setfit safetensors mpnet absa sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/all-mpnet-base-v2 base_model:finetune:sentence-transformers/all-mpnet-base-v2 model-index region:us
---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Needs Power and Mouse Cable to Plug in:Needs Power and Mouse Cable to Plug
    in back instead of side, In the way of operating a mouse in small area.
- text: wireless router via built-in wireless took no time:Connecting to my wireless
    router via built-in wireless took no time at all.
- text: The battery life is probably an:The battery life is probably an hour at best.
- text: and with free shipping and no tax:The 13" Macbook Pro just fits in my budget
    and with free shipping and no tax to CA this is the best price we can get for
    a great product.
- text: product is top quality.:The price was very good, and the product is top quality.
inference: false
model-index:
- name: SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.7788235294117647
      name: Accuracy
---

# SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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. In particular, this model is in charge of classifying aspect polarities.

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.

This model was trained within the context of a larger system for ABSA, which looks like so:

1. Use a spaCy model to select possible aspect span candidates.
2. Use a SetFit model to filter these possible aspect span candidates.
3. **Use this SetFit model to classify the filtered aspect span candidates.**

## 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
- **spaCy Model:** en_core_web_sm
- **SetFitABSA Aspect Model:** [setfit-absa-aspect](https://huggingface.co/setfit-absa-aspect)
- **SetFitABSA Polarity Model:** [marcelomoreno26/all-mpnet-base-v2-absa-polarity2](https://huggingface.co/marcelomoreno26/all-mpnet-base-v2-absa-polarity2)
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label    | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neutral  | <ul><li>'skip taking the cord with me because:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'The tech guy then said the:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'</li><li>'all dark, power light steady, hard:\xa0One night I turned the freaking thing off after using it, the next day I turn it on, no GUI, screen all dark, power light steady, hard drive light steady and not flashing as it usually does.'</li></ul>                                                                                                |
| positive | <ul><li>'of the good battery life.:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'is of high quality, has a:it is of high quality, has a killer GUI, is extremely stable, is highly expandable, is bundled with lots of very good applications, is easy to use, and is absolutely gorgeous.'</li><li>'has a killer GUI, is extremely:it is of high quality, has a killer GUI, is extremely stable, is highly expandable, is bundled with lots of very good applications, is easy to use, and is absolutely gorgeous.'</li></ul>                                                                                                                                       |
| negative | <ul><li>'then said the service center does not do:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'</li><li>'concern to the "sales" team, which is:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'</li><li>'on, no GUI, screen all:\xa0One night I turned the freaking thing off after using it, the next day I turn it on, no GUI, screen all dark, power light steady, hard drive light steady and not flashing as it usually does.'</li></ul> |
| conflict | <ul><li>'-No backlit keyboard, but not:-No backlit keyboard, but not an issue for me.'</li><li>"to replace the battery once, but:I did have to replace the battery once, but that was only a couple months ago and it's been working perfect ever since."</li><li>'Yes, they cost more, but:Yes, they cost more, but they more than make up for it in speed, construction quality, and longevity.'</li></ul>                                                                                                                                                                                                                                                                                                             |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7788   |

## 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 AbsaModel

# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
    "setfit-absa-aspect",
    "marcelomoreno26/all-mpnet-base-v2-absa-polarity2",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```

