setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index region:us
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: au revoir
- text: quand auront lieu les matchs de Aston Villa
- text: any upcoming fixtures for Juventus
- text: qui êtes-vous
- text: what is the score of Brentford match
inference: true
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: 1.0
      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:** 6 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                                                                                                                                                           |
|:------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| matches-match_time      | <ul><li>'Norwich City vs Newcastle United'</li><li>'will Manchester United play with chelsea'</li><li>'est-ce que Manchester United jouera avec chelsea'</li></ul> |
| matches-match_result    | <ul><li>'Liverpool and West Ham result'</li><li>'what is the score of Wolverhampton match'</li><li>'who won in Liverpool vs Newcastle United match'</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>                                                     |
| matches-team_next_match | <ul><li>'Real Madrid fixtures'</li><li>'quels sont les prochains matchs de Borussia Dortmund'</li><li>'próximos partidos de Atletico Madrid'</li></ul>             |
| greet-good_bye          | <ul><li>'See you later'</li><li>'A plus tard'</li><li>'stop'</li></ul>                                                                                             |
| greet-hi                | <ul><li>'Hello buddy'</li><li>'Salut'</li><li>'Hey'</li></ul>                                                                                                      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("fadyabdo/botpress_football_sft_model")
# Run inference
preds = model("au revoir")
```

<!--
### 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   | 5.2    | 10  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| greet-hi                | 5                     |
| greet-who_are_you       | 7                     |
| greet-good_bye          | 5                     |
| matches-team_next_match | 21                    |
| matches-match_time      | 12                    |
| matches-match_result    | 15                    |

### 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch   | Step     | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0012  | 1        | 0.1544        | -               |
| 0.0121  | 10       | 0.0658        | -               |
| 0.0241  | 20       | 0.1235        | -               |
| 0.0362  | 30       | 0.2422        | -               |
| 0.0483  | 40       | 0.2876        | -               |
| 0.0603  | 50       | 0.1208        | -               |
| 0.0724  | 60       | 0.1358        | -               |
| 0.0844  | 70       | 0.1494        | -               |
| 0.0965  | 80       | 0.1284        | -               |
| 0.1086  | 90       | 0.1107        | -               |
| 0.1206  | 100      | 0.2395        | -               |
| 0.1327  | 110      | 0.0661        | -               |
| 0.1448  | 120      | 0.1554        | -               |
| 0.1568  | 130      | 0.0258        | -               |
| 0.1689  | 140      | 0.0279        | -               |
| 0.1809  | 150      | 0.1162        | -               |
| 0.1930  | 160      | 0.0244        | -               |
| 0.2051  | 170      | 0.0221        | -               |
| 0.2171  | 180      | 0.0813        | -               |
| 0.2292  | 190      | 0.0188        | -               |
| 0.2413  | 200      | 0.03          | -               |
| 0.2533  | 210      | 0.0019        | -               |
| 0.2654  | 220      | 0.0076        | -               |
| 0.2774  | 230      | 0.01          | -               |
| 0.2895  | 240      | 0.0025        | -               |
| 0.3016  | 250      | 0.0705        | -               |
| 0.3136  | 260      | 0.0044        | -               |
| 0.3257  | 270      | 0.0038        | -               |
| 0.3378  | 280      | 0.006         | -               |
| 0.3498  | 290      | 0.0018        | -               |
| 0.3619  | 300      | 0.0003        | -               |
| 0.3739  | 310      | 0.0007        | -               |
| 0.3860  | 320      | 0.0128        | -               |
