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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: We are awaiting payment for the project completed in June. Please confirm
when this will be processed.
- text: Hello, Good morning, would you mind cancelling this rental car?
- text: 'Kindly book accommodation for Lindelani Mkhize as follows: Establishment:
City Lodge Lynwood Date checked in : 04 October 2023 Time checked in: 19h00pm
Date checked out: 06 October 2023 Time checked out: 07h00am'
- text: You've been selected for a free energy audit. Click here to schedule your
appointment.
- text: 'Please can you provide with the invoices for my stays this month as follows: 1.
Premier Splendid Inn Bayshore (07 Aug - 08 Aug) 2. Port Nolloth Beach Shack
(14 Aug - 17 Aug)'
metrics:
- silhouette_score
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: silhouette_score
value: 0.6826105442176871
name: Silhouette_Score
---
# SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 14 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Silhouette_Score |
|:--------|:-----------------|
| **all** | 0.6826 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM_HTTest")
# Run inference
preds = model("Hello, Good morning, would you mind cancelling this rental car?")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 25.6577 | 136 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 24 |
| 1 | 24 |
| 2 | 24 |
| 3 | 24 |
| 4 | 24 |
| 5 | 24 |
| 6 | 24 |
| 7 | 24 |
| 8 | 24 |
| 9 | 24 |
| 10 | 24 |
| 11 | 24 |
| 12 | 24 |
| 13 | 24 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- body_learning_rate: (3e-05, 3e-05)
- head_learning_rate: 3e-05
- loss: MultipleNegativesRankingLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 2.5259 | - |
| 0.0060 | 50 | 2.8997 | - |
| 0.0119 | 100 | 2.8192 | - |
| 0.0179 | 150 | 2.8803 | - |
| 0.0238 | 200 | 2.635 | - |
| 0.0298 | 250 | 2.5501 | - |
| 0.0357 | 300 | 2.4468 | - |
| 0.0417 | 350 | 2.1309 | - |
| 0.0476 | 400 | 2.0439 | - |
| 0.0536 | 450 | 1.9429 | - |
| 0.0595 | 500 | 1.9344 | - |
| 0.0655 | 550 | 1.8493 | - |
| 0.0714 | 600 | 1.7907 | - |
| 0.0774 | 650 | 1.7712 | - |
| 0.0833 | 700 | 1.7349 | - |
| 0.0893 | 750 | 1.7783 | - |
| 0.0952 | 800 | 1.7022 | - |
| 0.1012 | 850 | 1.6757 | - |
| 0.1071 | 900 | 1.709 | - |
| 0.1131 | 950 | 1.6231 | - |
| 0.1190 | 1000 | 1.6647 | - |
| 0.125 | 1050 | 1.7618 | - |
| 0.1310 | 1100 | 1.652 | - |
| 0.1369 | 1150 | 1.5564 | - |
| 0.1429 | 1200 | 1.7067 | - |
| 0.1488 | 1250 | 1.664 | - |
| 0.1548 | 1300 | 1.7426 | - |
