setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:BAAI/bge-small-en-v1.5 base_model:finetune:BAAI/bge-small-en-v1.5 model-index region:us
---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: A Black man, Floyd died in police custody May 25 after a Minneapolis cop kneeled
    on his neck for more than eight minutes.
- text: 'Now Modi has made international headlines for yet another similarity: He’s
    constructing a massive wall … but unlike Trump’s goal of keeping immigrants out,
    Modi’s wall was built to hide the country’s poverty from the gold-plated American
    president.'
- text: Billionaire Democrat presidential hopeful Mike Bloomberg is a staunch proponent
    of gun control for America with one caveat–he gets to spend his days surrounded
    by good guys with guns to keep him safe.
- text: The number of women behind the camera on Hollywood movies jumped to record
    levels in 2019, with 12 directing top-grossing films including “Frozen II,” “Captain
    Marvel” and “Hustlers,” two studies showed on Thursday.
- text: The hearing comes a day after the Democrat-led House held a hearing to discuss
    the alleged threat of white nationalist terrorism to the country.
inference: true
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.7010135135135135
      name: Accuracy
    - type: precision
      value: 0.7024038067625294
      name: Precision
    - type: recall
      value: 0.7010135135135135
      name: Recall
    - type: f1
      value: 0.7015820127453647
      name: F1
---

# SetFit with BAAI/bge-small-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
|:-------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| left   | <ul><li>'Tennessee has an annual sales tax-free holiday weekend that\xa0begins\xa0on the last Friday of July.\xa0'</li><li>'In what could be construed as an act of treason,\xa0President Trump recently ordered such\xa0paramilitary groups and right-wing thugs\xa0to take up arms and to threaten Democratic-led state governments such as Michigan\'s in order to force them to "reopen" their state.'</li><li>'Trump, not surprisingly, used the speech as an opportunity to attack former President Barack Obama, claiming that he did nothing to promote criminal justice reform when he was in office.\xa0'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                            |
| right  | <ul><li>'In the Joe Biden-Bernie Sanders “Unity” platform, Democrats are vowing to provide free, American taxpayer-funded health care to illegal aliens who are able to enroll in former President Obama’s Deferred Action for Childhood Arrivals (DACA) program.'</li><li>'The new numbers from Gallup are an unwelcome sight for Democrats after kicking off the week with a disaster caucus in Iowa who and simultaneously anticipating a Trump acquittal in the Senate. Trump will also now have the opportunity to shine in his newfound approval in Tuesday night’s address to the nation while Democrats are in disarray.'</li><li>'Though Trump has successfully increased wages by four percent over the last 12 months for America’s blue collar and working class by decreasing foreign competition through a crackdown on illegal immigration, experts have warned that those wage hikes will not continue heading into the 2020 election should current illegal immigration levels keep rising at record levels.'</li></ul> |
| center | <ul><li>'LeBron James shares thoughts on his Los Angeles house getting vandalized pic twitter com 4RFLK42xhu'</li><li>'O’Rourke, a native of the U.S.-Mexican border town El Paso, has blasted Trump’s use of tariffs as a “huge mistake” and has vowed to suspend them on his first day in office.'</li><li>'Here are a few people we will be reminding you about in every article that pertains to a film they re tied to '</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.7010   | 0.7024    | 0.7010 | 0.7016 |

## 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("JordanTallon/Unifeed")
# Run inference
preds = model("A Black man, Floyd died in police custody May 25 after a Minneapolis cop kneeled on his neck for more than eight minutes.")
