---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'pay rs.20.00 / c 91xx3402 ganeshramkudisodebur 22 - 09 - 2023 . ref:3648483126
. query ? click http://m.paytm.me/care : ppbl'
- text: inform m / s shree salasar balaji tex transfer rs . 10000.00 account . xxxxxxxx2869
yes bank account rtgs / neft / imp
- text: undelivered!\nyour hdfc bank debit card 9875 / c 8494\nreason ch shift . case
address change , update seamless card delivery > > hdfcbk.io/a/0nzoo052
- text: rs 5000.00 debit / c upi 23 - 09 - 2023 14:21:12 vpa 35890012004230@cnrb -
( upi ref 363290511260)-federal bank
- text: 472448 otp set hdfc bank 4 digit login pin . share otp you?call 18002586161
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit 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: 0.9715909090909091
name: Accuracy
---
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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.
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/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
- **Maximum Sequence Length:** 384 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2 | - '840989 otp proceed canara bank mobile banking . valid 15 minute . share otp . - canara bank . kbl8a1ju0mt'
- 'cheque . 000102 issue riya collection rs . 12,000.00 present / c xxxxx546157 return unpaid insufficient fund . team idfc bank'
- 'avl bal / c xxxx0959 10 - jul-2022 06:06:24 inr 0.00 . combine avl bal inr 0.00 . use mb app track / c - kotak bank'
|
| 0 | - '/ c . xxxxxxxx7146 debit rs.11933.00 16 - 09 - 23 / c xxxxxxxx4716 credit ( imp ref 325908759095 ) . warm regard , yes bank'
- 'send rs.290.00 kotak bank ac x4524 bharatpe90727843812@yesbankltd 13-10-23.upi ref 328684167136 . , kotak.com/fraud'
- 'rs.295 transfer / c ... 4322 : lien_marking_fo . total bal : rs.188.8cr . avlbl amt : rs.609.97(28 - 06 - 2022 16:39:53 ) - bank baroda'
|
| 1 | - 'rs 15000credite / c xx4524via neft neofirst technology india private- utr ref hsbcn23276508097 ; avail . bal.:rs 215180.62kotak bank'
- '/ c : xx6775 credit rs.60.00 14 - 11 - 2023 10:47:49 upi - id 8733076955@omni ( upi ref 331800008439).-canara bank'
- 'rs.28 credit / c ... 7783 upi/323962847509 kiwicashback_ax . total bal : rs.122751.36cr . avlbl amt : rs.94671.36(27 - 08 - 2023 15:37:01 ) - bank baroda'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9716 |
## 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("vipinbansal179/SetFit_sms_Analyzer1")
# Run inference
preds = model("472448 otp set hdfc bank 4 digit login pin . share otp you?call 18002586161")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 23.17 | 65 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 231 |
| 1 | 131 |
| 2 | 338 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:---------:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.2945 | - |
| 0.0026 | 50 | 0.3574 | - |
| 0.0052 | 100 | 0.2512 | - |
| 0.0079 | 150 | 0.2319 | - |
| 0.0105 | 200 | 0.2787 | - |
| 0.0131 | 250 | 0.2129 | - |
| 0.0157 | 300 | 0.2189 | - |
| 0.0183 | 350 | 0.0857 | - |
| 0.0210 | 400 | 0.0932 | - |