| 0.1607 | 1350 | 1.6281 | - |
| 0.1667 | 1400 | 1.6375 | - |
| 0.1726 | 1450 | 1.6216 | - |
| 0.1786 | 1500 | 1.5998 | - |
| 0.1845 | 1550 | 1.4892 | - |
| 0.1905 | 1600 | 1.556 | - |
| 0.1964 | 1650 | 1.6657 | - |
| 0.2024 | 1700 | 1.6113 | - |
| 0.2083 | 1750 | 1.634 | - |
| 0.2143 | 1800 | 1.6615 | - |
| 0.2202 | 1850 | 1.5192 | - |
| 0.2262 | 1900 | 1.5846 | - |
| 0.2321 | 1950 | 1.5376 | - |
| 0.2381 | 2000 | 1.6028 | - |
| 0.2440 | 2050 | 1.5744 | - |
| 0.25 | 2100 | 1.645 | - |
| 0.2560 | 2150 | 1.5432 | - |
| 0.2619 | 2200 | 1.5922 | - |
| 0.2679 | 2250 | 1.612 | - |
| 0.2738 | 2300 | 1.6553 | - |
| 0.2798 | 2350 | 1.5797 | - |
| 0.2857 | 2400 | 1.5249 | - |
| 0.2917 | 2450 | 1.639 | - |
| 0.2976 | 2500 | 1.7246 | - |
| 0.3036 | 2550 | 1.6186 | - |
| 0.3095 | 2600 | 1.537 | - |
| 0.3155 | 2650 | 1.5701 | - |
| 0.3214 | 2700 | 1.6095 | - |
| 0.3274 | 2750 | 1.5344 | - |
| 0.3333 | 2800 | 1.6029 | - |
| 0.3393 | 2850 | 1.6141 | - |
| 0.3452 | 2900 | 1.5655 | - |
| 0.3512 | 2950 | 1.5892 | - |
| 0.3571 | 3000 | 1.595 | - |
| 0.3631 | 3050 | 1.5068 | - |
| 0.3690 | 3100 | 1.5826 | - |
| 0.375 | 3150 | 1.481 | - |
| 0.3810 | 3200 | 1.6001 | - |
| 0.3869 | 3250 | 1.4991 | - |
| 0.3929 | 3300 | 1.605 | - |
| 0.3988 | 3350 | 1.6154 | - |
| 0.4048 | 3400 | 1.5516 | - |
| 0.4107 | 3450 | 1.559 | - |
| 0.4167 | 3500 | 1.559 | - |
| 0.4226 | 3550 | 1.5725 | - |
| 0.4286 | 3600 | 1.5719 | - |
| 0.4345 | 3650 | 1.4918 | - |
| 0.4405 | 3700 | 1.5816 | - |
| 0.4464 | 3750 | 1.5017 | - |
| 0.4524 | 3800 | 1.5093 | - |
| 0.4583 | 3850 | 1.5705 | - |
| 0.4643 | 3900 | 1.5584 | - |
| 0.4702 | 3950 | 1.5328 | - |
| 0.4762 | 4000 | 1.4932 | - |
| 0.4821 | 4050 | 1.5907 | - |
| 0.4881 | 4100 | 1.5339 | - |
| 0.4940 | 4150 | 1.4954 | - |
| 0.5 | 4200 | 1.5256 | - |
| 0.5060 | 4250 | 1.5349 | - |
| 0.5119 | 4300 | 1.5238 | - |
| 0.5179 | 4350 | 1.5222 | - |
| 0.5238 | 4400 | 1.6318 | - |
| 0.5298 | 4450 | 1.5872 | - |
| 0.5357 | 4500 | 1.4892 | - |
| 0.5417 | 4550 | 1.5764 | - |
| 0.5476 | 4600 | 1.6123 | - |
| 0.5536 | 4650 | 1.4708 | - |
| 0.5595 | 4700 | 1.5201 | - |
| 0.5655 | 4750 | 1.4975 | - |
| 0.5714 | 4800 | 1.5402 | - |
| 0.5774 | 4850 | 1.5396 | - |
| 0.5833 | 4900 | 1.5325 | - |
| 0.5893 | 4950 | 1.5166 | - |
| 0.5952 | 5000 | 1.5216 | - |
| 0.6012 | 5050 | 1.5934 | - |
| 0.6071 | 5100 | 1.5118 | - |
| 0.6131 | 5150 | 1.6581 | - |
| 0.6190 | 5200 | 1.4251 | - |
| 0.625 | 5250 | 1.5259 | - |
| 0.6310 | 5300 | 1.4854 | - |
| 0.6369 | 5350 | 1.6242 | - |
| 0.6429 | 5400 | 1.5234 | - |
| 0.6488 | 5450 | 1.4594 | - |
