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sft_model_project

Text Classification
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 ---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Modifier l’adresse du magasin
- text: أحذف المنتج من المخزن
- text: I want to change the store address
- text: I want to create a website that sells books
- text: What stores do we have?
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9743589743589743
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:** 13 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 |
|:------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| delete_category | <ul><li>'Supprimer une catégorie'</li><li>'I want to delete a product category'</li><li>'Remove a category from the list'</li></ul> |
| delete_product | <ul><li>'I want to delete the red t-shirt'</li><li>'Remove this item from inventory'</li><li>'Supprimer un produit'</li></ul> |
| greet-who_are_you | <ul><li>'how can you help me'</li><li>"pourquoi j'ai besoin de toi"</li><li>'je ne te comprends pas'</li></ul> |
| create_website | <ul><li>'أريد إنشاء موقع إلكتروني لمتجر الملابس الخاص بي'</li><li>'ساعدني في تصميم موقع أعمالي الخاصة بالتدريب الرياضي'</li><li>'ساعدني في إنشاء موقع لمطعمي'</li></ul> |
| read_category | <ul><li>'Can I see all the categories?'</li><li>'What categories are available?'</li><li>'Affiche-moi les catégories'</li></ul> |
| update_store | <ul><li>'Update store information'</li><li>'Modify the store contact details'</li><li>'Je veux changer les coordonnées du magasin'</li></ul> |
| update_category | <ul><li>'Je veux changer le nom d’une catégorie'</li><li>'Can I rename a category?'</li><li>'Update the category name to something else'</li></ul> |
| greet-good_bye | <ul><li>'See you later'</li><li>'A plus tard'</li><li>'stop'</li></ul> |
| update_product | <ul><li>'I want to change product details'</li><li>'Je veux modifier un produit'</li><li>'Edit the product information'</li></ul> |
| read_product | <ul><li>'Can I see the available items?'</li><li>'List the products'</li><li>'Affiche tous les produits'</li></ul> |
| delete_store | <ul><li>'Remove store number 3'</li><li>'Supprimer un magasin'</li><li>'Can I delete an existing store?'</li></ul> |
| read_store | <ul><li>'Quels sont les magasins disponibles ?'</li><li>'List all registered stores'</li><li>'Show me the list of stores'</li></ul> |