<!--
### 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   | 3   | 24.3447 | 80  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 235                   |
| neutral  | 127                   |
| positive | 271                   |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step  | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.3333 | 1     | 0.3749        | -               |
| 0.0030 | 50    | 0.3097        | -               |
| 0.0059 | 100   | 0.2214        | -               |
| 0.0089 | 150   | 0.2125        | -               |
| 0.0119 | 200   | 0.3202        | -               |
| 0.0148 | 250   | 0.1878        | -               |
| 0.0178 | 300   | 0.1208        | -               |
| 0.0208 | 350   | 0.2414        | -               |
| 0.0237 | 400   | 0.1961        | -               |
| 0.0267 | 450   | 0.0607        | -               |
| 0.0296 | 500   | 0.1103        | -               |
| 0.0326 | 550   | 0.1213        | -               |
| 0.0356 | 600   | 0.0972        | -               |
| 0.0385 | 650   | 0.0124        | -               |
| 0.0415 | 700   | 0.0151        | -               |
| 0.0445 | 750   | 0.1517        | -               |
| 0.0474 | 800   | 0.004         | -               |
| 0.0504 | 850   | 0.0204        | -               |
| 0.0534 | 900   | 0.0541        | -               |
| 0.0563 | 950   | 0.003         | -               |
| 0.0593 | 1000  | 0.0008        | -               |
| 0.0623 | 1050  | 0.0703        | -               |
| 0.0652 | 1100  | 0.0013        | -               |
| 0.0682 | 1150  | 0.0007        | -               |
| 0.0712 | 1200  | 0.0009        | -               |
| 0.0741 | 1250  | 0.0004        | -               |
| 0.0771 | 1300  | 0.0004        | -               |
| 0.0801 | 1350  | 0.0005        | -               |
| 0.0830 | 1400  | 0.0006        | -               |
| 0.0860 | 1450  | 0.0004        | -               |
| 0.0889 | 1500  | 0.0002        | -               |
| 0.0919 | 1550  | 0.0002        | -               |
| 0.0949 | 1600  | 0.0001        | -               |
| 0.0978 | 1650  | 0.0006        | -               |
| 0.1008 | 1700  | 0.0002        | -               |
| 0.1038 | 1750  | 0.0012        | -               |
| 0.1067 | 1800  | 0.0008        | -               |
| 0.1097 | 1850  | 0.0048        | -               |
| 0.1127 | 1900  | 0.0007        | -               |
| 0.1156 | 1950  | 0.0001        | -               |
| 0.1186 | 2000  | 0.0001        | -               |
| 0.1216 | 2050  | 0.0001        | -               |
| 0.1245 | 2100  | 0.0001        | -               |
| 0.1275 | 2150  | 0.0001        | -               |
| 0.1305 | 2200  | 0.0001        | -               |
| 0.1334 | 2250  | 0.0           | -               |
| 0.1364 | 2300  | 0.0001        | -               |
| 0.1394 | 2350  | 0.0002        | -               |
| 0.1423 | 2400  | 0.0           | -               |
| 0.1453 | 2450  | 0.0           | -               |
| 0.1482 | 2500  | 0.0589        | -               |
| 0.1512 | 2550  | 0.0036        | -               |
| 0.1542 | 2600  | 0.0013        | -               |
| 0.1571 | 2650  | 0.0           | -               |
| 0.1601 | 2700  | 0.0001        | -               |
| 0.1631 | 2750  | 0.0004        | -               |
| 0.1660 | 2800  | 0.0           | -               |
| 0.1690 | 2850  | 0.0002        | -               |
| 0.1720 | 2900  | 0.0096        | -               |
| 0.1749 | 2950  | 0.0           | -               |
| 0.1779 | 3000  | 0.0           | -               |
| 0.1809 | 3050  | 0.0001        | -               |
| 0.1838 | 3100  | 0.0           | -               |
| 0.1868 | 3150  | 0.0001        | -               |
| 0.1898 | 3200  | 0.0001        | -               |
| 0.1927 | 3250  | 0.0           | -               |
| 0.1957 | 3300  | 0.0           | -               |
| 0.1986 | 3350  | 0.0001        | -               |
| 0.2016 | 3400  | 0.0           | -               |
| 0.2046 | 3450  | 0.0002        | -               |
| 0.2075 | 3500  | 0.0           | -               |
| 0.2105 | 3550  | 0.0           | -               |
| 0.2135 | 3600  | 0.0001        | -               |
| 0.2164 | 3650  | 0.0           | -               |
| 0.2194 | 3700  | 0.0           | -               |
| 0.2224 | 3750  | 0.0001        | -               |
| 0.2253 | 3800  | 0.0           | -               |
| 0.2283 | 3850  | 0.0           | -               |
| 0.2313 | 3900  | 0.0           | -               |
| 0.2342 | 3950  | 0.0           | -               |