| 0.0236 | 450 | 0.065 | - |
| 0.0262 | 500 | 0.0553 | - |
| 0.0288 | 550 | 0.0674 | - |
| 0.0314 | 600 | 0.0239 | - |
| 0.0341 | 650 | 0.0054 | - |
| 0.0367 | 700 | 0.0025 | - |
| 0.0393 | 750 | 0.002 | - |
| 0.0419 | 800 | 0.0007 | - |
| 0.0446 | 850 | 0.001 | - |
| 0.0472 | 900 | 0.0008 | - |
| 0.0498 | 950 | 0.0008 | - |
| 0.0524 | 1000 | 0.0003 | - |
| 0.0550 | 1050 | 0.0012 | - |
| 0.0577 | 1100 | 0.002 | - |
| 0.0603 | 1150 | 0.0192 | - |
| 0.0629 | 1200 | 0.0041 | - |
| 0.0655 | 1250 | 0.0002 | - |
| 0.0681 | 1300 | 0.0001 | - |
| 0.0708 | 1350 | 0.0001 | - |
| 0.0734 | 1400 | 0.0001 | - |
| 0.0760 | 1450 | 0.0004 | - |
| 0.0786 | 1500 | 0.0003 | - |
| 0.0812 | 1550 | 0.0002 | - |
| 0.0839 | 1600 | 0.0004 | - |
| 0.0865 | 1650 | 0.0002 | - |
| 0.0891 | 1700 | 0.0002 | - |
| 0.0917 | 1750 | 0.0001 | - |
| 0.0943 | 1800 | 0.0001 | - |
| 0.0970 | 1850 | 0.0001 | - |
| 0.0996 | 1900 | 0.0001 | - |
| 0.1022 | 1950 | 0.0001 | - |
| 0.1048 | 2000 | 0.0001 | - |
| 0.1075 | 2050 | 0.0015 | - |
| 0.1101 | 2100 | 0.0001 | - |
| 0.1127 | 2150 | 0.0001 | - |
| 0.1153 | 2200 | 0.0001 | - |
| 0.1179 | 2250 | 0.0001 | - |
| 0.1206 | 2300 | 0.0 | - |
| 0.1232 | 2350 | 0.0001 | - |
| 0.1258 | 2400 | 0.0 | - |
| 0.1284 | 2450 | 0.0001 | - |
| 0.1310 | 2500 | 0.0 | - |
| 0.1337 | 2550 | 0.0001 | - |
| 0.1363 | 2600 | 0.0 | - |
| 0.1389 | 2650 | 0.0001 | - |
| 0.1415 | 2700 | 0.0 | - |
| 0.1441 | 2750 | 0.0 | - |
| 0.1468 | 2800 | 0.0 | - |
| 0.1494 | 2850 | 0.0 | - |
| 0.1520 | 2900 | 0.0 | - |
| 0.1546 | 2950 | 0.0 | - |
| 0.1572 | 3000 | 0.0 | - |
| 0.1599 | 3050 | 0.0 | - |
| 0.1625 | 3100 | 0.0 | - |
| 0.1651 | 3150 | 0.0 | - |
| 0.1677 | 3200 | 0.0 | - |
| 0.1704 | 3250 | 0.0 | - |
| 0.1730 | 3300 | 0.0 | - |
| 0.1756 | 3350 | 0.0 | - |
| 0.1782 | 3400 | 0.0 | - |
| 0.1808 | 3450 | 0.0 | - |
| 0.1835 | 3500 | 0.0 | - |
| 0.1861 | 3550 | 0.0003 | - |
| 0.1887 | 3600 | 0.0131 | - |
| 0.1913 | 3650 | 0.0004 | - |
| 0.1939 | 3700 | 0.0001 | - |
| 0.1966 | 3750 | 0.0 | - |
| 0.1992 | 3800 | 0.0001 | - |
| 0.2018 | 3850 | 0.0002 | - |
| 0.2044 | 3900 | 0.0 | - |
| 0.2070 | 3950 | 0.0 | - |
| 0.2097 | 4000 | 0.0001 | - |
| 0.2123 | 4050 | 0.0015 | - |
| 0.2149 | 4100 | 0.0002 | - |
| 0.2175 | 4150 | 0.0 | - |
| 0.2201 | 4200 | 0.0 | - |
| 0.2228 | 4250 | 0.0 | - |
| 0.2254 | 4300 | 0.0 | - |
| 0.2280 | 4350 | 0.0 | - |
| 0.2306 | 4400 | 0.0 | - |
| 0.2333 | 4450 | 0.0 | - |
| 0.2359 | 4500 | 0.0 | - |
| 0.2385 | 4550 | 0.0 | - |
| 0.2411 | 4600 | 0.0 | - |
| 0.2437 | 4650 | 0.0 | - |
| 0.2464 | 4700 | 0.0 | - |
| 0.2490 | 4750 | 0.0 | - |
| 0.2516 | 4800 | 0.0 | - |
| 0.2542 | 4850 | 0.0 | - |
| 0.2568 | 4900 | 0.0 | - |
| 0.2595 | 4950 | 0.0 | - |
| 0.2621 | 5000 | 0.0 | - |
| 0.2647 | 5050 | 0.0 | - |
| 0.2673 | 5100 | 0.0 | - |
| 0.2699 | 5150 | 0.0 | - |
| 0.2726 | 5200 | 0.0 | - |
| 0.2752 | 5250 | 0.0 | - |
| 0.2778 | 5300 | 0.0 | - |
| 0.2804 | 5350 | 0.0 | - |
| 0.2830 | 5400 | 0.0 | - |
| 0.2857 | 5450 | 0.0 | - |
| 0.2883 | 5500 | 0.0 | - |
| 0.2909 | 5550 | 0.0 | - |
| 0.2935 | 5600 | 0.0 | - |
| 0.2962 | 5650 | 0.0 | - |