| 0.3981  | 330      | 0.0022        | -               |
| 0.4101  | 340      | 0.0008        | -               |
| 0.4222  | 350      | 0.004         | -               |
| 0.4343  | 360      | 0.0006        | -               |
| 0.4463  | 370      | 0.0007        | -               |
| 0.4584  | 380      | 0.0005        | -               |
| 0.4704  | 390      | 0.0057        | -               |
| 0.4825  | 400      | 0.0007        | -               |
| 0.4946  | 410      | 0.0022        | -               |
| 0.5066  | 420      | 0.0012        | -               |
| 0.5187  | 430      | 0.0009        | -               |
| 0.5308  | 440      | 0.0004        | -               |
| 0.5428  | 450      | 0.0032        | -               |
| 0.5549  | 460      | 0.0007        | -               |
| 0.5669  | 470      | 0.0008        | -               |
| 0.5790  | 480      | 0.0005        | -               |
| 0.5911  | 490      | 0.0005        | -               |
| 0.6031  | 500      | 0.0008        | -               |
| 0.6152  | 510      | 0.0008        | -               |
| 0.6273  | 520      | 0.0004        | -               |
| 0.6393  | 530      | 0.0015        | -               |
| 0.6514  | 540      | 0.0002        | -               |
| 0.6634  | 550      | 0.0006        | -               |
| 0.6755  | 560      | 0.0015        | -               |
| 0.6876  | 570      | 0.0024        | -               |
| 0.6996  | 580      | 0.0004        | -               |
| 0.7117  | 590      | 0.0005        | -               |
| 0.7238  | 600      | 0.0011        | -               |
| 0.7358  | 610      | 0.0008        | -               |
| 0.7479  | 620      | 0.0002        | -               |
| 0.7600  | 630      | 0.0006        | -               |
| 0.7720  | 640      | 0.0003        | -               |
| 0.7841  | 650      | 0.0002        | -               |
| 0.7961  | 660      | 0.0007        | -               |
| 0.8082  | 670      | 0.0009        | -               |
| 0.8203  | 680      | 0.0002        | -               |
| 0.8323  | 690      | 0.0006        | -               |
| 0.8444  | 700      | 0.0015        | -               |
| 0.8565  | 710      | 0.0003        | -               |
| 0.8685  | 720      | 0.0003        | -               |
| 0.8806  | 730      | 0.0003        | -               |
| 0.8926  | 740      | 0.0015        | -               |
| 0.9047  | 750      | 0.0003        | -               |
| 0.9168  | 760      | 0.0005        | -               |
| 0.9288  | 770      | 0.0002        | -               |
| 0.9409  | 780      | 0.0003        | -               |
| 0.9530  | 790      | 0.0002        | -               |
| 0.9650  | 800      | 0.0004        | -               |
| 0.9771  | 810      | 0.0003        | -               |
| 0.9891  | 820      | 0.001         | -               |
| 1.0     | 829      | -             | 0.0216          |
| 1.0012  | 830      | 0.0003        | -               |
| 1.0133  | 840      | 0.0007        | -               |
| 1.0253  | 850      | 0.0004        | -               |
| 1.0374  | 860      | 0.0001        | -               |
| 1.0495  | 870      | 0.0008        | -               |
| 1.0615  | 880      | 0.0003        | -               |
| 1.0736  | 890      | 0.0006        | -               |
| 1.0856  | 900      | 0.0001        | -               |
| 1.0977  | 910      | 0.0018        | -               |
| 1.1098  | 920      | 0.0           | -               |
| 1.1218  | 930      | 0.0001        | -               |
| 1.1339  | 940      | 0.0007        | -               |
| 1.1460  | 950      | 0.0009        | -               |
| 1.1580  | 960      | 0.0004        | -               |
| 1.1701  | 970      | 0.0003        | -               |
| 1.1821  | 980      | 0.0015        | -               |
| 1.1942  | 990      | 0.0002        | -               |
| 1.2063  | 1000     | 0.0005        | -               |
| 1.2183  | 1010     | 0.0002        | -               |
| 1.2304  | 1020     | 0.0003        | -               |
| 1.2425  | 1030     | 0.0001        | -               |
| 1.2545  | 1040     | 0.0002        | -               |
| 1.2666  | 1050     | 0.0004        | -               |
| 1.2786  | 1060     | 0.0001        | -               |
| 1.2907  | 1070     | 0.0002        | -               |
| 1.3028  | 1080     | 0.0001        | -               |