| 0.6548 | 5500 | 1.5513 | - |
| 0.6607 | 5550 | 1.3946 | - |
| 0.6667 | 5600 | 1.4795 | - |
| 0.6726 | 5650 | 1.5203 | - |
| 0.6786 | 5700 | 1.5137 | - |
| 0.6845 | 5750 | 1.5305 | - |
| 0.6905 | 5800 | 1.4958 | - |
| 0.6964 | 5850 | 1.5028 | - |
| 0.7024 | 5900 | 1.419 | - |
| 0.7083 | 5950 | 1.5043 | - |
| 0.7143 | 6000 | 1.4512 | - |
| 0.7202 | 6050 | 1.5199 | - |
| 0.7262 | 6100 | 1.5097 | - |
| 0.7321 | 6150 | 1.4989 | - |
| 0.7381 | 6200 | 1.4632 | - |
| 0.7440 | 6250 | 1.4781 | - |
| 0.75 | 6300 | 1.4592 | - |
| 0.7560 | 6350 | 1.507 | - |
| 0.7619 | 6400 | 1.5535 | - |
| 0.7679 | 6450 | 1.3831 | - |
| 0.7738 | 6500 | 1.572 | - |
| 0.7798 | 6550 | 1.5461 | - |
| 0.7857 | 6600 | 1.5142 | - |
| 0.7917 | 6650 | 1.494 | - |
| 0.7976 | 6700 | 1.5487 | - |
| 0.8036 | 6750 | 1.4344 | - |
| 0.8095 | 6800 | 1.5262 | - |
| 0.8155 | 6850 | 1.4942 | - |
| 0.8214 | 6900 | 1.54 | - |
| 0.8274 | 6950 | 1.518 | - |
| 0.8333 | 7000 | 1.5765 | - |
| 0.8393 | 7050 | 1.5526 | - |
| 0.8452 | 7100 | 1.5548 | - |
| 0.8512 | 7150 | 1.3953 | - |
| 0.8571 | 7200 | 1.5273 | - |
| 0.8631 | 7250 | 1.4349 | - |
| 0.8690 | 7300 | 1.4176 | - |
| 0.875 | 7350 | 1.5242 | - |
| 0.8810 | 7400 | 1.5263 | - |
| 0.8869 | 7450 | 1.5435 | - |
| 0.8929 | 7500 | 1.4882 | - |
| 0.8988 | 7550 | 1.4965 | - |
| 0.9048 | 7600 | 1.5185 | - |
| 0.9107 | 7650 | 1.5739 | - |
| 0.9167 | 7700 | 1.5821 | - |
| 0.9226 | 7750 | 1.6197 | - |
| 0.9286 | 7800 | 1.5154 | - |
| 0.9345 | 7850 | 1.5844 | - |
| 0.9405 | 7900 | 1.5242 | - |
| 0.9464 | 7950 | 1.488 | - |
| 0.9524 | 8000 | 1.5414 | - |
| 0.9583 | 8050 | 1.4829 | - |
| 0.9643 | 8100 | 1.5162 | - |
| 0.9702 | 8150 | 1.4136 | - |
| 0.9762 | 8200 | 1.36 | - |
| 0.9821 | 8250 | 1.5511 | - |
| 0.9881 | 8300 | 1.4908 | - |
| 0.9940 | 8350 | 1.5312 | - |
| 1.0 | 8400 | 1.5008 | - |
| 1.0060 | 8450 | 1.4283 | - |
| 1.0119 | 8500 | 1.5027 | - |
| 1.0179 | 8550 | 1.48 | - |
| 1.0238 | 8600 | 1.425 | - |
| 1.0298 | 8650 | 1.5233 | - |
| 1.0357 | 8700 | 1.4259 | - |
| 1.0417 | 8750 | 1.4355 | - |
| 1.0476 | 8800 | 1.5006 | - |
| 1.0536 | 8850 | 1.511 | - |
| 1.0595 | 8900 | 1.3043 | - |
| 1.0655 | 8950 | 1.5039 | - |
| 1.0714 | 9000 | 1.4909 | - |
| 1.0774 | 9050 | 1.4493 | - |
| 1.0833 | 9100 | 1.4877 | - |
| 1.0893 | 9150 | 1.5232 | - |
| 1.0952 | 9200 | 1.6282 | - |
| 1.1012 | 9250 | 1.4438 | - |
| 1.1071 | 9300 | 1.5234 | - |
| 1.1131 | 9350 | 1.5368 | - |
| 1.1190 | 9400 | 1.5029 | - |
| 1.125 | 9450 | 1.4776 | - |
| 1.1310 | 9500 | 1.4877 | - |
| 1.1369 | 9550 | 1.4917 | - |