| 0.2988 | 5700 | 0.0 | - |
| 0.3014 | 5750 | 0.0 | - |
| 0.3040 | 5800 | 0.0 | - |
| 0.3066 | 5850 | 0.0 | - |
| 0.3093 | 5900 | 0.0 | - |
| 0.3119 | 5950 | 0.0 | - |
| 0.3145 | 6000 | 0.0 | - |
| 0.3171 | 6050 | 0.0 | - |
| 0.3197 | 6100 | 0.0 | - |
| 0.3224 | 6150 | 0.0 | - |
| 0.3250 | 6200 | 0.0 | - |
| 0.3276 | 6250 | 0.0 | - |
| 0.3302 | 6300 | 0.0 | - |
| 0.3328 | 6350 | 0.0 | - |
| 0.3355 | 6400 | 0.0 | - |
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| 0.3407 | 6500 | 0.0 | - |
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| 0.3591 | 6850 | 0.0 | - |
| 0.3617 | 6900 | 0.0 | - |
| 0.3643 | 6950 | 0.0 | - |
| 0.3669 | 7000 | 0.0 | - |
| 0.3695 | 7050 | 0.0 | - |
| 0.3722 | 7100 | 0.0 | - |
| 0.3748 | 7150 | 0.0 | - |
| 0.3774 | 7200 | 0.0 | - |
| 0.3800 | 7250 | 0.0 | - |
| 0.3826 | 7300 | 0.0 | - |
| 0.3853 | 7350 | 0.0 | - |
| 0.3879 | 7400 | 0.0 | - |
| 0.3905 | 7450 | 0.0 | - |
| 0.3931 | 7500 | 0.0 | - |
| 0.3957 | 7550 | 0.0 | - |
| 0.3984 | 7600 | 0.0 | - |
| 0.4010 | 7650 | 0.0 | - |
| 0.4036 | 7700 | 0.0 | - |
| 0.4062 | 7750 | 0.0 | - |
| 0.4088 | 7800 | 0.0 | - |
| 0.4115 | 7850 | 0.0 | - |
| 0.4141 | 7900 | 0.0 | - |
| 0.4167 | 7950 | 0.0 | - |
| 0.4193 | 8000 | 0.0 | - |
| 0.4220 | 8050 | 0.0 | - |
| 0.4246 | 8100 | 0.0 | - |
| 0.4272 | 8150 | 0.0 | - |
| 0.4298 | 8200 | 0.0 | - |
| 0.4324 | 8250 | 0.0 | - |
| 0.4351 | 8300 | 0.0 | - |
| 0.4377 | 8350 | 0.0 | - |
| 0.4403 | 8400 | 0.0 | - |
| 0.4429 | 8450 | 0.0 | - |
| 0.4455 | 8500 | 0.0 | - |
| 0.4482 | 8550 | 0.0 | - |
| 0.4508 | 8600 | 0.0 | - |
| 0.4534 | 8650 | 0.0 | - |
| 0.4560 | 8700 | 0.0 | - |
| 0.4586 | 8750 | 0.0 | - |
| 0.4613 | 8800 | 0.0 | - |
| 0.4639 | 8850 | 0.0 | - |
| 0.4665 | 8900 | 0.0 | - |
| 0.4691 | 8950 | 0.0001 | - |
| 0.4717 | 9000 | 0.0 | - |
| 0.4744 | 9050 | 0.0 | - |
| 0.4770 | 9100 | 0.0 | - |
| 0.4796 | 9150 | 0.0 | - |
| 0.4822 | 9200 | 0.0 | - |
| 0.4849 | 9250 | 0.0 | - |
| 0.4875 | 9300 | 0.0 | - |
| 0.4901 | 9350 | 0.0 | - |
| 0.4927 | 9400 | 0.0 | - |
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| 0.5163 | 9850 | 0.0 | - |
| 0.5189 | 9900 | 0.0 | - |
| 0.5215 | 9950 | 0.0 | - |
| 0.5242 | 10000 | 0.0 | - |
| 0.5268 | 10050 | 0.0 | - |
| 0.5294 | 10100 | 0.0 | - |
| 0.5320 | 10150 | 0.0 | - |
| 0.5346 | 10200 | 0.0 | - |
| 0.5373 | 10250 | 0.0 | - |
| 0.5399 | 10300 | 0.0 | - |
| 0.5425 | 10350 | 0.0 | - |
| 0.5451 | 10400 | 0.0 | - |
| 0.5478 | 10450 | 0.0 | - |
| 0.5504 | 10500 | 0.0 | - |
| 0.5530 | 10550 | 0.0 | - |
| 0.5556 | 10600 | 0.0 | - |
| 0.5582 | 10650 | 0.0 | - |
| 0.5609 | 10700 | 0.0 | - |
| 0.5635 | 10750 | 0.0 | - |
| 0.5661 | 10800 | 0.0 | - |
| 0.5687 | 10850 | 0.0 | - |
| 0.5713 | 10900 | 0.0 | - |
| 0.5740 | 10950 | 0.0 | - |
| 0.5766 | 11000 | 0.0 | - |
| 0.5792 | 11050 | 0.0 | - |
| 0.5818 | 11100 | 0.0 | - |
| 0.5844 | 11150 | 0.0 | - |
| 0.5871 | 11200 | 0.0 | - |
| 0.5897 | 11250 | 0.0 | - |
| 0.5923 | 11300 | 0.0 | - |
| 0.5949 | 11350 | 0.0 | - |
| 0.5975 | 11400 | 0.0 | - |
| 0.6002 | 11450 | 0.0 | - |
| 0.6028 | 11500 | 0.0 | - |
| 0.6054 | 11550 | 0.0 | - |
