| 1.3333 | 11200 | 1.4345        | -               |
| 1.3393 | 11250 | 1.5321        | -               |
| 1.3452 | 11300 | 1.5001        | -               |
| 1.3512 | 11350 | 1.3381        | -               |
| 1.3571 | 11400 | 1.4819        | -               |
| 1.3631 | 11450 | 1.4676        | -               |
| 1.3690 | 11500 | 1.5056        | -               |
| 1.375  | 11550 | 1.5052        | -               |
| 1.3810 | 11600 | 1.5217        | -               |
| 1.3869 | 11650 | 1.391         | -               |
| 1.3929 | 11700 | 1.46          | -               |
| 1.3988 | 11750 | 1.5022        | -               |
| 1.4048 | 11800 | 1.4579        | -               |
| 1.4107 | 11850 | 1.5025        | -               |
| 1.4167 | 11900 | 1.5058        | -               |
| 1.4226 | 11950 | 1.5107        | -               |
| 1.4286 | 12000 | 1.5327        | -               |
| 1.4345 | 12050 | 1.4727        | -               |
| 1.4405 | 12100 | 1.4353        | -               |
| 1.4464 | 12150 | 1.42          | -               |
| 1.4524 | 12200 | 1.5349        | -               |
| 1.4583 | 12250 | 1.473         | -               |
| 1.4643 | 12300 | 1.5228        | -               |
| 1.4702 | 12350 | 1.498         | -               |
| 1.4762 | 12400 | 1.4321        | -               |
| 1.4821 | 12450 | 1.5058        | -               |
| 1.4881 | 12500 | 1.4601        | -               |
| 1.4940 | 12550 | 1.5346        | -               |
| 1.5    | 12600 | 1.5985        | -               |
| 1.5060 | 12650 | 1.4683        | -               |
| 1.5119 | 12700 | 1.5088        | -               |
| 1.5179 | 12750 | 1.5082        | -               |
| 1.5238 | 12800 | 1.5784        | -               |
| 1.5298 | 12850 | 1.5241        | -               |
| 1.5357 | 12900 | 1.434         | -               |
| 1.5417 | 12950 | 1.452         | -               |
| 1.5476 | 13000 | 1.4459        | -               |
| 1.5536 | 13050 | 1.4965        | -               |
| 1.5595 | 13100 | 1.5313        | -               |
| 1.5655 | 13150 | 1.4781        | -               |
| 1.5714 | 13200 | 1.5502        | -               |
| 1.5774 | 13250 | 1.4602        | -               |
| 1.5833 | 13300 | 1.4477        | -               |
| 1.5893 | 13350 | 1.4736        | -               |
| 1.5952 | 13400 | 1.5035        | -               |
| 1.6012 | 13450 | 1.4829        | -               |
| 1.6071 | 13500 | 1.4941        | -               |
| 1.6131 | 13550 | 1.5462        | -               |
| 1.6190 | 13600 | 1.4764        | -               |
| 1.625  | 13650 | 1.4838        | -               |
| 1.6310 | 13700 | 1.4264        | -               |
| 1.6369 | 13750 | 1.6312        | -               |
| 1.6429 | 13800 | 1.4323        | -               |
| 1.6488 | 13850 | 1.514         | -               |
| 1.6548 | 13900 | 1.3944        | -               |
| 1.6607 | 13950 | 1.4709        | -               |
| 1.6667 | 14000 | 1.4268        | -               |
| 1.6726 | 14050 | 1.5699        | -               |
| 1.6786 | 14100 | 1.5433        | -               |
| 1.6845 | 14150 | 1.431         | -               |
| 1.6905 | 14200 | 1.5421        | -               |
| 1.6964 | 14250 | 1.4854        | -               |
| 1.7024 | 14300 | 1.4341        | -               |
| 1.7083 | 14350 | 1.4321        | -               |
| 1.7143 | 14400 | 1.4284        | -               |
| 1.7202 | 14450 | 1.4725        | -               |
| 1.7262 | 14500 | 1.5744        | -               |
| 1.7321 | 14550 | 1.4892        | -               |
| 1.7381 | 14600 | 1.5357        | -               |
| 1.7440 | 14650 | 1.4536        | -               |
| 1.75   | 14700 | 1.4861        | -               |