| 1.1429 | 9600 | 1.4474 | - |
| 1.1488 | 9650 | 1.3519 | - |
| 1.1548 | 9700 | 1.5118 | - |
| 1.1607 | 9750 | 1.5507 | - |
| 1.1667 | 9800 | 1.4395 | - |
| 1.1726 | 9850 | 1.4883 | - |
| 1.1786 | 9900 | 1.4524 | - |
| 1.1845 | 9950 | 1.4756 | - |
| 1.1905 | 10000 | 1.5255 | - |
| 1.1964 | 10050 | 1.4795 | - |
| 1.2024 | 10100 | 1.5277 | - |
| 1.2083 | 10150 | 1.477 | - |
| 1.2143 | 10200 | 1.4438 | - |
| 1.2202 | 10250 | 1.5517 | - |
| 1.2262 | 10300 | 1.588 | - |
| 1.2321 | 10350 | 1.5352 | - |
| 1.2381 | 10400 | 1.3697 | - |
| 1.2440 | 10450 | 1.4449 | - |
| 1.25 | 10500 | 1.4473 | - |
| 1.2560 | 10550 | 1.5566 | - |
| 1.2619 | 10600 | 1.4502 | - |
| 1.2679 | 10650 | 1.4821 | - |
| 1.2738 | 10700 | 1.4296 | - |
| 1.2798 | 10750 | 1.4801 | - |
| 1.2857 | 10800 | 1.4542 | - |
| 1.2917 | 10850 | 1.4258 | - |
| 1.2976 | 10900 | 1.4142 | - |
| 1.3036 | 10950 | 1.6023 | - |
| 1.3095 | 11000 | 1.4291 | - |
| 1.3155 | 11050 | 1.5386 | - |
| 1.3214 | 11100 | 1.4433 | - |
| 1.3274 | 11150 | 1.4218 | - |
| 1.3333 | 11200 | 1.4345 | - |
| 1.3393 | 11250 | 1.5321 | - |
| 1.3452 | 11300 | 1.5001 | - |
| 1.3512 | 11350 | 1.3381 | - |
| 1.3571 | 11400 | 1.4819 | - |
| 1.3631 | 11450 | 1.4676 | - |
| 1.3690 | 11500 | 1.5056 | - |
| 1.375 | 11550 | 1.5052 | - |
| 1.3810 | 11600 | 1.5217 | - |
| 1.3869 | 11650 | 1.391 | - |
| 1.3929 | 11700 | 1.46 | - |
| 1.3988 | 11750 | 1.5022 | - |
| 1.4048 | 11800 | 1.4579 | - |
| 1.4107 | 11850 | 1.5025 | - |
| 1.4167 | 11900 | 1.5058 | - |
| 1.4226 | 11950 | 1.5107 | - |
| 1.4286 | 12000 | 1.5327 | - |
| 1.4345 | 12050 | 1.4727 | - |
| 1.4405 | 12100 | 1.4353 | - |
| 1.4464 | 12150 | 1.42 | - |
| 1.4524 | 12200 | 1.5349 | - |
| 1.4583 | 12250 | 1.473 | - |
| 1.4643 | 12300 | 1.5228 | - |
| 1.4702 | 12350 | 1.498 | - |
| 1.4762 | 12400 | 1.4321 | - |
| 1.4821 | 12450 | 1.5058 | - |
| 1.4881 | 12500 | 1.4601 | - |
| 1.4940 | 12550 | 1.5346 | - |
| 1.5 | 12600 | 1.5985 | - |
| 1.5060 | 12650 | 1.4683 | - |
| 1.5119 | 12700 | 1.5088 | - |
| 1.5179 | 12750 | 1.5082 | - |
| 1.5238 | 12800 | 1.5784 | - |
| 1.5298 | 12850 | 1.5241 | - |
| 1.5357 | 12900 | 1.434 | - |
| 1.5417 | 12950 | 1.452 | - |
| 1.5476 | 13000 | 1.4459 | - |
| 1.5536 | 13050 | 1.4965 | - |
| 1.5595 | 13100 | 1.5313 | - |
| 1.5655 | 13150 | 1.4781 | - |
| 1.5714 | 13200 | 1.5502 | - |
| 1.5774 | 13250 | 1.4602 | - |
| 1.5833 | 13300 | 1.4477 | - |
| 1.5893 | 13350 | 1.4736 | - |
| 1.5952 | 13400 | 1.5035 | - |
| 1.6012 | 13450 | 1.4829 | - |
| 1.6071 | 13500 | 1.4941 | - |
| 1.6131 | 13550 | 1.5462 | - |
| 1.6190 | 13600 | 1.4764 | - |