| 0.6080 | 11600 | 0.0 | - |
| 0.6107 | 11650 | 0.0 | - |
| 0.6133 | 11700 | 0.0 | - |
| 0.6159 | 11750 | 0.0 | - |
| 0.6185 | 11800 | 0.0 | - |
| 0.6211 | 11850 | 0.0 | - |
| 0.6238 | 11900 | 0.0 | - |
| 0.6264 | 11950 | 0.0 | - |
| 0.6290 | 12000 | 0.0 | - |
| 0.6316 | 12050 | 0.0 | - |
| 0.6342 | 12100 | 0.0 | - |
| 0.6369 | 12150 | 0.0 | - |
| 0.6395 | 12200 | 0.0 | - |
| 0.6421 | 12250 | 0.0 | - |
| 0.6447 | 12300 | 0.0 | - |
| 0.6473 | 12350 | 0.0 | - |
| 0.6500 | 12400 | 0.0 | - |
| 0.6526 | 12450 | 0.0 | - |
| 0.6552 | 12500 | 0.0 | - |
| 0.6578 | 12550 | 0.0 | - |
| 0.6604 | 12600 | 0.0 | - |
| 0.6631 | 12650 | 0.0 | - |
| 0.6657 | 12700 | 0.0 | - |
| 0.6683 | 12750 | 0.0 | - |
| 0.6709 | 12800 | 0.0 | - |
| 0.6736 | 12850 | 0.0 | - |
| 0.6762 | 12900 | 0.0 | - |
| 0.6788 | 12950 | 0.0 | - |
| 0.6814 | 13000 | 0.0 | - |
| 0.6840 | 13050 | 0.0 | - |
| 0.6867 | 13100 | 0.0 | - |
| 0.6893 | 13150 | 0.0 | - |
| 0.6919 | 13200 | 0.0 | - |
| 0.6945 | 13250 | 0.0 | - |
| 0.6971 | 13300 | 0.0 | - |
| 0.6998 | 13350 | 0.0 | - |
| 0.7024 | 13400 | 0.0 | - |
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| 0.7417 | 14150 | 0.0 | - |
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| 0.7522 | 14350 | 0.0 | - |
| 0.7548 | 14400 | 0.0 | - |
| 0.7574 | 14450 | 0.0 | - |
| 0.7600 | 14500 | 0.0 | - |
| 0.7627 | 14550 | 0.0 | - |
| 0.7653 | 14600 | 0.0 | - |
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| 0.7705 | 14700 | 0.0 | - |
| 0.7731 | 14750 | 0.0 | - |
| 0.7758 | 14800 | 0.0 | - |
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| 0.7810 | 14900 | 0.0 | - |
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| 0.7915 | 15100 | 0.0 | - |
| 0.7941 | 15150 | 0.0 | - |
| 0.7967 | 15200 | 0.0 | - |
| 0.7994 | 15250 | 0.0 | - |
| 0.8020 | 15300 | 0.0 | - |
| 0.8046 | 15350 | 0.0 | - |
| 0.8072 | 15400 | 0.0 | - |
| 0.8098 | 15450 | 0.0 | - |
| 0.8125 | 15500 | 0.0 | - |
| 0.8151 | 15550 | 0.0 | - |
| 0.8177 | 15600 | 0.0 | - |
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| 0.8229 | 15700 | 0.0 | - |
| 0.8256 | 15750 | 0.0 | - |
| 0.8282 | 15800 | 0.0 | - |
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| 0.8334 | 15900 | 0.0 | - |
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| 0.8387 | 16000 | 0.0 | - |
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| 0.8439 | 16100 | 0.0 | - |
| 0.8465 | 16150 | 0.0 | - |
| 0.8491 | 16200 | 0.0 | - |
| 0.8518 | 16250 | 0.0 | - |
| 0.8544 | 16300 | 0.0 | - |
| 0.8570 | 16350 | 0.0 | - |
| 0.8596 | 16400 | 0.0 | - |
| 0.8622 | 16450 | 0.0 | - |
| 0.8649 | 16500 | 0.0 | - |
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| 0.8701 | 16600 | 0.0 | - |
| 0.8727 | 16650 | 0.0 | - |
| 0.8754 | 16700 | 0.0 | - |
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| 0.8832 | 16850 | 0.0 | - |
| 0.8858 | 16900 | 0.0 | - |
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| 0.8963 | 17100 | 0.0 | - |
| 0.8989 | 17150 | 0.0 | - |
| 0.9016 | 17200 | 0.0 | - |
| 0.9042 | 17250 | 0.0 | - |
| 0.9068 | 17300 | 0.0 | - |
| 0.9094 | 17350 | 0.0 | - |