```

<!--
### 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   | 9   | 32.9560 | 90  |

| Label  | Training Sample Count |
|:-------|:----------------------|
| center | 777                   |
| left   | 780                   |
| right  | 808                   |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (200, 200)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- 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: True
- warmup_proportion: 0.1
- seed: 326
- run_name: unifeed_bias_training
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch    | Step     | Training Loss | Validation Loss |
|:--------:|:--------:|:-------------:|:---------------:|
| 0.0002   | 1        | 0.2486        | -               |
| 1.0      | 4878     | 0.0092        | 0.308           |
| 2.0      | 9756     | 0.0004        | 0.3228          |
| 3.0      | 14634    | 0.0002        | 0.3326          |
| 4.0      | 19512    | 0.0002        | 0.3191          |
| 5.0      | 24390    | 0.0001        | 0.3279          |
| 6.0      | 29268    | 0.0001        | 0.3384          |
| 7.0      | 34146    | 0.0001        | 0.3311          |
| 8.0      | 39024    | 0.0001        | 0.3316          |
| 0.0068   | 1        | 0.0007        | -               |
| 1.0      | 148      | 0.0006        | 0.3042          |
| 2.0      | 296      | 0.0006        | 0.3352          |
| 3.0      | 444      | 0.0382        | 0.3059          |
| 4.0      | 592      | 0.0022        | 0.3055          |
| 5.0      | 740      | 0.0044        | 0.3034          |
| 6.0      | 888      | 0.0006        | 0.3185          |
| 7.0      | 1036     | 0.0005        | 0.3066          |
| 8.0      | 1184     | 0.0008        | 0.3196          |
| 9.0      | 1332     | 0.0004        | 0.326           |
| 10.0     | 1480     | 0.0004        | 0.352           |
| 11.0     | 1628     | 0.0005        | 0.3122          |
| 12.0     | 1776     | 0.0003        | 0.3268          |
| 13.0     | 1924     | 0.0004        | 0.2928          |
| 14.0     | 2072     | 0.0004        | 0.3148          |
| 15.0     | 2220     | 0.0003        | 0.3153          |
| 16.0     | 2368     | 0.0004        | 0.3385          |
| 17.0     | 2516     | 0.0004        | 0.3107          |
| 18.0     | 2664     | 0.0004        | 0.3225          |
| 19.0     | 2812     | 0.0003        | 0.3073          |
| 20.0     | 2960     | 0.0003        | 0.316           |
| 21.0     | 3108     | 0.0003        | 0.3053          |
| 22.0     | 3256     | 0.0004        | 0.3227          |
| 23.0     | 3404     | 0.0004        | 0.3099          |
| 24.0     | 3552     | 0.0003        | 0.3043          |
| 25.0     | 3700     | 0.0003        | 0.3316          |
| 0.0034   | 1        | 0.0004        | -               |
| 1.0      | 296      | 0.0003        | 0.3321          |
| 2.0      | 592      | 0.0016        | 0.3202          |
| 3.0      | 888      | 0.0005        | 0.3376          |
| 4.0      | 1184     | 0.0004        | 0.3167          |
| 5.0      | 1480     | 0.0003        | 0.3342          |
| 6.0      | 1776     | 0.0003        | 0.3183          |
| 7.0      | 2072     | 0.0003        | 0.3086          |
| 8.0      | 2368     | 0.0003        | 0.312           |
| 9.0      | 2664     | 0.0003        | 0.3169          |
| 10.0     | 2960     | 0.0003        | 0.3317          |
| 11.0     | 3256     | 0.0004        | 0.3126          |
| 12.0     | 3552     | 0.0003        | 0.3003          |
| 13.0     | 3848     | 0.0003        | 0.3119          |
| 14.0     | 4144     | 0.0003        | 0.316           |
| 15.0     | 4440     | 0.0002        | 0.3183          |
| 16.0     | 4736     | 0.0003        | 0.313           |
| 17.0     | 5032     | 0.0003        | 0.3187          |
| 18.0     | 5328     | 0.0002        | 0.3295          |
| 19.0     | 5624     | 0.0002        | 0.3487          |
| 20.0     | 5920     | 0.0003        | 0.3458          |
| 21.0     | 6216     | 0.0002        | 0.331           |
| 22.0     | 6512     | 0.0002        | 0.3499          |
| 23.0     | 6808     | 0.0003        | 0.3296          |
| 24.0     | 7104     | 0.0003        | 0.3097          |
| 25.0     | 7400     | 0.0003        | 0.3197          |