| 1.7560 | 14750 | 1.5268        | -               |
| 1.7619 | 14800 | 1.4613        | -               |
| 1.7679 | 14850 | 1.4313        | -               |
| 1.7738 | 14900 | 1.4522        | -               |
| 1.7798 | 14950 | 1.4291        | -               |
| 1.7857 | 15000 | 1.5054        | -               |
| 1.7917 | 15050 | 1.495         | -               |
| 1.7976 | 15100 | 1.5352        | -               |
| 1.8036 | 15150 | 1.4803        | -               |
| 1.8095 | 15200 | 1.3922        | -               |
| 1.8155 | 15250 | 1.4879        | -               |
| 1.8214 | 15300 | 1.4752        | -               |
| 1.8274 | 15350 | 1.5102        | -               |
| 1.8333 | 15400 | 1.4474        | -               |
| 1.8393 | 15450 | 1.4939        | -               |
| 1.8452 | 15500 | 1.5216        | -               |
| 1.8512 | 15550 | 1.4656        | -               |
| 1.8571 | 15600 | 1.5171        | -               |
| 1.8631 | 15650 | 1.3437        | -               |
| 1.8690 | 15700 | 1.4875        | -               |
| 1.875  | 15750 | 1.4692        | -               |
| 1.8810 | 15800 | 1.4804        | -               |
| 1.8869 | 15850 | 1.4423        | -               |
| 1.8929 | 15900 | 1.4592        | -               |
| 1.8988 | 15950 | 1.5764        | -               |
| 1.9048 | 16000 | 1.4083        | -               |
| 1.9107 | 16050 | 1.4852        | -               |
| 1.9167 | 16100 | 1.5158        | -               |
| 1.9226 | 16150 | 1.4602        | -               |
| 1.9286 | 16200 | 1.4465        | -               |
| 1.9345 | 16250 | 1.412         | -               |
| 1.9405 | 16300 | 1.483         | -               |
| 1.9464 | 16350 | 1.5342        | -               |
| 1.9524 | 16400 | 1.3866        | -               |
| 1.9583 | 16450 | 1.4318        | -               |
| 1.9643 | 16500 | 1.6241        | -               |
| 1.9702 | 16550 | 1.5514        | -               |
| 1.9762 | 16600 | 1.46          | -               |
| 1.9821 | 16650 | 1.4069        | -               |
| 1.9881 | 16700 | 1.457         | -               |
| 1.9940 | 16750 | 1.4273        | -               |
| 2.0    | 16800 | 1.3673        | -               |
| 2.0060 | 16850 | 1.3753        | -               |
| 2.0119 | 16900 | 1.4279        | -               |
| 2.0179 | 16950 | 1.3897        | -               |
| 2.0238 | 17000 | 1.4659        | -               |
| 2.0298 | 17050 | 1.4494        | -               |
| 2.0357 | 17100 | 1.4533        | -               |
| 2.0417 | 17150 | 1.3735        | -               |
| 2.0476 | 17200 | 1.4232        | -               |
| 2.0536 | 17250 | 1.4229        | -               |
| 2.0595 | 17300 | 1.4597        | -               |
| 2.0655 | 17350 | 1.4825        | -               |
| 2.0714 | 17400 | 1.4661        | -               |
| 2.0774 | 17450 | 1.4332        | -               |
| 2.0833 | 17500 | 1.5895        | -               |
| 2.0893 | 17550 | 1.4824        | -               |
| 2.0952 | 17600 | 1.4472        | -               |
| 2.1012 | 17650 | 1.4001        | -               |
| 2.1071 | 17700 | 1.4638        | -               |
| 2.1131 | 17750 | 1.4651        | -               |
| 2.1190 | 17800 | 1.4711        | -               |
| 2.125  | 17850 | 1.4474        | -               |
| 2.1310 | 17900 | 1.4544        | -               |
| 2.1369 | 17950 | 1.3935        | -               |
| 2.1429 | 18000 | 1.4449        | -               |
| 2.1488 | 18050 | 1.4671        | -               |
| 2.1548 | 18100 | 1.4169        | -               |
| 2.1607 | 18150 | 1.5095        | -               |
| 2.1667 | 18200 | 1.4186        | -               |
| 2.1726 | 18250 | 1.4574        | -               |
| 2.1786 | 18300 | 1.4448        | -               |