| greet-hi | <ul><li>'Hello buddy'</li><li>'Salut'</li><li>'Hey'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9744 |
## 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("Decius/sft_model_project")
# Run inference
preds = model("أحذف المنتج من المخزن")
```
<!--
### 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 | 5.7846 | 13 |
| Label | Training Sample Count |
|:------------------|:----------------------|
| greet-hi | 5 |
| greet-who_are_you | 7 |
| greet-good_bye | 5 |
| create_website | 21 |
| read_category | 3 |
| update_category | 3 |
| delete_category | 3 |
| read_product | 3 |
| update_product | 3 |
| delete_product | 3 |
| read_store | 3 |
| update_store | 3 |
| delete_store | 3 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (4, 4)
- 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
- l2_weight: 0.01
- seed: 42
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0011 | 1 | 0.1475 | - |
| 0.0111 | 10 | 0.1345 | - |
| 0.0222 | 20 | 0.0807 | - |
| 0.0333 | 30 | 0.0943 | - |
| 0.0444 | 40 | 0.0785 | - |
| 0.0555 | 50 | 0.1016 | - |
| 0.0666 | 60 | 0.0756 | - |
| 0.0777 | 70 | 0.0775 | - |
| 0.0888 | 80 | 0.0368 | - |
| 0.0999 | 90 | 0.0635 | - |
| 0.1110 | 100 | 0.0395 | - |
| 0.1221 | 110 | 0.0279 | - |
| 0.1332 | 120 | 0.0217 | - |
| 0.1443 | 130 | 0.0254 | - |
| 0.1554 | 140 | 0.0406 | - |
| 0.1665 | 150 | 0.0143 | - |
| 0.1776 | 160 | 0.0482 | - |
| 0.1887 | 170 | 0.042 | - |
| 0.1998 | 180 | 0.0286 | - |
| 0.2109 | 190 | 0.012 | - |
| 0.2220 | 200 | 0.0258 | - |
| 0.2331 | 210 | 0.0193 | - |
| 0.2442 | 220 | 0.0126 | - |
| 0.2553 | 230 | 0.0342 | - |
| 0.2664 | 240 | 0.0238 | - |
| 0.2775 | 250 | 0.0111 | - |
| 0.2886 | 260 | 0.0101 | - |
| 0.2997 | 270 | 0.0099 | - |
| 0.3108 | 280 | 0.0208 | - |
| 0.3219 | 290 | 0.0089 | - |
| 0.3330 | 300 | 0.0276 | - |
| 0.3441 | 310 | 0.0099 | - |
| 0.3552 | 320 | 0.0191 | - |
| 0.3663 | 330 | 0.0199 | - |
| 0.3774 | 340 | 0.0095 | - |
| 0.3885 | 350 | 0.0142 | - |
| 0.3996 | 360 | 0.0083 | - |
| 0.4107 | 370 | 0.0079 | - |
| 0.4218 | 380 | 0.0072 | - |
| 0.4329 | 390 | 0.0098 | - |
| 0.4440 | 400 | 0.01 | - |
| 0.4550 | 410 | 0.0084 | - |
| 0.4661 | 420 | 0.0024 | - |
| 0.4772 | 430 | 0.0176 | - |
| 0.4883 | 440 | 0.0068 | - |
| 0.4994 | 450 | 0.0209 | - |
| 0.5105 | 460 | 0.0038 | - |
| 0.5216 | 470 | 0.0063 | - |
| 0.5327 | 480 | 0.034 | - |
| 0.5438 | 490 | 0.0191 | - |
| 0.5549 | 500 | 0.0159 | - |
| 0.5660 | 510 | 0.0088 | - |
| 0.5771 | 520 | 0.0032 | - |
| 0.5882 | 530 | 0.0045 | - |