| 0.0068   | 1        | 0.0003        | -               |
| 1.0      | 148      | 0.0003        | 0.3219          |
| 2.0      | 296      | 0.0003        | 0.3185          |
| 3.0      | 444      | 0.0003        | 0.3114          |
| 4.0      | 592      | 0.0003        | 0.2989          |
| 5.0      | 740      | 0.0003        | 0.335           |
| 6.0      | 888      | 0.0004        | 0.3132          |
| 7.0      | 1036     | 0.0003        | 0.3264          |
| 8.0      | 1184     | 0.0004        | 0.3461          |
| 9.0      | 1332     | 0.0002        | 0.3185          |
| 10.0     | 1480     | 0.0002        | 0.3336          |
| 11.0     | 1628     | 0.0003        | 0.3282          |
| 12.0     | 1776     | 0.0003        | 0.3206          |
| 13.0     | 1924     | 0.0002        | 0.3303          |
| 14.0     | 2072     | 0.0002        | 0.3362          |
| 15.0     | 2220     | 0.0002        | 0.3382          |
| 16.0     | 2368     | 0.0002        | 0.3241          |
| 17.0     | 2516     | 0.0002        | 0.3303          |
| 18.0     | 2664     | 0.0002        | 0.3301          |
| 19.0     | 2812     | 0.0002        | 0.319           |
| 20.0     | 2960     | 0.0002        | 0.3304          |
| 21.0     | 3108     | 0.0002        | 0.3379          |
| 22.0     | 3256     | 0.0002        | 0.3424          |
| 23.0     | 3404     | 0.0002        | 0.3273          |
| 24.0     | 3552     | 0.0002        | 0.3213          |
| 25.0     | 3700     | 0.0002        | 0.3191          |
| 0.0068   | 1        | 0.0003        | -               |
| 1.0      | 148      | 0.0003        | 0.3245          |
| 2.0      | 296      | 0.0002        | 0.3148          |
| 3.0      | 444      | 0.0002        | 0.3174          |
| 4.0      | 592      | 0.0003        | 0.3242          |
| 5.0      | 740      | 0.0003        | 0.3352          |
| 6.0      | 888      | 0.0003        | 0.3112          |
| 7.0      | 1036     | 0.0003        | 0.3204          |
| 8.0      | 1184     | 0.0003        | 0.3734          |
| 9.0      | 1332     | 0.0002        | 0.3383          |
| 10.0     | 1480     | 0.0003        | 0.3272          |
| 11.0     | 1628     | 0.0002        | 0.3106          |
| 12.0     | 1776     | 0.0003        | 0.3307          |
| 13.0     | 1924     | 0.0003        | 0.3359          |
| 14.0     | 2072     | 0.0002        | 0.3264          |
| 15.0     | 2220     | 0.0002        | 0.3254          |
| 16.0     | 2368     | 0.0002        | 0.3349          |
| 17.0     | 2516     | 0.0132        | 0.3399          |
| 18.0     | 2664     | 0.0002        | 0.343           |
| 19.0     | 2812     | 0.0002        | 0.3306          |
| 20.0     | 2960     | 0.0002        | 0.3472          |
| 21.0     | 3108     | 0.0002        | 0.3234          |
| 22.0     | 3256     | 0.002         | 0.3281          |
| 23.0     | 3404     | 0.0002        | 0.3289          |
| 24.0     | 3552     | 0.0002        | 0.2974          |
| 25.0     | 3700     | 0.0002        | 0.3153          |
| 26.0     | 3848     | 0.0002        | 0.3273          |
| 27.0     | 3996     | 0.0002        | 0.313           |
| 28.0     | 4144     | 0.0002        | 0.3303          |
| 29.0     | 4292     | 0.0002        | 0.3106          |
| 30.0     | 4440     | 0.0002        | 0.3155          |
| 31.0     | 4588     | 0.0002        | 0.3553          |
| 32.0     | 4736     | 0.0002        | 0.3039          |
| 33.0     | 4884     | 0.0001        | 0.3133          |
| 34.0     | 5032     | 0.0002        | 0.3323          |
| 35.0     | 5180     | 0.0002        | 0.3264          |
| 36.0     | 5328     | 0.0002        | 0.3133          |
| 37.0     | 5476     | 0.0002        | 0.3308          |
| 38.0     | 5624     | 0.0002        | 0.3137          |
| 39.0     | 5772     | 0.0002        | 0.3062          |
| 40.0     | 5920     | 0.0002        | 0.3438          |
| 41.0     | 6068     | 0.0002        | 0.3426          |
| 42.0     | 6216     | 0.0002        | 0.326           |
| 43.0     | 6364     | 0.0002        | 0.322           |
| 44.0     | 6512     | 0.0002        | 0.3202          |
| 45.0     | 6660     | 0.0002        | 0.3253          |