| 0.2372 | 4000  | 0.0           | -               |
| 0.2402 | 4050  | 0.0           | -               |
| 0.2431 | 4100  | 0.0           | -               |
| 0.2461 | 4150  | 0.0           | -               |
| 0.2491 | 4200  | 0.0           | -               |
| 0.2520 | 4250  | 0.0           | -               |
| 0.2550 | 4300  | 0.0           | -               |
| 0.2579 | 4350  | 0.0           | -               |
| 0.2609 | 4400  | 0.0           | -               |
| 0.2639 | 4450  | 0.0           | -               |
| 0.2668 | 4500  | 0.0           | -               |
| 0.2698 | 4550  | 0.0           | -               |
| 0.2728 | 4600  | 0.0           | -               |
| 0.2757 | 4650  | 0.0           | -               |
| 0.2787 | 4700  | 0.0           | -               |
| 0.2817 | 4750  | 0.0           | -               |
| 0.2846 | 4800  | 0.0           | -               |
| 0.2876 | 4850  | 0.0001        | -               |
| 0.2906 | 4900  | 0.0071        | -               |
| 0.2935 | 4950  | 0.1151        | -               |
| 0.2965 | 5000  | 0.0055        | -               |
| 0.2995 | 5050  | 0.0005        | -               |
| 0.3024 | 5100  | 0.0041        | -               |
| 0.3054 | 5150  | 0.0001        | -               |
| 0.3083 | 5200  | 0.0003        | -               |
| 0.3113 | 5250  | 0.0001        | -               |
| 0.3143 | 5300  | 0.0           | -               |
| 0.3172 | 5350  | 0.0001        | -               |
| 0.3202 | 5400  | 0.0           | -               |
| 0.3232 | 5450  | 0.0           | -               |
| 0.3261 | 5500  | 0.0           | -               |
| 0.3291 | 5550  | 0.0           | -               |
| 0.3321 | 5600  | 0.0           | -               |
| 0.3350 | 5650  | 0.0           | -               |
| 0.3380 | 5700  | 0.0           | -               |
| 0.3410 | 5750  | 0.0           | -               |
| 0.3439 | 5800  | 0.0           | -               |
| 0.3469 | 5850  | 0.0           | -               |
| 0.3499 | 5900  | 0.0           | -               |
| 0.3528 | 5950  | 0.0           | -               |
| 0.3558 | 6000  | 0.0           | -               |
| 0.3588 | 6050  | 0.0           | -               |
| 0.3617 | 6100  | 0.0           | -               |
| 0.3647 | 6150  | 0.0           | -               |
| 0.3676 | 6200  | 0.0           | -               |
| 0.3706 | 6250  | 0.0           | -               |
| 0.3736 | 6300  | 0.0           | -               |
| 0.3765 | 6350  | 0.0           | -               |
| 0.3795 | 6400  | 0.0           | -               |
| 0.3825 | 6450  | 0.0           | -               |
| 0.3854 | 6500  | 0.0           | -               |
| 0.3884 | 6550  | 0.0           | -               |
| 0.3914 | 6600  | 0.0           | -               |
| 0.3943 | 6650  | 0.0           | -               |
| 0.3973 | 6700  | 0.0           | -               |
| 0.4003 | 6750  | 0.0           | -               |
| 0.4032 | 6800  | 0.0           | -               |
| 0.4062 | 6850  | 0.0           | -               |
| 0.4092 | 6900  | 0.0           | -               |
| 0.4121 | 6950  | 0.0           | -               |
| 0.4151 | 7000  | 0.0           | -               |
| 0.4181 | 7050  | 0.0           | -               |
| 0.4210 | 7100  | 0.0           | -               |
| 0.4240 | 7150  | 0.0           | -               |
| 0.4269 | 7200  | 0.0           | -               |
| 0.4299 | 7250  | 0.0           | -               |
| 0.4329 | 7300  | 0.0           | -               |
| 0.4358 | 7350  | 0.0           | -               |
| 0.4388 | 7400  | 0.0           | -               |
| 0.4418 | 7450  | 0.0           | -               |
| 0.4447 | 7500  | 0.0           | -               |
| 0.4477 | 7550  | 0.0           | -               |
| 0.4507 | 7600  | 0.0           | -               |
| 0.4536 | 7650  | 0.0003        | -               |
| 0.4566 | 7700  | 0.0           | -               |
| 0.4596 | 7750  | 0.0           | -               |
| 0.4625 | 7800  | 0.0           | -               |
| 0.4655 | 7850  | 0.0           | -               |
| 0.4685 | 7900  | 0.0           | -               |
| 0.4714 | 7950  | 0.0           | -               |
| 0.4744 | 8000  | 0.0           | -               |
| 0.4773 | 8050  | 0.0           | -               |
| 0.4803 | 8100  | 0.0           | -               |
| 0.4833 | 8150  | 0.0           | -               |
| 0.4862 | 8200  | 0.0           | -               |
| 0.4892 | 8250  | 0.0           | -               |
| 0.4922 | 8300  | 0.0           | -               |