| 0.5993 | 540 | 0.0192 | - |
| 0.6104 | 550 | 0.0123 | - |
| 0.6215 | 560 | 0.0048 | - |
| 0.6326 | 570 | 0.0068 | - |
| 0.6437 | 580 | 0.0036 | - |
| 0.6548 | 590 | 0.0123 | - |
| 0.6659 | 600 | 0.0104 | - |
| 0.6770 | 610 | 0.0023 | - |
| 0.6881 | 620 | 0.0062 | - |
| 0.6992 | 630 | 0.0048 | - |
| 0.7103 | 640 | 0.0063 | - |
| 0.7214 | 650 | 0.0012 | - |
| 0.7325 | 660 | 0.0026 | - |
| 0.7436 | 670 | 0.0136 | - |
| 0.7547 | 680 | 0.0144 | - |
| 0.7658 | 690 | 0.0045 | - |
| 0.7769 | 700 | 0.0013 | - |
| 0.7880 | 710 | 0.0058 | - |
| 0.7991 | 720 | 0.0056 | - |
| 0.8102 | 730 | 0.004 | - |
| 0.8213 | 740 | 0.0023 | - |
| 0.8324 | 750 | 0.0047 | - |
| 0.8435 | 760 | 0.001 | - |
| 0.8546 | 770 | 0.0028 | - |
| 0.8657 | 780 | 0.0042 | - |
| 0.8768 | 790 | 0.0016 | - |
| 0.8879 | 800 | 0.002 | - |
| 0.8990 | 810 | 0.0004 | - |
| 0.9101 | 820 | 0.0034 | - |
| 0.9212 | 830 | 0.0016 | - |
| 0.9323 | 840 | 0.0076 | - |
| 0.9434 | 850 | 0.0021 | - |
| 0.9545 | 860 | 0.0027 | - |
| 0.9656 | 870 | 0.0017 | - |
| 0.9767 | 880 | 0.0024 | - |
| 0.9878 | 890 | 0.0014 | - |
| 0.9989 | 900 | 0.0015 | - |
| 1.0 | 901 | - | 0.0316 |
| 1.0100 | 910 | 0.0014 | - |
| 1.0211 | 920 | 0.0009 | - |
| 1.0322 | 930 | 0.0015 | - |
| 1.0433 | 940 | 0.0023 | - |
| 1.0544 | 950 | 0.0004 | - |
| 1.0655 | 960 | 0.0006 | - |
| 1.0766 | 970 | 0.001 | - |
| 1.0877 | 980 | 0.0005 | - |
| 1.0988 | 990 | 0.0044 | - |
| 1.1099 | 1000 | 0.0011 | - |
| 1.1210 | 1010 | 0.0008 | - |
| 1.1321 | 1020 | 0.0008 | - |
| 1.1432 | 1030 | 0.0007 | - |
| 1.1543 | 1040 | 0.0004 | - |
| 1.1654 | 1050 | 0.0009 | - |
| 1.1765 | 1060 | 0.0017 | - |
| 1.1876 | 1070 | 0.002 | - |
| 1.1987 | 1080 | 0.0008 | - |
| 1.2098 | 1090 | 0.002 | - |
| 1.2209 | 1100 | 0.0005 | - |
| 1.2320 | 1110 | 0.0012 | - |
| 1.2431 | 1120 | 0.002 | - |
| 1.2542 | 1130 | 0.0012 | - |
| 1.2653 | 1140 | 0.0025 | - |
| 1.2764 | 1150 | 0.0008 | - |
| 1.2875 | 1160 | 0.0009 | - |
| 1.2986 | 1170 | 0.0011 | - |
| 1.3097 | 1180 | 0.0004 | - |
| 1.3208 | 1190 | 0.001 | - |
| 1.3319 | 1200 | 0.0008 | - |
| 1.3430 | 1210 | 0.0005 | - |
| 1.3541 | 1220 | 0.0006 | - |
| 1.3651 | 1230 | 0.0007 | - |
| 1.3762 | 1240 | 0.0009 | - |
| 1.3873 | 1250 | 0.0008 | - |
| 1.3984 | 1260 | 0.0009 | - |
| 1.4095 | 1270 | 0.0009 | - |
| 1.4206 | 1280 | 0.0008 | - |
| 1.4317 | 1290 | 0.0007 | - |
| 1.4428 | 1300 | 0.001 | - |
| 1.4539 | 1310 | 0.0004 | - |
| 1.4650 | 1320 | 0.0004 | - |
| 1.4761 | 1330 | 0.0008 | - |