| 46.0     | 6808     | 0.0002        | 0.3272          |
| 47.0     | 6956     | 0.0002        | 0.3258          |
| 48.0     | 7104     | 0.0002        | 0.3252          |
| 49.0     | 7252     | 0.0002        | 0.3233          |
| 50.0     | 7400     | 0.0002        | 0.3234          |
| 0.0135   | 1        | 0.0002        | -               |
| 1.0      | 74       | 0.0002        | -               |
| 0.0068   | 1        | 0.0002        | -               |
| 1.0      | 148      | 0.0002        | 0.3036          |
| 2.0      | 296      | 0.0002        | 0.3555          |
| 3.0      | 444      | 0.0002        | 0.3331          |
| 4.0      | 592      | 0.0002        | 0.3086          |
| 5.0      | 740      | 0.0002        | 0.3036          |
| 6.0      | 888      | 0.0002        | 0.3217          |
| 7.0      | 1036     | 0.0002        | 0.3416          |
| 8.0      | 1184     | 0.0002        | 0.3309          |
| 9.0      | 1332     | 0.0002        | 0.3424          |
| 10.0     | 1480     | 0.0003        | 0.3655          |
| 11.0     | 1628     | 0.0002        | 0.3042          |
| 12.0     | 1776     | 0.0019        | 0.326           |
| 13.0     | 1924     | 0.0002        | 0.3161          |
| 14.0     | 2072     | 0.0002        | 0.3286          |
| 15.0     | 2220     | 0.0002        | 0.3563          |
| 16.0     | 2368     | 0.0002        | 0.326           |
| 17.0     | 2516     | 0.0002        | 0.3114          |
| 18.0     | 2664     | 0.0002        | 0.3366          |
| 19.0     | 2812     | 0.0002        | 0.329           |
| 20.0     | 2960     | 0.0002        | 0.3217          |
| 21.0     | 3108     | 0.0002        | 0.325           |
| 22.0     | 3256     | 0.0002        | 0.3243          |
| 23.0     | 3404     | 0.0002        | 0.3341          |
| 24.0     | 3552     | 0.0002        | 0.3237          |
| 25.0     | 3700     | 0.0002        | 0.3433          |
| 26.0     | 3848     | 0.0002        | 0.3196          |
| 27.0     | 3996     | 0.0001        | 0.3372          |
| 28.0     | 4144     | 0.0001        | 0.3191          |
| 29.0     | 4292     | 0.0001        | 0.328           |
| 30.0     | 4440     | 0.0002        | 0.3416          |
| 31.0     | 4588     | 0.0002        | 0.3132          |
| 32.0     | 4736     | 0.0002        | 0.3429          |
| 33.0     | 4884     | 0.0002        | 0.336           |
| 34.0     | 5032     | 0.0002        | 0.3507          |
| 35.0     | 5180     | 0.0001        | 0.3483          |
| 36.0     | 5328     | 0.0002        | 0.3325          |
| 37.0     | 5476     | 0.0001        | 0.3406          |
| 38.0     | 5624     | 0.0003        | 0.3538          |
| 39.0     | 5772     | 0.0002        | 0.3422          |
| 40.0     | 5920     | 0.0002        | 0.3359          |
| 41.0     | 6068     | 0.0002        | 0.3252          |
| 42.0     | 6216     | 0.0002        | 0.326           |
| 43.0     | 6364     | 0.0002        | 0.3613          |
| 44.0     | 6512     | 0.0001        | 0.332           |
| 45.0     | 6660     | 0.0002        | 0.3295          |
| 46.0     | 6808     | 0.0002        | 0.3265          |
| 47.0     | 6956     | 0.0002        | 0.2982          |
| 48.0     | 7104     | 0.0002        | 0.3017          |
| 49.0     | 7252     | 0.0001        | 0.309           |
| 50.0     | 7400     | 0.0001        | 0.3199          |
| 51.0     | 7548     | 0.0001        | 0.325           |
| 52.0     | 7696     | 0.0002        | 0.3222          |
| 53.0     | 7844     | 0.0001        | 0.3189          |
| 54.0     | 7992     | 0.0001        | 0.3329          |
| 55.0     | 8140     | 0.0001        | 0.3272          |
| 56.0     | 8288     | 0.0001        | 0.3292          |
| 57.0     | 8436     | 0.0001        | 0.3283          |
| 58.0     | 8584     | 0.0001        | 0.3301          |
| 59.0     | 8732     | 0.0001        | 0.3334          |
| 60.0     | 8880     | 0.0001        | 0.3144          |
| 61.0     | 9028     | 0.0002        | 0.3487          |
| 62.0     | 9176     | 0.0002        | 0.3602          |
| **63.0** | **9324** | **0.0001**    | **0.3056**      |
| 64.0     | 9472     | 0.0001        | 0.3415          |
| 65.0     | 9620     | 0.0002        | 0.3299          |
