---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- precision
- recall
- f1
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.
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
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
### 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 | - 'Tennessee has an annual sales tax-free holiday weekend that\xa0begins\xa0on the last Friday of July.\xa0'
- '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.'
- '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'
|
| right | - '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.'
- '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.'
- '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.'
|
| center | - 'LeBron James shares thoughts on his Los Angeles house getting vandalized pic twitter com 4RFLK42xhu'
- '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.'
- 'Here are a few people we will be reminding you about in every article that pertains to a film they re tied to '
|
## 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.")
```
## 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}
}
```