| 2.1845 | 18350 | 1.5045        | -               |
| 2.1905 | 18400 | 1.4998        | -               |
| 2.1964 | 18450 | 1.3559        | -               |
| 2.2024 | 18500 | 1.4862        | -               |
| 2.2083 | 18550 | 1.4018        | -               |
| 2.2143 | 18600 | 1.4407        | -               |
| 2.2202 | 18650 | 1.5812        | -               |
| 2.2262 | 18700 | 1.4268        | -               |
| 2.2321 | 18750 | 1.4434        | -               |
| 2.2381 | 18800 | 1.5467        | -               |
| 2.2440 | 18850 | 1.4281        | -               |
| 2.25   | 18900 | 1.482         | -               |
| 2.2560 | 18950 | 1.5261        | -               |
| 2.2619 | 19000 | 1.4152        | -               |
| 2.2679 | 19050 | 1.5267        | -               |
| 2.2738 | 19100 | 1.4237        | -               |
| 2.2798 | 19150 | 1.5455        | -               |
| 2.2857 | 19200 | 1.4679        | -               |
| 2.2917 | 19250 | 1.3398        | -               |
| 2.2976 | 19300 | 1.4697        | -               |
| 2.3036 | 19350 | 1.4176        | -               |
| 2.3095 | 19400 | 1.4661        | -               |
| 2.3155 | 19450 | 1.4397        | -               |
| 2.3214 | 19500 | 1.5095        | -               |
| 2.3274 | 19550 | 1.4873        | -               |
| 2.3333 | 19600 | 1.4312        | -               |
| 2.3393 | 19650 | 1.441         | -               |
| 2.3452 | 19700 | 1.4341        | -               |
| 2.3512 | 19750 | 1.4229        | -               |
| 2.3571 | 19800 | 1.4917        | -               |
| 2.3631 | 19850 | 1.4397        | -               |
| 2.3690 | 19900 | 1.4027        | -               |
| 2.375  | 19950 | 1.5022        | -               |
| 2.3810 | 20000 | 1.441         | -               |
| 2.3869 | 20050 | 1.4392        | -               |
| 2.3929 | 20100 | 1.4454        | -               |
| 2.3988 | 20150 | 1.4886        | -               |
| 2.4048 | 20200 | 1.4776        | -               |
| 2.4107 | 20250 | 1.3946        | -               |
| 2.4167 | 20300 | 1.5492        | -               |
| 2.4226 | 20350 | 1.534         | -               |
| 2.4286 | 20400 | 1.4011        | -               |
| 2.4345 | 20450 | 1.5276        | -               |
| 2.4405 | 20500 | 1.4633        | -               |
| 2.4464 | 20550 | 1.4446        | -               |
| 2.4524 | 20600 | 1.5005        | -               |
| 2.4583 | 20650 | 1.4818        | -               |
| 2.4643 | 20700 | 1.4319        | -               |
| 2.4702 | 20750 | 1.4406        | -               |
| 2.4762 | 20800 | 1.4496        | -               |
| 2.4821 | 20850 | 1.4963        | -               |
| 2.4881 | 20900 | 1.4731        | -               |
| 2.4940 | 20950 | 1.4536        | -               |
| 2.5    | 21000 | 1.5153        | -               |
| 2.5060 | 21050 | 1.5522        | -               |
| 2.5119 | 21100 | 1.3759        | -               |
| 2.5179 | 21150 | 1.4285        | -               |
| 2.5238 | 21200 | 1.4162        | -               |
| 2.5298 | 21250 | 1.4383        | -               |
| 2.5357 | 21300 | 1.4408        | -               |
| 2.5417 | 21350 | 1.4009        | -               |
| 2.5476 | 21400 | 1.4589        | -               |
| 2.5536 | 21450 | 1.4478        | -               |
| 2.5595 | 21500 | 1.4876        | -               |
| 2.5655 | 21550 | 1.4206        | -               |
| 2.5714 | 21600 | 1.4927        | -               |
| 2.5774 | 21650 | 1.5047        | -               |
| 2.5833 | 21700 | 1.3988        | -               |
| 2.5893 | 21750 | 1.4714        | -               |
| 2.5952 | 21800 | 1.3605        | -               |
| 2.6012 | 21850 | 1.5635        | -               |