| 66.0     | 9768     | 0.0001        | 0.3254          |
| 67.0     | 9916     | 0.0001        | 0.3396          |
| 68.0     | 10064    | 0.0001        | 0.3501          |
| 69.0     | 10212    | 0.0001        | 0.3275          |
| 70.0     | 10360    | 0.0001        | 0.34            |
| 71.0     | 10508    | 0.0001        | 0.3351          |
| 72.0     | 10656    | 0.0001        | 0.3367          |
| 73.0     | 10804    | 0.0001        | 0.3548          |
| 74.0     | 10952    | 0.0001        | 0.33            |
| 75.0     | 11100    | 0.0001        | 0.3259          |
| 76.0     | 11248    | 0.0002        | 0.3283          |
| 77.0     | 11396    | 0.0001        | 0.3214          |
| 78.0     | 11544    | 0.0001        | 0.324           |
| 79.0     | 11692    | 0.0001        | 0.3247          |
| 80.0     | 11840    | 0.0001        | 0.3347          |
| 81.0     | 11988    | 0.0001        | 0.3292          |
| 82.0     | 12136    | 0.0002        | 0.3568          |
| 83.0     | 12284    | 0.0001        | 0.324           |
| 84.0     | 12432    | 0.0001        | 0.3245          |
| 85.0     | 12580    | 0.0001        | 0.3368          |
| 86.0     | 12728    | 0.0001        | 0.3372          |
| 87.0     | 12876    | 0.0001        | 0.3432          |
| 88.0     | 13024    | 0.0001        | 0.3048          |
| 89.0     | 13172    | 0.0001        | 0.3395          |
| 90.0     | 13320    | 0.0001        | 0.3204          |
| 91.0     | 13468    | 0.0001        | 0.3122          |
| 92.0     | 13616    | 0.0001        | 0.3372          |
| 93.0     | 13764    | 0.0001        | 0.3306          |
| 94.0     | 13912    | 0.0001        | 0.3362          |
| 95.0     | 14060    | 0.0001        | 0.3386          |
| 96.0     | 14208    | 0.0001        | 0.3198          |
| 97.0     | 14356    | 0.0001        | 0.3176          |
| 98.0     | 14504    | 0.0001        | 0.3604          |
| 99.0     | 14652    | 0.0001        | 0.3507          |
| 100.0    | 14800    | 0.0001        | 0.3272          |
| 0.0023   | 1        | 0.0001        | -               |
| 1.0      | 444      | 0.0002        | 0.3295          |
| 2.0      | 888      | 0.0001        | 0.3144          |
| 3.0      | 1332     | 0.0001        | 0.3213          |
| 4.0      | 1776     | 0.0001        | 0.3362          |
| 5.0      | 2220     | 0.0001        | 0.3398          |
| 6.0      | 2664     | 0.0001        | 0.3385          |
| 7.0      | 3108     | 0.0002        | 0.3406          |
| 8.0      | 3552     | 0.0001        | 0.3253          |
| 9.0      | 3996     | 0.0001        | 0.3253          |
| 10.0     | 4440     | 0.0001        | 0.3119          |
| 11.0     | 4884     | 0.0001        | 0.3204          |
| 12.0     | 5328     | 0.0001        | 0.3387          |
| 13.0     | 5772     | 0.0001        | 0.3387          |
| 14.0     | 6216     | 0.0001        | 0.3584          |
| 15.0     | 6660     | 0.0001        | 0.3548          |
| 16.0     | 7104     | 0.0001        | 0.3314          |
| 17.0     | 7548     | 0.0001        | 0.3335          |
| 18.0     | 7992     | 0.0001        | 0.3325          |
| 19.0     | 8436     | 0.0001        | 0.3545          |
| 20.0     | 8880     | 0.0001        | 0.3456          |
| **21.0** | **9324** | **0.0001**    | **0.3532**      |
| 22.0     | 9768     | 0.0001        | 0.3524          |
| 23.0     | 10212    | 0.0001        | 0.352           |
| 24.0     | 10656    | 0.0001        | 0.3502          |
| 25.0     | 11100    | 0.0           | 0.3275          |
| 0.0034   | 1        | 0.0001        | -               |
| 1.0      | 296      | 0.0001        | 0.3209          |
| 2.0      | 592      | 0.0001        | 0.3265          |
| 3.0      | 888      | 0.0001        | 0.3414          |
| 4.0      | 1184     | 0.0001        | 0.3314          |
| 5.0      | 1480     | 0.0002        | 0.3498          |
| 6.0      | 1776     | 0.0001        | 0.337           |
| 7.0      | 2072     | 0.0001        | 0.3347          |
| 8.0      | 2368     | 0.0001        | 0.3494          |
| 9.0      | 2664     | 0.0001        | 0.3326          |
| 10.0     | 2960     | 0.0001        | 0.3259          |
| 11.0     | 3256     | 0.0002        | 0.3443          |