| 0.9120 | 17400 | 0.0 | - |
| 0.9147 | 17450 | 0.0 | - |
| 0.9173 | 17500 | 0.0 | - |
| 0.9199 | 17550 | 0.0 | - |
| 0.9225 | 17600 | 0.0 | - |
| 0.9251 | 17650 | 0.0 | - |
| 0.9278 | 17700 | 0.0 | - |
| 0.9304 | 17750 | 0.0 | - |
| 0.9330 | 17800 | 0.0 | - |
| 0.9356 | 17850 | 0.0 | - |
| 0.9383 | 17900 | 0.0 | - |
| 0.9409 | 17950 | 0.0 | - |
| 0.9435 | 18000 | 0.0 | - |
| 0.9461 | 18050 | 0.0 | - |
| 0.9487 | 18100 | 0.0 | - |
| 0.9514 | 18150 | 0.0 | - |
| 0.9540 | 18200 | 0.0 | - |
| 0.9566 | 18250 | 0.0 | - |
| 0.9592 | 18300 | 0.0 | - |
| 0.9618 | 18350 | 0.0 | - |
| 0.9645 | 18400 | 0.0 | - |
| 0.9671 | 18450 | 0.0 | - |
| 0.9697 | 18500 | 0.0 | - |
| 0.9723 | 18550 | 0.0 | - |
| 0.9749 | 18600 | 0.0 | - |
| 0.9776 | 18650 | 0.0 | - |
| 0.9802 | 18700 | 0.0 | - |
| 0.9828 | 18750 | 0.0 | - |
| 0.9854 | 18800 | 0.0 | - |
| 0.9880 | 18850 | 0.0 | - |
| 0.9907 | 18900 | 0.0 | - |
| 0.9933 | 18950 | 0.0 | - |
| 0.9959 | 19000 | 0.0 | - |
| 0.9985 | 19050 | 0.0 | - |
| **1.0** | **19078** | **-** | **0.0437** |
| 1.0012 | 19100 | 0.0 | - |
| 1.0038 | 19150 | 0.0 | - |
| 1.0064 | 19200 | 0.0 | - |
| 1.0090 | 19250 | 0.0 | - |
| 1.0116 | 19300 | 0.0 | - |
| 1.0143 | 19350 | 0.0 | - |
| 1.0169 | 19400 | 0.0 | - |
| 1.0195 | 19450 | 0.3698 | - |
| 1.0221 | 19500 | 0.1546 | - |
| 1.0247 | 19550 | 0.0179 | - |
| 1.0274 | 19600 | 0.0004 | - |
| 1.0300 | 19650 | 0.0005 | - |
| 1.0326 | 19700 | 0.0 | - |
| 1.0352 | 19750 | 0.0002 | - |
| 1.0378 | 19800 | 0.0 | - |
| 1.0405 | 19850 | 0.0 | - |
| 1.0431 | 19900 | 0.0 | - |
| 1.0457 | 19950 | 0.0002 | - |
| 1.0483 | 20000 | 0.0011 | - |
| 1.0509 | 20050 | 0.0 | - |
| 1.0536 | 20100 | 0.0 | - |
| 1.0562 | 20150 | 0.0 | - |
| 1.0588 | 20200 | 0.0003 | - |
| 1.0614 | 20250 | 0.0 | - |
| 1.0641 | 20300 | 0.0003 | - |
| 1.0667 | 20350 | 0.0003 | - |
| 1.0693 | 20400 | 0.0 | - |
| 1.0719 | 20450 | 0.0 | - |
| 1.0745 | 20500 | 0.0 | - |
| 1.0772 | 20550 | 0.0 | - |
| 1.0798 | 20600 | 0.0 | - |
| 1.0824 | 20650 | 0.0 | - |
| 1.0850 | 20700 | 0.0 | - |
| 1.0876 | 20750 | 0.0 | - |
| 1.0903 | 20800 | 0.0 | - |
| 1.0929 | 20850 | 0.0 | - |
| 1.0955 | 20900 | 0.0 | - |
| 1.0981 | 20950 | 0.0 | - |
| 1.1007 | 21000 | 0.0 | - |
| 1.1034 | 21050 | 0.0 | - |
| 1.1060 | 21100 | 0.0 | - |
| 1.1086 | 21150 | 0.0 | - |
| 1.1112 | 21200 | 0.0 | - |
| 1.1138 | 21250 | 0.0 | - |
| 1.1165 | 21300 | 0.0 | - |
| 1.1191 | 21350 | 0.0 | - |
| 1.1217 | 21400 | 0.0 | - |
| 1.1243 | 21450 | 0.0 | - |
| 1.1270 | 21500 | 0.0 | - |
| 1.1296 | 21550 | 0.0 | - |
| 1.1322 | 21600 | 0.0 | - |
| 1.1348 | 21650 | 0.0 | - |
| 1.1374 | 21700 | 0.0 | - |
| 1.1401 | 21750 | 0.0 | - |
| 1.1427 | 21800 | 0.0 | - |
| 1.1453 | 21850 | 0.0 | - |
| 1.1479 | 21900 | 0.0 | - |
| 1.1505 | 21950 | 0.0 | - |
| 1.1532 | 22000 | 0.0 | - |
| 1.1558 | 22050 | 0.0 | - |
| 1.1584 | 22100 | 0.0 | - |
| 1.1610 | 22150 | 0.0 | - |
| 1.1636 | 22200 | 0.0 | - |
| 1.1663 | 22250 | 0.0 | - |
| 1.1689 | 22300 | 0.0 | - |
| 1.1715 | 22350 | 0.0 | - |
| 1.1741 | 22400 | 0.0 | - |
| 1.1767 | 22450 | 0.0 | - |
| 1.1794 | 22500 | 0.0 | - |
| 1.1820 | 22550 | 0.0 | - |