| 2.6071 | 21900 | 1.4678        | -               |
| 2.6131 | 21950 | 1.4618        | -               |
| 2.6190 | 22000 | 1.4407        | -               |
| 2.625  | 22050 | 1.5451        | -               |
| 2.6310 | 22100 | 1.4844        | -               |
| 2.6369 | 22150 | 1.4088        | -               |
| 2.6429 | 22200 | 1.5056        | -               |
| 2.6488 | 22250 | 1.4678        | -               |
| 2.6548 | 22300 | 1.4262        | -               |
| 2.6607 | 22350 | 1.4492        | -               |
| 2.6667 | 22400 | 1.4463        | -               |
| 2.6726 | 22450 | 1.3851        | -               |
| 2.6786 | 22500 | 1.513         | -               |
| 2.6845 | 22550 | 1.45          | -               |
| 2.6905 | 22600 | 1.4382        | -               |
| 2.6964 | 22650 | 1.4637        | -               |
| 2.7024 | 22700 | 1.4487        | -               |
| 2.7083 | 22750 | 1.4507        | -               |
| 2.7143 | 22800 | 1.5065        | -               |
| 2.7202 | 22850 | 1.4116        | -               |
| 2.7262 | 22900 | 1.479         | -               |
| 2.7321 | 22950 | 1.444         | -               |
| 2.7381 | 23000 | 1.4056        | -               |
| 2.7440 | 23050 | 1.3913        | -               |
| 2.75   | 23100 | 1.5108        | -               |
| 2.7560 | 23150 | 1.4092        | -               |
| 2.7619 | 23200 | 1.4341        | -               |
| 2.7679 | 23250 | 1.4274        | -               |
| 2.7738 | 23300 | 1.4748        | -               |
| 2.7798 | 23350 | 1.3819        | -               |
| 2.7857 | 23400 | 1.5012        | -               |
| 2.7917 | 23450 | 1.3594        | -               |
| 2.7976 | 23500 | 1.4708        | -               |
| 2.8036 | 23550 | 1.4425        | -               |
| 2.8095 | 23600 | 1.3566        | -               |
| 2.8155 | 23650 | 1.456         | -               |
| 2.8214 | 23700 | 1.5937        | -               |
| 2.8274 | 23750 | 1.3835        | -               |
| 2.8333 | 23800 | 1.4137        | -               |
| 2.8393 | 23850 | 1.3861        | -               |
| 2.8452 | 23900 | 1.4249        | -               |
| 2.8512 | 23950 | 1.3599        | -               |
| 2.8571 | 24000 | 1.4789        | -               |
| 2.8631 | 24050 | 1.4527        | -               |
| 2.8690 | 24100 | 1.4406        | -               |
| 2.875  | 24150 | 1.4301        | -               |
| 2.8810 | 24200 | 1.4059        | -               |
| 2.8869 | 24250 | 1.5052        | -               |
| 2.8929 | 24300 | 1.4429        | -               |
| 2.8988 | 24350 | 1.5183        | -               |
| 2.9048 | 24400 | 1.4288        | -               |
| 2.9107 | 24450 | 1.4673        | -               |
| 2.9167 | 24500 | 1.4582        | -               |
| 2.9226 | 24550 | 1.4792        | -               |
| 2.9286 | 24600 | 1.4598        | -               |
| 2.9345 | 24650 | 1.4785        | -               |
| 2.9405 | 24700 | 1.4259        | -               |
| 2.9464 | 24750 | 1.4877        | -               |
| 2.9524 | 24800 | 1.5162        | -               |
| 2.9583 | 24850 | 1.4854        | -               |
| 2.9643 | 24900 | 1.3679        | -               |
| 2.9702 | 24950 | 1.3985        | -               |
| 2.9762 | 25000 | 1.421         | -               |
| 2.9821 | 25050 | 1.5048        | -               |
| 2.9881 | 25100 | 1.4618        | -               |
| 2.9940 | 25150 | 1.5061        | -               |
| 3.0    | 25200 | 1.3634        | -               |

### Framework Versions
- Python: 3.12.0
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.0+cpu
- Datasets: 3.0.2
- Tokenizers: 0.20.1

## 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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