| 1.4872 | 1340 | 0.0003 | - |
| 1.4983 | 1350 | 0.0004 | - |
| 1.5094 | 1360 | 0.0096 | - |
| 1.5205 | 1370 | 0.001 | - |
| 1.5316 | 1380 | 0.0006 | - |
| 1.5427 | 1390 | 0.0015 | - |
| 1.5538 | 1400 | 0.0008 | - |
| 1.5649 | 1410 | 0.0006 | - |
| 1.5760 | 1420 | 0.0007 | - |
| 1.5871 | 1430 | 0.0009 | - |
| 1.5982 | 1440 | 0.0004 | - |
| 1.6093 | 1450 | 0.0013 | - |
| 1.6204 | 1460 | 0.0007 | - |
| 1.6315 | 1470 | 0.0004 | - |
| 1.6426 | 1480 | 0.0005 | - |
| 1.6537 | 1490 | 0.0006 | - |
| 1.6648 | 1500 | 0.0008 | - |
| 1.6759 | 1510 | 0.0007 | - |
| 1.6870 | 1520 | 0.0005 | - |
| 1.6981 | 1530 | 0.0004 | - |
| 1.7092 | 1540 | 0.0005 | - |
| 1.7203 | 1550 | 0.0007 | - |
| 1.7314 | 1560 | 0.0006 | - |
| 1.7425 | 1570 | 0.0004 | - |
| 1.7536 | 1580 | 0.0006 | - |
| 1.7647 | 1590 | 0.0005 | - |
| 1.7758 | 1600 | 0.0006 | - |
| 1.7869 | 1610 | 0.0011 | - |
| 1.7980 | 1620 | 0.0007 | - |
| 1.8091 | 1630 | 0.0005 | - |
| 1.8202 | 1640 | 0.0005 | - |
| 1.8313 | 1650 | 0.0003 | - |
| 1.8424 | 1660 | 0.0004 | - |
| 1.8535 | 1670 | 0.0006 | - |
| 1.8646 | 1680 | 0.0005 | - |
| 1.8757 | 1690 | 0.0006 | - |
| 1.8868 | 1700 | 0.0004 | - |
| 1.8979 | 1710 | 0.0004 | - |
| 1.9090 | 1720 | 0.0002 | - |
| 1.9201 | 1730 | 0.0005 | - |
| 1.9312 | 1740 | 0.0005 | - |
| 1.9423 | 1750 | 0.001 | - |
| 1.9534 | 1760 | 0.0006 | - |
| 1.9645 | 1770 | 0.001 | - |
| 1.9756 | 1780 | 0.0004 | - |
| 1.9867 | 1790 | 0.0005 | - |
| 1.9978 | 1800 | 0.0002 | - |
| 2.0 | 1802 | - | 0.0260 |
| 2.0089 | 1810 | 0.0005 | - |
| 2.0200 | 1820 | 0.0005 | - |
| 2.0311 | 1830 | 0.0004 | - |
| 2.0422 | 1840 | 0.0005 | - |
| 2.0533 | 1850 | 0.0002 | - |
| 2.0644 | 1860 | 0.0005 | - |
| 2.0755 | 1870 | 0.0007 | - |
| 2.0866 | 1880 | 0.0005 | - |
| 2.0977 | 1890 | 0.0003 | - |
| 2.1088 | 1900 | 0.0004 | - |
| 2.1199 | 1910 | 0.0003 | - |
| 2.1310 | 1920 | 0.0014 | - |
| 2.1421 | 1930 | 0.0005 | - |
| 2.1532 | 1940 | 0.0002 | - |
| 2.1643 | 1950 | 0.0003 | - |
| 2.1754 | 1960 | 0.0007 | - |
| 2.1865 | 1970 | 0.0005 | - |
| 2.1976 | 1980 | 0.0004 | - |
| 2.2087 | 1990 | 0.0006 | - |
| 2.2198 | 2000 | 0.0005 | - |
| 2.2309 | 2010 | 0.0003 | - |
| 2.2420 | 2020 | 0.0006 | - |
| 2.2531 | 2030 | 0.0006 | - |
| 2.2642 | 2040 | 0.0006 | - |
| 2.2752 | 2050 | 0.0003 | - |
| 2.2863 | 2060 | 0.0014 | - |
| 2.2974 | 2070 | 0.0004 | - |
| 2.3085 | 2080 | 0.0005 | - |
| 2.3196 | 2090 | 0.0004 | - |
| 2.3307 | 2100 | 0.0004 | - |
| 2.3418 | 2110 | 0.0004 | - |