| 1.1846 | 22600 | 0.0 | - |
| 1.1872 | 22650 | 0.0 | - |
| 1.1899 | 22700 | 0.0 | - |
| 1.1925 | 22750 | 0.0 | - |
| 1.1951 | 22800 | 0.0 | - |
| 1.1977 | 22850 | 0.0 | - |
| 1.2003 | 22900 | 0.0 | - |
| 1.2030 | 22950 | 0.0 | - |
| 1.2056 | 23000 | 0.0 | - |
| 1.2082 | 23050 | 0.0 | - |
| 1.2108 | 23100 | 0.0 | - |
| 1.2134 | 23150 | 0.0 | - |
| 1.2161 | 23200 | 0.0 | - |
| 1.2187 | 23250 | 0.0 | - |
| 1.2213 | 23300 | 0.0 | - |
| 1.2239 | 23350 | 0.0 | - |
| 1.2265 | 23400 | 0.0 | - |
| 1.2292 | 23450 | 0.0 | - |
| 1.2318 | 23500 | 0.0 | - |
| 1.2344 | 23550 | 0.0 | - |
| 1.2370 | 23600 | 0.0 | - |
| 1.2396 | 23650 | 0.0 | - |
| 1.2423 | 23700 | 0.0 | - |
| 1.2449 | 23750 | 0.0 | - |
| 1.2475 | 23800 | 0.0 | - |
| 1.2501 | 23850 | 0.0 | - |
| 1.2528 | 23900 | 0.0 | - |
| 1.2554 | 23950 | 0.0 | - |
| 1.2580 | 24000 | 0.0 | - |
| 1.2606 | 24050 | 0.0 | - |
| 1.2632 | 24100 | 0.0 | - |
| 1.2659 | 24150 | 0.0 | - |
| 1.2685 | 24200 | 0.0 | - |
| 1.2711 | 24250 | 0.0 | - |
| 1.2737 | 24300 | 0.0 | - |
| 1.2763 | 24350 | 0.0 | - |
| 1.2790 | 24400 | 0.0 | - |
| 1.2816 | 24450 | 0.0 | - |
| 1.2842 | 24500 | 0.0 | - |
| 1.2868 | 24550 | 0.0 | - |
| 1.2894 | 24600 | 0.0 | - |
| 1.2921 | 24650 | 0.0 | - |
| 1.2947 | 24700 | 0.0 | - |
| 1.2973 | 24750 | 0.0 | - |
| 1.2999 | 24800 | 0.0 | - |
| 1.3025 | 24850 | 0.0 | - |
| 1.3052 | 24900 | 0.0 | - |
| 1.3078 | 24950 | 0.0 | - |
| 1.3104 | 25000 | 0.0 | - |
| 1.3130 | 25050 | 0.0 | - |
| 1.3157 | 25100 | 0.0 | - |
| 1.3183 | 25150 | 0.0 | - |
| 1.3209 | 25200 | 0.0 | - |
| 1.3235 | 25250 | 0.0 | - |
| 1.3261 | 25300 | 0.0 | - |
| 1.3288 | 25350 | 0.0 | - |
| 1.3314 | 25400 | 0.0 | - |
| 1.3340 | 25450 | 0.0 | - |
| 1.3366 | 25500 | 0.0 | - |
| 1.3392 | 25550 | 0.0 | - |
| 1.3419 | 25600 | 0.0 | - |
| 1.3445 | 25650 | 0.0 | - |
| 1.3471 | 25700 | 0.0 | - |
| 1.3497 | 25750 | 0.0 | - |
| 1.3523 | 25800 | 0.0 | - |
| 1.3550 | 25850 | 0.0 | - |
| 1.3576 | 25900 | 0.0 | - |
| 1.3602 | 25950 | 0.0 | - |
| 1.3628 | 26000 | 0.0 | - |
| 1.3654 | 26050 | 0.0 | - |
| 1.3681 | 26100 | 0.0 | - |
| 1.3707 | 26150 | 0.0 | - |
| 1.3733 | 26200 | 0.0 | - |
| 1.3759 | 26250 | 0.0 | - |
| 1.3786 | 26300 | 0.0 | - |
| 1.3812 | 26350 | 0.0 | - |
| 1.3838 | 26400 | 0.0 | - |
| 1.3864 | 26450 | 0.0 | - |
| 1.3890 | 26500 | 0.0 | - |
| 1.3917 | 26550 | 0.0 | - |
| 1.3943 | 26600 | 0.0 | - |
| 1.3969 | 26650 | 0.0 | - |
| 1.3995 | 26700 | 0.0 | - |
| 1.4021 | 26750 | 0.0 | - |
| 1.4048 | 26800 | 0.0 | - |
| 1.4074 | 26850 | 0.0 | - |
| 1.4100 | 26900 | 0.0 | - |
| 1.4126 | 26950 | 0.0 | - |
| 1.4152 | 27000 | 0.0 | - |
| 1.4179 | 27050 | 0.0 | - |
| 1.4205 | 27100 | 0.0 | - |
| 1.4231 | 27150 | 0.0 | - |
| 1.4257 | 27200 | 0.0 | - |
| 1.4283 | 27250 | 0.0 | - |
| 1.4310 | 27300 | 0.0 | - |
| 1.4336 | 27350 | 0.0 | - |
| 1.4362 | 27400 | 0.0 | - |
| 1.4388 | 27450 | 0.0 | - |
| 1.4415 | 27500 | 0.0 | - |
| 1.4441 | 27550 | 0.0 | - |
| 1.4467 | 27600 | 0.0 | - |
| 1.4493 | 27650 | 0.0 | - |
| 1.4519 | 27700 | 0.0 | - |
| 1.4546 | 27750 | 0.0 | - |
| 1.4572 | 27800 | 0.0 | - |
| 1.4598 | 27850 | 0.0 | - |