| 1.625 | 13650 | 1.4838 | - |
| 1.6310 | 13700 | 1.4264 | - |
| 1.6369 | 13750 | 1.6312 | - |
| 1.6429 | 13800 | 1.4323 | - |
| 1.6488 | 13850 | 1.514 | - |
| 1.6548 | 13900 | 1.3944 | - |
| 1.6607 | 13950 | 1.4709 | - |
| 1.6667 | 14000 | 1.4268 | - |
| 1.6726 | 14050 | 1.5699 | - |
| 1.6786 | 14100 | 1.5433 | - |
| 1.6845 | 14150 | 1.431 | - |
| 1.6905 | 14200 | 1.5421 | - |
| 1.6964 | 14250 | 1.4854 | - |
| 1.7024 | 14300 | 1.4341 | - |
| 1.7083 | 14350 | 1.4321 | - |
| 1.7143 | 14400 | 1.4284 | - |
| 1.7202 | 14450 | 1.4725 | - |
| 1.7262 | 14500 | 1.5744 | - |
| 1.7321 | 14550 | 1.4892 | - |
| 1.7381 | 14600 | 1.5357 | - |
| 1.7440 | 14650 | 1.4536 | - |
| 1.75 | 14700 | 1.4861 | - |
| 1.7560 | 14750 | 1.5268 | - |
| 1.7619 | 14800 | 1.4613 | - |
| 1.7679 | 14850 | 1.4313 | - |
| 1.7738 | 14900 | 1.4522 | - |
| 1.7798 | 14950 | 1.4291 | - |
| 1.7857 | 15000 | 1.5054 | - |
| 1.7917 | 15050 | 1.495 | - |
| 1.7976 | 15100 | 1.5352 | - |
| 1.8036 | 15150 | 1.4803 | - |
| 1.8095 | 15200 | 1.3922 | - |
| 1.8155 | 15250 | 1.4879 | - |
| 1.8214 | 15300 | 1.4752 | - |
| 1.8274 | 15350 | 1.5102 | - |
| 1.8333 | 15400 | 1.4474 | - |
| 1.8393 | 15450 | 1.4939 | - |
| 1.8452 | 15500 | 1.5216 | - |
| 1.8512 | 15550 | 1.4656 | - |
| 1.8571 | 15600 | 1.5171 | - |
| 1.8631 | 15650 | 1.3437 | - |
| 1.8690 | 15700 | 1.4875 | - |
| 1.875 | 15750 | 1.4692 | - |
| 1.8810 | 15800 | 1.4804 | - |
| 1.8869 | 15850 | 1.4423 | - |
| 1.8929 | 15900 | 1.4592 | - |
| 1.8988 | 15950 | 1.5764 | - |
| 1.9048 | 16000 | 1.4083 | - |
| 1.9107 | 16050 | 1.4852 | - |
| 1.9167 | 16100 | 1.5158 | - |
| 1.9226 | 16150 | 1.4602 | - |
| 1.9286 | 16200 | 1.4465 | - |
| 1.9345 | 16250 | 1.412 | - |
| 1.9405 | 16300 | 1.483 | - |
| 1.9464 | 16350 | 1.5342 | - |
| 1.9524 | 16400 | 1.3866 | - |
| 1.9583 | 16450 | 1.4318 | - |
| 1.9643 | 16500 | 1.6241 | - |
| 1.9702 | 16550 | 1.5514 | - |
| 1.9762 | 16600 | 1.46 | - |
| 1.9821 | 16650 | 1.4069 | - |
| 1.9881 | 16700 | 1.457 | - |
| 1.9940 | 16750 | 1.4273 | - |
| 2.0 | 16800 | 1.3673 | - |
| 2.0060 | 16850 | 1.3753 | - |
| 2.0119 | 16900 | 1.4279 | - |
| 2.0179 | 16950 | 1.3897 | - |
| 2.0238 | 17000 | 1.4659 | - |
| 2.0298 | 17050 | 1.4494 | - |
| 2.0357 | 17100 | 1.4533 | - |
| 2.0417 | 17150 | 1.3735 | - |
| 2.0476 | 17200 | 1.4232 | - |
| 2.0536 | 17250 | 1.4229 | - |
| 2.0595 | 17300 | 1.4597 | - |
| 2.0655 | 17350 | 1.4825 | - |
| 2.0714 | 17400 | 1.4661 | - |
| 2.0774 | 17450 | 1.4332 | - |
| 2.0833 | 17500 | 1.5895 | - |
| 2.0893 | 17550 | 1.4824 | - |
| 2.0952 | 17600 | 1.4472 | - |