| 2.3529 | 2120 | 0.0004 | - |
| 2.3640 | 2130 | 0.0011 | - |
| 2.3751 | 2140 | 0.0003 | - |
| 2.3862 | 2150 | 0.0003 | - |
| 2.3973 | 2160 | 0.0005 | - |
| 2.4084 | 2170 | 0.0006 | - |
| 2.4195 | 2180 | 0.0004 | - |
| 2.4306 | 2190 | 0.0002 | - |
| 2.4417 | 2200 | 0.0002 | - |
| 2.4528 | 2210 | 0.0006 | - |
| 2.4639 | 2220 | 0.0003 | - |
| 2.4750 | 2230 | 0.0002 | - |
| 2.4861 | 2240 | 0.0006 | - |
| 2.4972 | 2250 | 0.0006 | - |
| 2.5083 | 2260 | 0.0004 | - |
| 2.5194 | 2270 | 0.0005 | - |
| 2.5305 | 2280 | 0.0004 | - |
| 2.5416 | 2290 | 0.0005 | - |
| 2.5527 | 2300 | 0.0005 | - |
| 2.5638 | 2310 | 0.0006 | - |
| 2.5749 | 2320 | 0.0005 | - |
| 2.5860 | 2330 | 0.0003 | - |
| 2.5971 | 2340 | 0.0007 | - |
| 2.6082 | 2350 | 0.0002 | - |
| 2.6193 | 2360 | 0.0003 | - |
| 2.6304 | 2370 | 0.0003 | - |
| 2.6415 | 2380 | 0.0004 | - |
| 2.6526 | 2390 | 0.0004 | - |
| 2.6637 | 2400 | 0.0005 | - |
| 2.6748 | 2410 | 0.0003 | - |
| 2.6859 | 2420 | 0.0003 | - |
| 2.6970 | 2430 | 0.0003 | - |
| 2.7081 | 2440 | 0.0005 | - |
| 2.7192 | 2450 | 0.0006 | - |
| 2.7303 | 2460 | 0.0005 | - |
| 2.7414 | 2470 | 0.0005 | - |
| 2.7525 | 2480 | 0.0006 | - |
| 2.7636 | 2490 | 0.0002 | - |
| 2.7747 | 2500 | 0.0002 | - |
| 2.7858 | 2510 | 0.0002 | - |
| 2.7969 | 2520 | 0.0007 | - |
| 2.8080 | 2530 | 0.0003 | - |
| 2.8191 | 2540 | 0.0004 | - |
| 2.8302 | 2550 | 0.0003 | - |
| 2.8413 | 2560 | 0.0002 | - |
| 2.8524 | 2570 | 0.0006 | - |
| 2.8635 | 2580 | 0.0003 | - |
| 2.8746 | 2590 | 0.0002 | - |
| 2.8857 | 2600 | 0.0002 | - |
| 2.8968 | 2610 | 0.0002 | - |
| 2.9079 | 2620 | 0.0003 | - |
| 2.9190 | 2630 | 0.0003 | - |
| 2.9301 | 2640 | 0.0002 | - |
| 2.9412 | 2650 | 0.0002 | - |
| 2.9523 | 2660 | 0.0002 | - |
| 2.9634 | 2670 | 0.0003 | - |
| 2.9745 | 2680 | 0.0003 | - |
| 2.9856 | 2690 | 0.0003 | - |
| 2.9967 | 2700 | 0.0003 | - |
| 3.0 | 2703 | - | 0.0244 |
| 3.0078 | 2710 | 0.0002 | - |
| 3.0189 | 2720 | 0.0004 | - |
| 3.0300 | 2730 | 0.0002 | - |
| 3.0411 | 2740 | 0.0003 | - |
| 3.0522 | 2750 | 0.0003 | - |
| 3.0633 | 2760 | 0.0002 | - |
| 3.0744 | 2770 | 0.0001 | - |
| 3.0855 | 2780 | 0.0002 | - |
| 3.0966 | 2790 | 0.0003 | - |
| 3.1077 | 2800 | 0.0003 | - |
| 3.1188 | 2810 | 0.0004 | - |
| 3.1299 | 2820 | 0.0005 | - |
| 3.1410 | 2830 | 0.0002 | - |
| 3.1521 | 2840 | 0.0003 | - |
| 3.1632 | 2850 | 0.0002 | - |
| 3.1743 | 2860 | 0.0003 | - |
| 3.1853 | 2870 | 0.0002 | - |
| 3.1964 | 2880 | 0.0007 | - |
| 3.2075 | 2890 | 0.0002 | - |