| 12.0     | 3552     | 0.0001        | 0.3431          |
| 13.0     | 3848     | 0.0001        | 0.324           |
| 14.0     | 4144     | 0.0001        | 0.3339          |
| 15.0     | 4440     | 0.0001        | 0.3255          |
| 16.0     | 4736     | 0.0001        | 0.3379          |
| 17.0     | 5032     | 0.0001        | 0.3285          |
| 18.0     | 5328     | 0.0001        | 0.3362          |
| 19.0     | 5624     | 0.0001        | 0.3319          |
| 20.0     | 5920     | 0.0001        | 0.3456          |
| 21.0     | 6216     | 0.0001        | 0.329           |
| 22.0     | 6512     | 0.0001        | 0.3386          |
| 23.0     | 6808     | 0.0001        | 0.3278          |
| 24.0     | 7104     | 0.0001        | 0.3078          |
| 25.0     | 7400     | 0.0001        | 0.3155          |
| 0.0068   | 1        | 0.0001        | -               |
| 1.0      | 148      | 0.0001        | 0.3225          |
| 2.0      | 296      | 0.0001        | 0.3526          |
| 3.0      | 444      | 0.0001        | 0.3265          |
| 4.0      | 592      | 0.0001        | 0.3206          |
| 5.0      | 740      | 0.0001        | 0.3126          |
| 6.0      | 888      | 0.0001        | 0.3306          |
| 7.0      | 1036     | 0.0001        | 0.3189          |
| 8.0      | 1184     | 0.0001        | 0.3246          |
| 9.0      | 1332     | 0.0001        | 0.3346          |
| 10.0     | 1480     | 0.0001        | 0.3528          |
| 11.0     | 1628     | 0.0001        | 0.3204          |
| 12.0     | 1776     | 0.0001        | 0.34            |
| 13.0     | 1924     | 0.0001        | 0.3291          |
| 14.0     | 2072     | 0.0001        | 0.3444          |
| 15.0     | 2220     | 0.0001        | 0.339           |
| 16.0     | 2368     | 0.0001        | 0.3533          |
| 17.0     | 2516     | 0.0001        | 0.3288          |
| 18.0     | 2664     | 0.0001        | 0.3475          |
| 19.0     | 2812     | 0.0001        | 0.3464          |
| 20.0     | 2960     | 0.0001        | 0.3351          |
| 21.0     | 3108     | 0.0001        | 0.3421          |
| 22.0     | 3256     | 0.0001        | 0.3351          |
| 23.0     | 3404     | 0.0001        | 0.3416          |
| 24.0     | 3552     | 0.0001        | 0.3414          |
| 25.0     | 3700     | 0.0001        | 0.3433          |
| 26.0     | 3848     | 0.0001        | 0.3339          |
| 27.0     | 3996     | 0.0001        | 0.35            |
| 28.0     | 4144     | 0.0001        | 0.3215          |
| 29.0     | 4292     | 0.0001        | 0.3278          |
| 30.0     | 4440     | 0.0001        | 0.3508          |
| 31.0     | 4588     | 0.0001        | 0.3356          |
| 32.0     | 4736     | 0.0001        | 0.3617          |
| 33.0     | 4884     | 0.0001        | 0.3368          |
| 34.0     | 5032     | 0.0001        | 0.3551          |
| 35.0     | 5180     | 0.0001        | 0.3582          |
| 36.0     | 5328     | 0.0001        | 0.333           |
| 37.0     | 5476     | 0.0           | 0.3461          |
| 38.0     | 5624     | 0.0001        | 0.3515          |
| 39.0     | 5772     | 0.0001        | 0.3601          |
| 40.0     | 5920     | 0.0001        | 0.347           |
| 41.0     | 6068     | 0.0001        | 0.3444          |
| 42.0     | 6216     | 0.0           | 0.3609          |
| 43.0     | 6364     | 0.0           | 0.3432          |
| 44.0     | 6512     | 0.0           | 0.3526          |
| 45.0     | 6660     | 0.0           | 0.3382          |
| 46.0     | 6808     | 0.0           | 0.353           |
| 47.0     | 6956     | 0.0001        | 0.3374          |
| 48.0     | 7104     | 0.0001        | 0.327           |
| 49.0     | 7252     | 0.0001        | 0.3202          |
| 50.0     | 7400     | 0.0           | 0.3386          |
| 51.0     | 7548     | 0.0001        | 0.3501          |
| 52.0     | 7696     | 0.0002        | 0.3341          |
| 53.0     | 7844     | 0.0001        | 0.3024          |
| 54.0     | 7992     | 0.0001        | 0.3456          |
| 55.0     | 8140     | 0.0           | 0.3323          |
| 56.0     | 8288     | 0.0           | 0.3259          |
| 57.0     | 8436     | 0.0           | 0.3246          |