| 1.3148  | 1090     | 0.0002        | -               |
| 1.3269  | 1100     | 0.0001        | -               |
| 1.3390  | 1110     | 0.0002        | -               |
| 1.3510  | 1120     | 0.0003        | -               |
| 1.3631  | 1130     | 0.0001        | -               |
| 1.3752  | 1140     | 0.0001        | -               |
| 1.3872  | 1150     | 0.0001        | -               |
| 1.3993  | 1160     | 0.0002        | -               |
| 1.4113  | 1170     | 0.0001        | -               |
| 1.4234  | 1180     | 0.0005        | -               |
| 1.4355  | 1190     | 0.0002        | -               |
| 1.4475  | 1200     | 0.0002        | -               |
| 1.4596  | 1210     | 0.0002        | -               |
| 1.4717  | 1220     | 0.0001        | -               |
| 1.4837  | 1230     | 0.0001        | -               |
| 1.4958  | 1240     | 0.0001        | -               |
| 1.5078  | 1250     | 0.0001        | -               |
| 1.5199  | 1260     | 0.001         | -               |
| 1.5320  | 1270     | 0.0001        | -               |
| 1.5440  | 1280     | 0.0003        | -               |
| 1.5561  | 1290     | 0.0001        | -               |
| 1.5682  | 1300     | 0.0002        | -               |
| 1.5802  | 1310     | 0.0005        | -               |
| 1.5923  | 1320     | 0.0002        | -               |
| 1.6043  | 1330     | 0.0001        | -               |
| 1.6164  | 1340     | 0.0004        | -               |
| 1.6285  | 1350     | 0.0002        | -               |
| 1.6405  | 1360     | 0.0001        | -               |
| 1.6526  | 1370     | 0.0004        | -               |
| 1.6647  | 1380     | 0.0003        | -               |
| 1.6767  | 1390     | 0.0002        | -               |
| 1.6888  | 1400     | 0.0001        | -               |
| 1.7008  | 1410     | 0.0008        | -               |
| 1.7129  | 1420     | 0.0003        | -               |
| 1.7250  | 1430     | 0.0005        | -               |
| 1.7370  | 1440     | 0.0001        | -               |
| 1.7491  | 1450     | 0.0001        | -               |
| 1.7612  | 1460     | 0.0001        | -               |
| 1.7732  | 1470     | 0.0007        | -               |
| 1.7853  | 1480     | 0.0001        | -               |
| 1.7973  | 1490     | 0.0002        | -               |
| 1.8094  | 1500     | 0.0001        | -               |
| 1.8215  | 1510     | 0.001         | -               |
| 1.8335  | 1520     | 0.0002        | -               |
| 1.8456  | 1530     | 0.0003        | -               |
| 1.8577  | 1540     | 0.0004        | -               |
| 1.8697  | 1550     | 0.0005        | -               |
| 1.8818  | 1560     | 0.0001        | -               |
| 1.8938  | 1570     | 0.0006        | -               |
| 1.9059  | 1580     | 0.0005        | -               |
| 1.9180  | 1590     | 0.0002        | -               |
| 1.9300  | 1600     | 0.0002        | -               |
| 1.9421  | 1610     | 0.0001        | -               |
| 1.9542  | 1620     | 0.0003        | -               |
| 1.9662  | 1630     | 0.0005        | -               |
| 1.9783  | 1640     | 0.0007        | -               |
| 1.9903  | 1650     | 0.0001        | -               |
| 2.0     | 1658     | -             | 0.0186          |
| 2.0024  | 1660     | 0.0           | -               |
| 2.0145  | 1670     | 0.0001        | -               |
| 2.0265  | 1680     | 0.0002        | -               |
| 2.0386  | 1690     | 0.0001        | -               |
| 2.0507  | 1700     | 0.0002        | -               |
| 2.0627  | 1710     | 0.0001        | -               |
| 2.0748  | 1720     | 0.0001        | -               |
| 2.0869  | 1730     | 0.0002        | -               |
| 2.0989  | 1740     | 0.0001        | -               |
| 2.1110  | 1750     | 0.0002        | -               |
| 2.1230  | 1760     | 0.0001        | -               |
| 2.1351  | 1770     | 0.0003        | -               |
| 2.1472  | 1780     | 0.0006        | -               |
| 2.1592  | 1790     | 0.0001        | -               |
| 2.1713  | 1800     | 0.0002        | -               |
| 2.1834  | 1810     | 0.0002        | -               |
| 2.1954  | 1820     | 0.0001        | -               |
| 2.2075  | 1830     | 0.0           | -               |
| 2.2195  | 1840     | 0.0001        | -               |