| 1.4624 | 27900 | 0.0 | - |
| 1.4650 | 27950 | 0.0 | - |
| 1.4677 | 28000 | 0.0 | - |
| 1.4703 | 28050 | 0.0 | - |
| 1.4729 | 28100 | 0.0 | - |
| 1.4755 | 28150 | 0.0 | - |
| 1.4781 | 28200 | 0.0 | - |
| 1.4808 | 28250 | 0.0 | - |
| 1.4834 | 28300 | 0.0 | - |
| 1.4860 | 28350 | 0.0 | - |
| 1.4886 | 28400 | 0.0 | - |
| 1.4912 | 28450 | 0.0 | - |
| 1.4939 | 28500 | 0.0 | - |
| 1.4965 | 28550 | 0.0 | - |
| 1.4991 | 28600 | 0.0 | - |
| 1.5017 | 28650 | 0.0 | - |
| 1.5044 | 28700 | 0.0 | - |
| 1.5070 | 28750 | 0.0 | - |
| 1.5096 | 28800 | 0.0 | - |
| 1.5122 | 28850 | 0.0 | - |
| 1.5148 | 28900 | 0.0 | - |
| 1.5175 | 28950 | 0.0 | - |
| 1.5201 | 29000 | 0.0 | - |
| 1.5227 | 29050 | 0.0 | - |
| 1.5253 | 29100 | 0.0 | - |
| 1.5279 | 29150 | 0.0 | - |
| 1.5306 | 29200 | 0.0 | - |
| 1.5332 | 29250 | 0.0 | - |
| 1.5358 | 29300 | 0.0 | - |
| 1.5384 | 29350 | 0.0 | - |
| 1.5410 | 29400 | 0.0 | - |
| 1.5437 | 29450 | 0.0 | - |
| 1.5463 | 29500 | 0.0 | - |
| 1.5489 | 29550 | 0.0 | - |
| 1.5515 | 29600 | 0.0 | - |
| 1.5541 | 29650 | 0.0 | - |
| 1.5568 | 29700 | 0.0 | - |
| 1.5594 | 29750 | 0.0 | - |
| 1.5620 | 29800 | 0.0 | - |
| 1.5646 | 29850 | 0.0 | - |
| 1.5673 | 29900 | 0.0 | - |
| 1.5699 | 29950 | 0.0 | - |
| 1.5725 | 30000 | 0.0 | - |
| 1.5751 | 30050 | 0.0 | - |
| 1.5777 | 30100 | 0.0 | - |
| 1.5804 | 30150 | 0.0 | - |
| 1.5830 | 30200 | 0.0 | - |
| 1.5856 | 30250 | 0.0 | - |
| 1.5882 | 30300 | 0.0 | - |
| 1.5908 | 30350 | 0.0 | - |
| 1.5935 | 30400 | 0.0 | - |
| 1.5961 | 30450 | 0.0 | - |
| 1.5987 | 30500 | 0.0 | - |
| 1.6013 | 30550 | 0.0 | - |
| 1.6039 | 30600 | 0.0 | - |
| 1.6066 | 30650 | 0.0 | - |
| 1.6092 | 30700 | 0.0 | - |
| 1.6118 | 30750 | 0.0 | - |
| 1.6144 | 30800 | 0.0 | - |
| 1.6170 | 30850 | 0.0 | - |
| 1.6197 | 30900 | 0.0 | - |
| 1.6223 | 30950 | 0.0 | - |
| 1.6249 | 31000 | 0.0 | - |
| 1.6275 | 31050 | 0.0 | - |
| 1.6301 | 31100 | 0.0 | - |
| 1.6328 | 31150 | 0.0 | - |
| 1.6354 | 31200 | 0.0 | - |
| 1.6380 | 31250 | 0.0 | - |
| 1.6406 | 31300 | 0.0 | - |
| 1.6433 | 31350 | 0.0 | - |
| 1.6459 | 31400 | 0.0 | - |
| 1.6485 | 31450 | 0.0 | - |
| 1.6511 | 31500 | 0.0 | - |
| 1.6537 | 31550 | 0.0 | - |
| 1.6564 | 31600 | 0.0 | - |
| 1.6590 | 31650 | 0.0 | - |
| 1.6616 | 31700 | 0.0 | - |
| 1.6642 | 31750 | 0.0 | - |
| 1.6668 | 31800 | 0.0 | - |
| 1.6695 | 31850 | 0.0 | - |
| 1.6721 | 31900 | 0.0 | - |
| 1.6747 | 31950 | 0.0 | - |
| 1.6773 | 32000 | 0.0 | - |
| 1.6799 | 32050 | 0.0 | - |
| 1.6826 | 32100 | 0.0 | - |
| 1.6852 | 32150 | 0.0 | - |
| 1.6878 | 32200 | 0.0 | - |
| 1.6904 | 32250 | 0.0 | - |
| 1.6930 | 32300 | 0.0 | - |
| 1.6957 | 32350 | 0.0 | - |
| 1.6983 | 32400 | 0.0 | - |
| 1.7009 | 32450 | 0.0 | - |
| 1.7035 | 32500 | 0.0 | - |
| 1.7062 | 32550 | 0.0 | - |
| 1.7088 | 32600 | 0.0 | - |
| 1.7114 | 32650 | 0.0 | - |
| 1.7140 | 32700 | 0.0 | - |
| 1.7166 | 32750 | 0.0 | - |
| 1.7193 | 32800 | 0.0 | - |
| 1.7219 | 32850 | 0.0 | - |
| 1.7245 | 32900 | 0.0 | - |
| 1.7271 | 32950 | 0.0 | - |
| 1.7297 | 33000 | 0.0 | - |
| 1.7324 | 33050 | 0.0 | - |
| 1.7350 | 33100 | 0.0 | - |
| 1.7376 | 33150 | 0.0 | - |