| 0.4951 | 8350  | 0.0           | -               |
| 0.4981 | 8400  | 0.0           | -               |
| 0.5011 | 8450  | 0.0           | -               |
| 0.5040 | 8500  | 0.0           | -               |
| 0.5070 | 8550  | 0.0           | -               |
| 0.5100 | 8600  | 0.0           | -               |
| 0.5129 | 8650  | 0.0           | -               |
| 0.5159 | 8700  | 0.0           | -               |
| 0.5189 | 8750  | 0.0           | -               |
| 0.5218 | 8800  | 0.0           | -               |
| 0.5248 | 8850  | 0.0           | -               |
| 0.5278 | 8900  | 0.0           | -               |
| 0.5307 | 8950  | 0.0           | -               |
| 0.5337 | 9000  | 0.0           | -               |
| 0.5366 | 9050  | 0.0           | -               |
| 0.5396 | 9100  | 0.0           | -               |
| 0.5426 | 9150  | 0.0           | -               |
| 0.5455 | 9200  | 0.0           | -               |
| 0.5485 | 9250  | 0.0           | -               |
| 0.5515 | 9300  | 0.0           | -               |
| 0.5544 | 9350  | 0.0           | -               |
| 0.5574 | 9400  | 0.0           | -               |
| 0.5604 | 9450  | 0.0           | -               |
| 0.5633 | 9500  | 0.0           | -               |
| 0.5663 | 9550  | 0.0           | -               |
| 0.5693 | 9600  | 0.0           | -               |
| 0.5722 | 9650  | 0.0           | -               |
| 0.5752 | 9700  | 0.0           | -               |
| 0.5782 | 9750  | 0.0           | -               |
| 0.5811 | 9800  | 0.0           | -               |
| 0.5841 | 9850  | 0.0           | -               |
| 0.5870 | 9900  | 0.0           | -               |
| 0.5900 | 9950  | 0.0           | -               |
| 0.5930 | 10000 | 0.0           | -               |
| 0.5959 | 10050 | 0.0           | -               |
| 0.5989 | 10100 | 0.0           | -               |
| 0.6019 | 10150 | 0.0           | -               |
| 0.6048 | 10200 | 0.0           | -               |
| 0.6078 | 10250 | 0.0           | -               |
| 0.6108 | 10300 | 0.0           | -               |
| 0.6137 | 10350 | 0.0           | -               |
| 0.6167 | 10400 | 0.0           | -               |
| 0.6197 | 10450 | 0.0           | -               |
| 0.6226 | 10500 | 0.0           | -               |
| 0.6256 | 10550 | 0.0           | -               |
| 0.6286 | 10600 | 0.0           | -               |
| 0.6315 | 10650 | 0.0           | -               |
| 0.6345 | 10700 | 0.0           | -               |
| 0.6375 | 10750 | 0.0           | -               |
| 0.6404 | 10800 | 0.0           | -               |
| 0.6434 | 10850 | 0.0           | -               |
| 0.6463 | 10900 | 0.0           | -               |
| 0.6493 | 10950 | 0.0           | -               |
| 0.6523 | 11000 | 0.0           | -               |
| 0.6552 | 11050 | 0.0           | -               |
| 0.6582 | 11100 | 0.0           | -               |
| 0.6612 | 11150 | 0.0           | -               |
| 0.6641 | 11200 | 0.0           | -               |
| 0.6671 | 11250 | 0.0           | -               |
| 0.6701 | 11300 | 0.0           | -               |
| 0.6730 | 11350 | 0.0           | -               |
| 0.6760 | 11400 | 0.0           | -               |
| 0.6790 | 11450 | 0.0           | -               |
| 0.6819 | 11500 | 0.0           | -               |
| 0.6849 | 11550 | 0.0           | -               |
| 0.6879 | 11600 | 0.0           | -               |
| 0.6908 | 11650 | 0.0           | -               |
| 0.6938 | 11700 | 0.0           | -               |
| 0.6968 | 11750 | 0.0           | -               |
| 0.6997 | 11800 | 0.0           | -               |
| 0.7027 | 11850 | 0.0           | -               |
| 0.7056 | 11900 | 0.0           | -               |
| 0.7086 | 11950 | 0.0           | -               |
| 0.7116 | 12000 | 0.0           | -               |
| 0.7145 | 12050 | 0.0           | -               |
| 0.7175 | 12100 | 0.0           | -               |
| 0.7205 | 12150 | 0.0           | -               |
| 0.7234 | 12200 | 0.0           | -               |
| 0.7264 | 12250 | 0.0           | -               |
| 0.7294 | 12300 | 0.0           | -               |
| 0.7323 | 12350 | 0.0           | -               |
| 0.7353 | 12400 | 0.0           | -               |
| 0.7383 | 12450 | 0.0           | -               |
| 0.7412 | 12500 | 0.0           | -               |
| 0.7442 | 12550 | 0.0           | -               |
| 0.7472 | 12600 | 0.0           | -               |
| 0.7501 | 12650 | 0.0           | -               |