| 58.0     | 8584     | 0.0           | 0.3341          |
| 59.0     | 8732     | 0.0           | 0.3347          |
| 60.0     | 8880     | 0.0           | 0.322           |
| 61.0     | 9028     | 0.0001        | 0.3323          |
| 62.0     | 9176     | 0.0           | 0.3471          |
| **63.0** | **9324** | **0.0001**    | **0.2913**      |
| 64.0     | 9472     | 0.0           | 0.3144          |
| 65.0     | 9620     | 0.0001        | 0.3184          |
| 66.0     | 9768     | 0.0           | 0.3251          |
| 67.0     | 9916     | 0.0001        | 0.3342          |
| 68.0     | 10064    | 0.0           | 0.3486          |
| 69.0     | 10212    | 0.0           | 0.3381          |
| 70.0     | 10360    | 0.0           | 0.3161          |
| 71.0     | 10508    | 0.0           | 0.3036          |
| 72.0     | 10656    | 0.0           | 0.3141          |
| 73.0     | 10804    | 0.0           | 0.3307          |
| 74.0     | 10952    | 0.0           | 0.3153          |
| 75.0     | 11100    | 0.0           | 0.3016          |
| 76.0     | 11248    | 0.0001        | 0.3321          |
| 77.0     | 11396    | 0.0001        | 0.3194          |
| 78.0     | 11544    | 0.0001        | 0.3496          |
| 79.0     | 11692    | 0.0           | 0.3218          |
| 80.0     | 11840    | 0.0           | 0.3251          |
| 81.0     | 11988    | 0.0           | 0.3468          |
| 82.0     | 12136    | 0.0           | 0.3803          |
| 83.0     | 12284    | 0.0           | 0.3354          |
| 84.0     | 12432    | 0.0           | 0.351           |
| 85.0     | 12580    | 0.0           | 0.3231          |
| 86.0     | 12728    | 0.0           | 0.3027          |
| 87.0     | 12876    | 0.0           | 0.3309          |
| 88.0     | 13024    | 0.0           | 0.3194          |
| 89.0     | 13172    | 0.0           | 0.3611          |
| 90.0     | 13320    | 0.0           | 0.3288          |
| 91.0     | 13468    | 0.0           | 0.3261          |
| 92.0     | 13616    | 0.0           | 0.3268          |
| 93.0     | 13764    | 0.0           | 0.3433          |
| 94.0     | 13912    | 0.0           | 0.3438          |
| 95.0     | 14060    | 0.0           | 0.3288          |
| 96.0     | 14208    | 0.0           | 0.3263          |
| 97.0     | 14356    | 0.0           | 0.3331          |
| 98.0     | 14504    | 0.0           | 0.3334          |
| 99.0     | 14652    | 0.0           | 0.319           |
| 100.0    | 14800    | 0.0           | 0.3033          |
| 101.0    | 14948    | 0.0001        | 0.3051          |
| 102.0    | 15096    | 0.0           | 0.3321          |
| 103.0    | 15244    | 0.0           | 0.3181          |
| 104.0    | 15392    | 0.0           | 0.2943          |
| 105.0    | 15540    | 0.0           | 0.3137          |
| 106.0    | 15688    | 0.0           | 0.3111          |
| 107.0    | 15836    | 0.0           | 0.2968          |
| 108.0    | 15984    | 0.0           | 0.3072          |
| 109.0    | 16132    | 0.0           | 0.3154          |
| 110.0    | 16280    | 0.0001        | 0.3211          |
| 111.0    | 16428    | 0.0           | 0.2974          |
| 112.0    | 16576    | 0.0           | 0.3057          |
| 113.0    | 16724    | 0.0           | 0.296           |
| 114.0    | 16872    | 0.0           | 0.3104          |
| 115.0    | 17020    | 0.0           | 0.3029          |
| 116.0    | 17168    | 0.0           | 0.329           |
| 117.0    | 17316    | 0.0           | 0.3275          |
| 118.0    | 17464    | 0.0           | 0.3343          |
| 119.0    | 17612    | 0.0           | 0.3168          |
| 120.0    | 17760    | 0.0           | 0.3208          |
| 121.0    | 17908    | 0.0           | 0.2973          |
| 122.0    | 18056    | 0.0           | 0.3121          |
| 123.0    | 18204    | 0.0           | 0.3049          |
| 124.0    | 18352    | 0.0           | 0.3079          |
| 125.0    | 18500    | 0.0           | 0.2994          |
| 126.0    | 18648    | 0.0           | 0.3189          |
| 127.0    | 18796    | 0.0           | 0.3255          |
| 128.0    | 18944    | 0.0           | 0.3111          |
| 129.0    | 19092    | 0.0           | 0.3182          |
| 130.0    | 19240    | 0.0           | 0.356           |