| 2.1012 | 17650 | 1.4001 | - |
| 2.1071 | 17700 | 1.4638 | - |
| 2.1131 | 17750 | 1.4651 | - |
| 2.1190 | 17800 | 1.4711 | - |
| 2.125 | 17850 | 1.4474 | - |
| 2.1310 | 17900 | 1.4544 | - |
| 2.1369 | 17950 | 1.3935 | - |
| 2.1429 | 18000 | 1.4449 | - |
| 2.1488 | 18050 | 1.4671 | - |
| 2.1548 | 18100 | 1.4169 | - |
| 2.1607 | 18150 | 1.5095 | - |
| 2.1667 | 18200 | 1.4186 | - |
| 2.1726 | 18250 | 1.4574 | - |
| 2.1786 | 18300 | 1.4448 | - |
| 2.1845 | 18350 | 1.5045 | - |
| 2.1905 | 18400 | 1.4998 | - |
| 2.1964 | 18450 | 1.3559 | - |
| 2.2024 | 18500 | 1.4862 | - |
| 2.2083 | 18550 | 1.4018 | - |
| 2.2143 | 18600 | 1.4407 | - |
| 2.2202 | 18650 | 1.5812 | - |
| 2.2262 | 18700 | 1.4268 | - |
| 2.2321 | 18750 | 1.4434 | - |
| 2.2381 | 18800 | 1.5467 | - |
| 2.2440 | 18850 | 1.4281 | - |
| 2.25 | 18900 | 1.482 | - |
| 2.2560 | 18950 | 1.5261 | - |
| 2.2619 | 19000 | 1.4152 | - |
| 2.2679 | 19050 | 1.5267 | - |
| 2.2738 | 19100 | 1.4237 | - |
| 2.2798 | 19150 | 1.5455 | - |
| 2.2857 | 19200 | 1.4679 | - |
| 2.2917 | 19250 | 1.3398 | - |
| 2.2976 | 19300 | 1.4697 | - |
| 2.3036 | 19350 | 1.4176 | - |
| 2.3095 | 19400 | 1.4661 | - |
| 2.3155 | 19450 | 1.4397 | - |
| 2.3214 | 19500 | 1.5095 | - |
| 2.3274 | 19550 | 1.4873 | - |
| 2.3333 | 19600 | 1.4312 | - |
| 2.3393 | 19650 | 1.441 | - |
| 2.3452 | 19700 | 1.4341 | - |
| 2.3512 | 19750 | 1.4229 | - |
| 2.3571 | 19800 | 1.4917 | - |
| 2.3631 | 19850 | 1.4397 | - |
| 2.3690 | 19900 | 1.4027 | - |
| 2.375 | 19950 | 1.5022 | - |
| 2.3810 | 20000 | 1.441 | - |
| 2.3869 | 20050 | 1.4392 | - |
| 2.3929 | 20100 | 1.4454 | - |
| 2.3988 | 20150 | 1.4886 | - |
| 2.4048 | 20200 | 1.4776 | - |
| 2.4107 | 20250 | 1.3946 | - |
| 2.4167 | 20300 | 1.5492 | - |
| 2.4226 | 20350 | 1.534 | - |
| 2.4286 | 20400 | 1.4011 | - |
| 2.4345 | 20450 | 1.5276 | - |
| 2.4405 | 20500 | 1.4633 | - |
| 2.4464 | 20550 | 1.4446 | - |
| 2.4524 | 20600 | 1.5005 | - |
| 2.4583 | 20650 | 1.4818 | - |
| 2.4643 | 20700 | 1.4319 | - |
| 2.4702 | 20750 | 1.4406 | - |
| 2.4762 | 20800 | 1.4496 | - |
| 2.4821 | 20850 | 1.4963 | - |
| 2.4881 | 20900 | 1.4731 | - |
| 2.4940 | 20950 | 1.4536 | - |
| 2.5 | 21000 | 1.5153 | - |
| 2.5060 | 21050 | 1.5522 | - |
| 2.5119 | 21100 | 1.3759 | - |
| 2.5179 | 21150 | 1.4285 | - |
| 2.5238 | 21200 | 1.4162 | - |
| 2.5298 | 21250 | 1.4383 | - |
| 2.5357 | 21300 | 1.4408 | - |
| 2.5417 | 21350 | 1.4009 | - |
| 2.5476 | 21400 | 1.4589 | - |
| 2.5536 | 21450 | 1.4478 | - |
| 2.5595 | 21500 | 1.4876 | - |
| 2.5655 | 21550 | 1.4206 | - |
| 2.5714 | 21600 | 1.4927 | - |