| 0.7531 | 12700 | 0.0           | -               |
| 0.7560 | 12750 | 0.0           | -               |
| 0.7590 | 12800 | 0.0           | -               |
| 0.7620 | 12850 | 0.0           | -               |
| 0.7649 | 12900 | 0.0           | -               |
| 0.7679 | 12950 | 0.0           | -               |
| 0.7709 | 13000 | 0.0           | -               |
| 0.7738 | 13050 | 0.0           | -               |
| 0.7768 | 13100 | 0.0           | -               |
| 0.7798 | 13150 | 0.0           | -               |
| 0.7827 | 13200 | 0.0           | -               |
| 0.7857 | 13250 | 0.0           | -               |
| 0.7887 | 13300 | 0.0           | -               |
| 0.7916 | 13350 | 0.0           | -               |
| 0.7946 | 13400 | 0.0           | -               |
| 0.7976 | 13450 | 0.0           | -               |
| 0.8005 | 13500 | 0.0           | -               |
| 0.8035 | 13550 | 0.0           | -               |
| 0.8065 | 13600 | 0.0           | -               |
| 0.8094 | 13650 | 0.0           | -               |
| 0.8124 | 13700 | 0.0           | -               |
| 0.8153 | 13750 | 0.0           | -               |
| 0.8183 | 13800 | 0.0           | -               |
| 0.8213 | 13850 | 0.0           | -               |
| 0.8242 | 13900 | 0.0           | -               |
| 0.8272 | 13950 | 0.0           | -               |
| 0.8302 | 14000 | 0.0           | -               |
| 0.8331 | 14050 | 0.0           | -               |
| 0.8361 | 14100 | 0.0           | -               |
| 0.8391 | 14150 | 0.0           | -               |
| 0.8420 | 14200 | 0.0           | -               |
| 0.8450 | 14250 | 0.0           | -               |
| 0.8480 | 14300 | 0.0           | -               |
| 0.8509 | 14350 | 0.0           | -               |
| 0.8539 | 14400 | 0.0           | -               |
| 0.8569 | 14450 | 0.0           | -               |
| 0.8598 | 14500 | 0.0           | -               |
| 0.8628 | 14550 | 0.0           | -               |
| 0.8657 | 14600 | 0.0           | -               |
| 0.8687 | 14650 | 0.0           | -               |
| 0.8717 | 14700 | 0.0           | -               |
| 0.8746 | 14750 | 0.0           | -               |
| 0.8776 | 14800 | 0.0           | -               |
| 0.8806 | 14850 | 0.0           | -               |
| 0.8835 | 14900 | 0.0           | -               |
| 0.8865 | 14950 | 0.0           | -               |
| 0.8895 | 15000 | 0.0           | -               |
| 0.8924 | 15050 | 0.0           | -               |
| 0.8954 | 15100 | 0.0           | -               |
| 0.8984 | 15150 | 0.0           | -               |
| 0.9013 | 15200 | 0.0           | -               |
| 0.9043 | 15250 | 0.0           | -               |
| 0.9073 | 15300 | 0.0           | -               |
| 0.9102 | 15350 | 0.0           | -               |
| 0.9132 | 15400 | 0.0           | -               |
| 0.9162 | 15450 | 0.0           | -               |
| 0.9191 | 15500 | 0.0           | -               |
| 0.9221 | 15550 | 0.0           | -               |
| 0.9250 | 15600 | 0.0           | -               |
| 0.9280 | 15650 | 0.0           | -               |
| 0.9310 | 15700 | 0.0           | -               |
| 0.9339 | 15750 | 0.0           | -               |
| 0.9369 | 15800 | 0.0           | -               |
| 0.9399 | 15850 | 0.0           | -               |
| 0.9428 | 15900 | 0.0           | -               |
| 0.9458 | 15950 | 0.0           | -               |
| 0.9488 | 16000 | 0.0           | -               |
| 0.9517 | 16050 | 0.0           | -               |
| 0.9547 | 16100 | 0.0           | -               |
| 0.9577 | 16150 | 0.0           | -               |
| 0.9606 | 16200 | 0.0           | -               |
| 0.9636 | 16250 | 0.0           | -               |
| 0.9666 | 16300 | 0.0           | -               |
| 0.9695 | 16350 | 0.0           | -               |
| 0.9725 | 16400 | 0.0           | -               |
| 0.9755 | 16450 | 0.0           | -               |
| 0.9784 | 16500 | 0.0           | -               |
| 0.9814 | 16550 | 0.0           | -               |
| 0.9843 | 16600 | 0.0           | -               |
| 0.9873 | 16650 | 0.0           | -               |
| 0.9903 | 16700 | 0.0           | -               |
| 0.9932 | 16750 | 0.0           | -               |
| 0.9962 | 16800 | 0.0           | -               |
| 0.9992 | 16850 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1

## 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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