| 3.2186 | 2900 | 0.0002 | - |
| 3.2297 | 2910 | 0.0002 | - |
| 3.2408 | 2920 | 0.0003 | - |
| 3.2519 | 2930 | 0.0002 | - |
| 3.2630 | 2940 | 0.0002 | - |
| 3.2741 | 2950 | 0.0003 | - |
| 3.2852 | 2960 | 0.0005 | - |
| 3.2963 | 2970 | 0.0003 | - |
| 3.3074 | 2980 | 0.0002 | - |
| 3.3185 | 2990 | 0.0003 | - |
| 3.3296 | 3000 | 0.0003 | - |
| 3.3407 | 3010 | 0.0002 | - |
| 3.3518 | 3020 | 0.0002 | - |
| 3.3629 | 3030 | 0.0003 | - |
| 3.3740 | 3040 | 0.0001 | - |
| 3.3851 | 3050 | 0.0003 | - |
| 3.3962 | 3060 | 0.0003 | - |
| 3.4073 | 3070 | 0.0004 | - |
| 3.4184 | 3080 | 0.0002 | - |
| 3.4295 | 3090 | 0.0003 | - |
| 3.4406 | 3100 | 0.0003 | - |
| 3.4517 | 3110 | 0.0002 | - |
| 3.4628 | 3120 | 0.0002 | - |
| 3.4739 | 3130 | 0.0002 | - |
| 3.4850 | 3140 | 0.0004 | - |
| 3.4961 | 3150 | 0.0005 | - |
| 3.5072 | 3160 | 0.0006 | - |
| 3.5183 | 3170 | 0.0002 | - |
| 3.5294 | 3180 | 0.0002 | - |
| 3.5405 | 3190 | 0.0004 | - |
| 3.5516 | 3200 | 0.0003 | - |
| 3.5627 | 3210 | 0.0002 | - |
| 3.5738 | 3220 | 0.0001 | - |
| 3.5849 | 3230 | 0.0002 | - |
| 3.5960 | 3240 | 0.0002 | - |
| 3.6071 | 3250 | 0.0001 | - |
| 3.6182 | 3260 | 0.0002 | - |
| 3.6293 | 3270 | 0.0002 | - |
| 3.6404 | 3280 | 0.0002 | - |
| 3.6515 | 3290 | 0.0002 | - |
| 3.6626 | 3300 | 0.0003 | - |
| 3.6737 | 3310 | 0.0003 | - |
| 3.6848 | 3320 | 0.0003 | - |
| 3.6959 | 3330 | 0.0002 | - |
| 3.7070 | 3340 | 0.0001 | - |
| 3.7181 | 3350 | 0.0002 | - |
| 3.7292 | 3360 | 0.0002 | - |
| 3.7403 | 3370 | 0.0002 | - |
| 3.7514 | 3380 | 0.0002 | - |
| 3.7625 | 3390 | 0.0002 | - |
| 3.7736 | 3400 | 0.0001 | - |
| 3.7847 | 3410 | 0.0003 | - |
| 3.7958 | 3420 | 0.0002 | - |
| 3.8069 | 3430 | 0.0003 | - |
| 3.8180 | 3440 | 0.0003 | - |
| 3.8291 | 3450 | 0.0002 | - |
| 3.8402 | 3460 | 0.0002 | - |
| 3.8513 | 3470 | 0.0002 | - |
| 3.8624 | 3480 | 0.0002 | - |
| 3.8735 | 3490 | 0.0002 | - |
| 3.8846 | 3500 | 0.0002 | - |
| 3.8957 | 3510 | 0.0002 | - |
| 3.9068 | 3520 | 0.0003 | - |
| 3.9179 | 3530 | 0.0001 | - |
| 3.9290 | 3540 | 0.0002 | - |
| 3.9401 | 3550 | 0.0002 | - |
| 3.9512 | 3560 | 0.0002 | - |
| 3.9623 | 3570 | 0.0003 | - |
| 3.9734 | 3580 | 0.0003 | - |
| 3.9845 | 3590 | 0.0002 | - |
| 3.9956 | 3600 | 0.0002 | - |
| 4.0 | 3604 | - | 0.0237 |
### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.0
- Sentence Transformers: 3.4.1
- Transformers: 4.44.0
- PyTorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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