| 131.0    | 19388    | 0.0           | 0.3299          |
| 132.0    | 19536    | 0.0           | 0.3308          |
| 133.0    | 19684    | 0.0           | 0.3379          |
| 134.0    | 19832    | 0.0           | 0.3233          |
| 135.0    | 19980    | 0.0           | 0.327           |
| 136.0    | 20128    | 0.0           | 0.318           |
| 137.0    | 20276    | 0.0           | 0.2937          |
| 138.0    | 20424    | 0.0           | 0.3039          |
| 139.0    | 20572    | 0.0           | 0.3367          |
| 140.0    | 20720    | 0.0           | 0.3185          |
| 141.0    | 20868    | 0.0           | 0.3441          |
| 142.0    | 21016    | 0.0           | 0.3055          |
| 143.0    | 21164    | 0.0           | 0.3202          |
| 144.0    | 21312    | 0.0           | 0.3144          |
| 145.0    | 21460    | 0.0           | 0.3304          |
| 146.0    | 21608    | 0.0           | 0.3165          |
| 147.0    | 21756    | 0.0           | 0.309           |
| 148.0    | 21904    | 0.0           | 0.3086          |
| 149.0    | 22052    | 0.0           | 0.2987          |
| 150.0    | 22200    | 0.0           | 0.3198          |
| 151.0    | 22348    | 0.0           | 0.3372          |
| 152.0    | 22496    | 0.0           | 0.3156          |
| 153.0    | 22644    | 0.0           | 0.3206          |
| 154.0    | 22792    | 0.0           | 0.322           |
| 155.0    | 22940    | 0.0           | 0.3445          |
| 156.0    | 23088    | 0.0           | 0.3183          |
| 157.0    | 23236    | 0.0           | 0.3203          |
| 158.0    | 23384    | 0.0           | 0.3337          |
| 159.0    | 23532    | 0.0           | 0.3245          |
| 160.0    | 23680    | 0.0           | 0.3068          |
| 161.0    | 23828    | 0.0           | 0.3199          |
| 162.0    | 23976    | 0.0           | 0.3308          |
| 163.0    | 24124    | 0.0           | 0.3446          |
| 164.0    | 24272    | 0.0           | 0.341           |
| 165.0    | 24420    | 0.0           | 0.3155          |
| 166.0    | 24568    | 0.0           | 0.3306          |
| 167.0    | 24716    | 0.0           | 0.3422          |
| 168.0    | 24864    | 0.0           | 0.336           |
| 169.0    | 25012    | 0.0           | 0.3271          |
| 170.0    | 25160    | 0.0           | 0.3062          |
| 171.0    | 25308    | 0.0           | 0.305           |
| 172.0    | 25456    | 0.0           | 0.3047          |
| 173.0    | 25604    | 0.0           | 0.3281          |
| 174.0    | 25752    | 0.0           | 0.3059          |
| 175.0    | 25900    | 0.0           | 0.2993          |
| 176.0    | 26048    | 0.0           | 0.3206          |
| 177.0    | 26196    | 0.0           | 0.3274          |
| 178.0    | 26344    | 0.0           | 0.3249          |
| 179.0    | 26492    | 0.0           | 0.3049          |
| 180.0    | 26640    | 0.0           | 0.3131          |
| 181.0    | 26788    | 0.0           | 0.3119          |
| 182.0    | 26936    | 0.0           | 0.3457          |
| 183.0    | 27084    | 0.0           | 0.3242          |
| 184.0    | 27232    | 0.0           | 0.3006          |
| 185.0    | 27380    | 0.0           | 0.3054          |
| 186.0    | 27528    | 0.0           | 0.3135          |
| 187.0    | 27676    | 0.0           | 0.3102          |
| 188.0    | 27824    | 0.0           | 0.3394          |
| 189.0    | 27972    | 0.0           | 0.3256          |
| 190.0    | 28120    | 0.0           | 0.2973          |
| 191.0    | 28268    | 0.0           | 0.3124          |
| 192.0    | 28416    | 0.0           | 0.321           |
| 193.0    | 28564    | 0.0           | 0.3332          |
| 194.0    | 28712    | 0.0           | 0.3136          |
| 195.0    | 28860    | 0.0           | 0.32            |
| 196.0    | 29008    | 0.0           | 0.3486          |
| 197.0    | 29156    | 0.0           | 0.3259          |
| 198.0    | 29304    | 0.0           | 0.3134          |
| 199.0    | 29452    | 0.0           | 0.3437          |
| 200.0    | 29600    | 0.0           | 0.3029          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- Tokenizers: 0.15.2

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->