| 2.2316  | 1850     | 0.0002        | -               |
| 2.2437  | 1860     | 0.0004        | -               |
| 2.2557  | 1870     | 0.0003        | -               |
| 2.2678  | 1880     | 0.0002        | -               |
| 2.2799  | 1890     | 0.0002        | -               |
| 2.2919  | 1900     | 0.0004        | -               |
| 2.3040  | 1910     | 0.0002        | -               |
| 2.3160  | 1920     | 0.0001        | -               |
| 2.3281  | 1930     | 0.0           | -               |
| 2.3402  | 1940     | 0.0002        | -               |
| 2.3522  | 1950     | 0.0001        | -               |
| 2.3643  | 1960     | 0.0           | -               |
| 2.3764  | 1970     | 0.0003        | -               |
| 2.3884  | 1980     | 0.0002        | -               |
| 2.4005  | 1990     | 0.0001        | -               |
| 2.4125  | 2000     | 0.0003        | -               |
| 2.4246  | 2010     | 0.0003        | -               |
| 2.4367  | 2020     | 0.0002        | -               |
| 2.4487  | 2030     | 0.0002        | -               |
| 2.4608  | 2040     | 0.0002        | -               |
| 2.4729  | 2050     | 0.0001        | -               |
| 2.4849  | 2060     | 0.0001        | -               |
| 2.4970  | 2070     | 0.0002        | -               |
| 2.5090  | 2080     | 0.0           | -               |
| 2.5211  | 2090     | 0.0002        | -               |
| 2.5332  | 2100     | 0.0004        | -               |
| 2.5452  | 2110     | 0.0005        | -               |
| 2.5573  | 2120     | 0.0003        | -               |
| 2.5694  | 2130     | 0.0001        | -               |
| 2.5814  | 2140     | 0.0002        | -               |
| 2.5935  | 2150     | 0.0008        | -               |
| 2.6055  | 2160     | 0.0002        | -               |
| 2.6176  | 2170     | 0.0003        | -               |
| 2.6297  | 2180     | 0.0001        | -               |
| 2.6417  | 2190     | 0.0002        | -               |
| 2.6538  | 2200     | 0.0001        | -               |
| 2.6659  | 2210     | 0.0001        | -               |
| 2.6779  | 2220     | 0.0           | -               |
| 2.6900  | 2230     | 0.0002        | -               |
| 2.7021  | 2240     | 0.0           | -               |
| 2.7141  | 2250     | 0.0001        | -               |
| 2.7262  | 2260     | 0.0001        | -               |
| 2.7382  | 2270     | 0.0003        | -               |
| 2.7503  | 2280     | 0.0001        | -               |
| 2.7624  | 2290     | 0.0003        | -               |
| 2.7744  | 2300     | 0.0001        | -               |
| 2.7865  | 2310     | 0.0002        | -               |
| 2.7986  | 2320     | 0.0001        | -               |
| 2.8106  | 2330     | 0.0001        | -               |
| 2.8227  | 2340     | 0.0001        | -               |
| 2.8347  | 2350     | 0.0001        | -               |
| 2.8468  | 2360     | 0.0002        | -               |
| 2.8589  | 2370     | 0.0001        | -               |
| 2.8709  | 2380     | 0.0001        | -               |
| 2.8830  | 2390     | 0.0           | -               |
| 2.8951  | 2400     | 0.0           | -               |
| 2.9071  | 2410     | 0.0           | -               |
| 2.9192  | 2420     | 0.0001        | -               |
| 2.9312  | 2430     | 0.0002        | -               |
| 2.9433  | 2440     | 0.0001        | -               |
| 2.9554  | 2450     | 0.0001        | -               |
| 2.9674  | 2460     | 0.0001        | -               |
| 2.9795  | 2470     | 0.0003        | -               |
| 2.9916  | 2480     | 0.0001        | -               |
| **3.0** | **2487** | **-**         | **0.0176**      |
| 3.0036  | 2490     | 0.0001        | -               |
| 3.0157  | 2500     | 0.0           | -               |
| 3.0277  | 2510     | 0.0002        | -               |
| 3.0398  | 2520     | 0.0           | -               |
| 3.0519  | 2530     | 0.0002        | -               |
| 3.0639  | 2540     | 0.0002        | -               |
| 3.0760  | 2550     | 0.0           | -               |
| 3.0881  | 2560     | 0.0001        | -               |
| 3.1001  | 2570     | 0.0001        | -               |
| 3.1122  | 2580     | 0.0003        | -               |
| 3.1242  | 2590     | 0.0003        | -               |