| 2.5774 | 21650 | 1.5047 | - |
| 2.5833 | 21700 | 1.3988 | - |
| 2.5893 | 21750 | 1.4714 | - |
| 2.5952 | 21800 | 1.3605 | - |
| 2.6012 | 21850 | 1.5635 | - |
| 2.6071 | 21900 | 1.4678 | - |
| 2.6131 | 21950 | 1.4618 | - |
| 2.6190 | 22000 | 1.4407 | - |
| 2.625 | 22050 | 1.5451 | - |
| 2.6310 | 22100 | 1.4844 | - |
| 2.6369 | 22150 | 1.4088 | - |
| 2.6429 | 22200 | 1.5056 | - |
| 2.6488 | 22250 | 1.4678 | - |
| 2.6548 | 22300 | 1.4262 | - |
| 2.6607 | 22350 | 1.4492 | - |
| 2.6667 | 22400 | 1.4463 | - |
| 2.6726 | 22450 | 1.3851 | - |
| 2.6786 | 22500 | 1.513 | - |
| 2.6845 | 22550 | 1.45 | - |
| 2.6905 | 22600 | 1.4382 | - |
| 2.6964 | 22650 | 1.4637 | - |
| 2.7024 | 22700 | 1.4487 | - |
| 2.7083 | 22750 | 1.4507 | - |
| 2.7143 | 22800 | 1.5065 | - |
| 2.7202 | 22850 | 1.4116 | - |
| 2.7262 | 22900 | 1.479 | - |
| 2.7321 | 22950 | 1.444 | - |
| 2.7381 | 23000 | 1.4056 | - |
| 2.7440 | 23050 | 1.3913 | - |
| 2.75 | 23100 | 1.5108 | - |
| 2.7560 | 23150 | 1.4092 | - |
| 2.7619 | 23200 | 1.4341 | - |
| 2.7679 | 23250 | 1.4274 | - |
| 2.7738 | 23300 | 1.4748 | - |
| 2.7798 | 23350 | 1.3819 | - |
| 2.7857 | 23400 | 1.5012 | - |
| 2.7917 | 23450 | 1.3594 | - |
| 2.7976 | 23500 | 1.4708 | - |
| 2.8036 | 23550 | 1.4425 | - |
| 2.8095 | 23600 | 1.3566 | - |
| 2.8155 | 23650 | 1.456 | - |
| 2.8214 | 23700 | 1.5937 | - |
| 2.8274 | 23750 | 1.3835 | - |
| 2.8333 | 23800 | 1.4137 | - |
| 2.8393 | 23850 | 1.3861 | - |
| 2.8452 | 23900 | 1.4249 | - |
| 2.8512 | 23950 | 1.3599 | - |
| 2.8571 | 24000 | 1.4789 | - |
| 2.8631 | 24050 | 1.4527 | - |
| 2.8690 | 24100 | 1.4406 | - |
| 2.875 | 24150 | 1.4301 | - |
| 2.8810 | 24200 | 1.4059 | - |
| 2.8869 | 24250 | 1.5052 | - |
| 2.8929 | 24300 | 1.4429 | - |
| 2.8988 | 24350 | 1.5183 | - |
| 2.9048 | 24400 | 1.4288 | - |
| 2.9107 | 24450 | 1.4673 | - |
| 2.9167 | 24500 | 1.4582 | - |
| 2.9226 | 24550 | 1.4792 | - |
| 2.9286 | 24600 | 1.4598 | - |
| 2.9345 | 24650 | 1.4785 | - |
| 2.9405 | 24700 | 1.4259 | - |
| 2.9464 | 24750 | 1.4877 | - |
| 2.9524 | 24800 | 1.5162 | - |
| 2.9583 | 24850 | 1.4854 | - |
| 2.9643 | 24900 | 1.3679 | - |
| 2.9702 | 24950 | 1.3985 | - |
| 2.9762 | 25000 | 1.421 | - |
| 2.9821 | 25050 | 1.5048 | - |
| 2.9881 | 25100 | 1.4618 | - |
| 2.9940 | 25150 | 1.5061 | - |
| 3.0 | 25200 | 1.3634 | - |
### Framework Versions
- Python: 3.12.0
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.0+cpu
- Datasets: 3.0.2
- Tokenizers: 0.20.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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