| 1.7402 | 33200 | 0.0 | - |
| 1.7428 | 33250 | 0.0 | - |
| 1.7455 | 33300 | 0.0 | - |
| 1.7481 | 33350 | 0.0 | - |
| 1.7507 | 33400 | 0.0 | - |
| 1.7533 | 33450 | 0.0 | - |
| 1.7559 | 33500 | 0.0 | - |
| 1.7586 | 33550 | 0.0 | - |
| 1.7612 | 33600 | 0.0 | - |
| 1.7638 | 33650 | 0.0 | - |
| 1.7664 | 33700 | 0.0 | - |
| 1.7691 | 33750 | 0.0 | - |
| 1.7717 | 33800 | 0.0 | - |
| 1.7743 | 33850 | 0.0 | - |
| 1.7769 | 33900 | 0.0 | - |
| 1.7795 | 33950 | 0.0 | - |
| 1.7822 | 34000 | 0.0 | - |
| 1.7848 | 34050 | 0.0 | - |
| 1.7874 | 34100 | 0.0 | - |
| 1.7900 | 34150 | 0.0 | - |
| 1.7926 | 34200 | 0.0 | - |
| 1.7953 | 34250 | 0.0 | - |
| 1.7979 | 34300 | 0.0 | - |
| 1.8005 | 34350 | 0.0 | - |
| 1.8031 | 34400 | 0.0 | - |
| 1.8057 | 34450 | 0.0 | - |
| 1.8084 | 34500 | 0.0 | - |
| 1.8110 | 34550 | 0.0 | - |
| 1.8136 | 34600 | 0.0 | - |
| 1.8162 | 34650 | 0.0 | - |
| 1.8188 | 34700 | 0.0 | - |
| 1.8215 | 34750 | 0.0 | - |
| 1.8241 | 34800 | 0.0 | - |
| 1.8267 | 34850 | 0.0 | - |
| 1.8293 | 34900 | 0.0 | - |
| 1.8320 | 34950 | 0.0 | - |
| 1.8346 | 35000 | 0.0 | - |
| 1.8372 | 35050 | 0.0 | - |
| 1.8398 | 35100 | 0.0 | - |
| 1.8424 | 35150 | 0.0 | - |
| 1.8451 | 35200 | 0.0 | - |
| 1.8477 | 35250 | 0.0 | - |
| 1.8503 | 35300 | 0.0 | - |
| 1.8529 | 35350 | 0.0 | - |
| 1.8555 | 35400 | 0.0 | - |
| 1.8582 | 35450 | 0.0 | - |
| 1.8608 | 35500 | 0.0 | - |
| 1.8634 | 35550 | 0.0 | - |
| 1.8660 | 35600 | 0.0 | - |
| 1.8686 | 35650 | 0.0 | - |
| 1.8713 | 35700 | 0.0 | - |
| 1.8739 | 35750 | 0.0 | - |
| 1.8765 | 35800 | 0.0 | - |
| 1.8791 | 35850 | 0.0 | - |
| 1.8817 | 35900 | 0.0 | - |
| 1.8844 | 35950 | 0.0 | - |
| 1.8870 | 36000 | 0.0 | - |
| 1.8896 | 36050 | 0.0 | - |
| 1.8922 | 36100 | 0.0 | - |
| 1.8949 | 36150 | 0.0 | - |
| 1.8975 | 36200 | 0.0 | - |
| 1.9001 | 36250 | 0.0 | - |
| 1.9027 | 36300 | 0.0 | - |
| 1.9053 | 36350 | 0.0 | - |
| 1.9080 | 36400 | 0.0 | - |
| 1.9106 | 36450 | 0.0 | - |
| 1.9132 | 36500 | 0.0 | - |
| 1.9158 | 36550 | 0.0 | - |
| 1.9184 | 36600 | 0.0 | - |
| 1.9211 | 36650 | 0.0 | - |
| 1.9237 | 36700 | 0.0 | - |
| 1.9263 | 36750 | 0.0 | - |
| 1.9289 | 36800 | 0.0 | - |
| 1.9315 | 36850 | 0.0 | - |
| 1.9342 | 36900 | 0.0 | - |
| 1.9368 | 36950 | 0.0 | - |
| 1.9394 | 37000 | 0.0 | - |
| 1.9420 | 37050 | 0.0 | - |
| 1.9446 | 37100 | 0.0 | - |
| 1.9473 | 37150 | 0.0 | - |
| 1.9499 | 37200 | 0.0 | - |
| 1.9525 | 37250 | 0.0 | - |
| 1.9551 | 37300 | 0.0 | - |
| 1.9578 | 37350 | 0.0 | - |
| 1.9604 | 37400 | 0.0 | - |
| 1.9630 | 37450 | 0.0 | - |
| 1.9656 | 37500 | 0.0 | - |
| 1.9682 | 37550 | 0.0 | - |
| 1.9709 | 37600 | 0.0 | - |
| 1.9735 | 37650 | 0.0 | - |
| 1.9761 | 37700 | 0.0 | - |
| 1.9787 | 37750 | 0.0 | - |
| 1.9813 | 37800 | 0.0 | - |
| 1.9840 | 37850 | 0.0 | - |
| 1.9866 | 37900 | 0.0 | - |
| 1.9892 | 37950 | 0.0 | - |
| 1.9918 | 38000 | 0.0 | - |
| 1.9944 | 38050 | 0.0 | - |
| 1.9971 | 38100 | 0.0 | - |
| 1.9997 | 38150 | 0.0 | - |
| 2.0 | 38156 | - | 0.0438 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.0.0
- Datasets: 2.16.1
- Tokenizers: 0.15.0
## 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}
}
```