| 3.1363  | 2600     | 0.0001        | -               |
| 3.1484  | 2610     | 0.0           | -               |
| 3.1604  | 2620     | 0.0002        | -               |
| 3.1725  | 2630     | 0.0001        | -               |
| 3.1846  | 2640     | 0.0001        | -               |
| 3.1966  | 2650     | 0.0001        | -               |
| 3.2087  | 2660     | 0.0003        | -               |
| 3.2207  | 2670     | 0.0001        | -               |
| 3.2328  | 2680     | 0.0001        | -               |
| 3.2449  | 2690     | 0.0001        | -               |
| 3.2569  | 2700     | 0.0001        | -               |
| 3.2690  | 2710     | 0.0002        | -               |
| 3.2811  | 2720     | 0.0001        | -               |
| 3.2931  | 2730     | 0.0005        | -               |
| 3.3052  | 2740     | 0.0           | -               |
| 3.3172  | 2750     | 0.0001        | -               |
| 3.3293  | 2760     | 0.0002        | -               |
| 3.3414  | 2770     | 0.0003        | -               |
| 3.3534  | 2780     | 0.0001        | -               |
| 3.3655  | 2790     | 0.0001        | -               |
| 3.3776  | 2800     | 0.0001        | -               |
| 3.3896  | 2810     | 0.0004        | -               |
| 3.4017  | 2820     | 0.0001        | -               |
| 3.4138  | 2830     | 0.0002        | -               |
| 3.4258  | 2840     | 0.0001        | -               |
| 3.4379  | 2850     | 0.0003        | -               |
| 3.4499  | 2860     | 0.0001        | -               |
| 3.4620  | 2870     | 0.0002        | -               |
| 3.4741  | 2880     | 0.0001        | -               |
| 3.4861  | 2890     | 0.0003        | -               |
| 3.4982  | 2900     | 0.0003        | -               |
| 3.5103  | 2910     | 0.0001        | -               |
| 3.5223  | 2920     | 0.0           | -               |
| 3.5344  | 2930     | 0.0           | -               |
| 3.5464  | 2940     | 0.0001        | -               |
| 3.5585  | 2950     | 0.0002        | -               |
| 3.5706  | 2960     | 0.0002        | -               |
| 3.5826  | 2970     | 0.0001        | -               |
| 3.5947  | 2980     | 0.0           | -               |
| 3.6068  | 2990     | 0.0001        | -               |
| 3.6188  | 3000     | 0.0003        | -               |
| 3.6309  | 3010     | 0.0001        | -               |
| 3.6429  | 3020     | 0.0           | -               |
| 3.6550  | 3030     | 0.0002        | -               |
| 3.6671  | 3040     | 0.0003        | -               |
| 3.6791  | 3050     | 0.0005        | -               |
| 3.6912  | 3060     | 0.0001        | -               |
| 3.7033  | 3070     | 0.0           | -               |
| 3.7153  | 3080     | 0.0001        | -               |
| 3.7274  | 3090     | 0.0002        | -               |
| 3.7394  | 3100     | 0.0001        | -               |
| 3.7515  | 3110     | 0.0001        | -               |
| 3.7636  | 3120     | 0.0002        | -               |
| 3.7756  | 3130     | 0.0001        | -               |
| 3.7877  | 3140     | 0.0           | -               |
| 3.7998  | 3150     | 0.0001        | -               |
| 3.8118  | 3160     | 0.0001        | -               |
| 3.8239  | 3170     | 0.0001        | -               |
| 3.8359  | 3180     | 0.0001        | -               |
| 3.8480  | 3190     | 0.0005        | -               |
| 3.8601  | 3200     | 0.0           | -               |
| 3.8721  | 3210     | 0.0001        | -               |
| 3.8842  | 3220     | 0.0001        | -               |
| 3.8963  | 3230     | 0.0001        | -               |
| 3.9083  | 3240     | 0.0001        | -               |
| 3.9204  | 3250     | 0.0001        | -               |
| 3.9324  | 3260     | 0.0           | -               |
| 3.9445  | 3270     | 0.0001        | -               |
| 3.9566  | 3280     | 0.0001        | -               |
| 3.9686  | 3290     | 0.0002        | -               |
| 3.9807  | 3300     | 0.0002        | -               |
| 3.9928  | 3310     | 0.0001        | -               |
| 4.0     | 3316     | -             | 0.0187          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.37.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- 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}
}
```

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