---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Mujhe apne galtiyon ka ehsaas hai aur main unke liye maafi chahta hoon.
- text: Mujhe yeh step samajhne mein dikkat ho rahi hai, kya aap madad kar sakte hain?
- text: Mujhe abhi tak kuch update kyun nahi mila, yeh bahut frustrating hai.
- text: Is app ka loading time mujhe thoda zyada lagta hai.
- text: Kya aap mujhe is event ki timing bata sakte hain?
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
model-index:
- name: SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.32
      name: Accuracy
---

# SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) 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:** [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 19 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                                                                                                                                                                                                                                           |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4     | <ul><li>'Yeh rahin wo steps jisse aap apni payment kar sakte hain.'</li><li>'Kya aap mujhe yeh batane ka tarika thoda aasan kar sakte hain?'</li><li>'Is option ke madhyam se aap apni queries kaise solve kar sakte hain, jaan lijiye.'</li></ul> |
| 16    | <ul><li>'Aapke feedback ko humne dhyan mein rakha hai.'</li><li>'Yeh galti humare systems ki wajah se hui hai.'</li><li>'Mujhe is samasya ko suljhane mein zyada samay lena nahi chahiye tha.'</li></ul>                                           |
| 8     | <ul><li>'Main aapko pareshan karne ke liye maafi chahta hoon.'</li><li>'Humein is samasya ke liye maafi chahiye.'</li><li>'Mere kaam se agar aapko takleef hui ho, toh mujhe maaf kar dijiye.'</li></ul>                                           |
| 13    | <ul><li>'Mujhe yeh clarify karne ki zarurat hai ki agla step kya hai?'</li><li>'Mujhe pata karna hai ki maine jo complaint ki thi uska kya hua.'</li><li>'Mujhe bataye ki pehle kitne payments honge iss plan ke liye.'</li></ul>                  |
| 15    | <ul><li>'Yeh features sahi hai, lekin kuch aur additional functionalities honi chahiye.'</li><li>'Product ke size ki jankari hamesha saaf honi chahiye.'</li><li>'Main chahunga ki online form aur simple ho.'</li></ul>                           |
| 12    | <ul><li>'Mujhe product ke sath kuch samasya hai.'</li><li>'Mera phone charging nahi ho raha.'</li><li>'Mujhe courier service mein dikkat hai, report karna hai.'</li></ul>                                                                         |
| 11    | <ul><li>'Mujhe samajh nahi aa raha, is offer mein koi chhupi shartein toh nahi hai?'</li><li>'Kis tarah se main feedback de sakta hoon?'</li><li>'Kya koi referral program hai jo mujhe join karna chahiye?'</li></ul>                             |
| 2     | <ul><li>'Item ke sath saathi accessories nahi mil rahe hain.'</li><li>'Aap logon ne jo samay liya, wo bilkul zyada tha.'</li><li>'Meri order delivery mein bahut der ho gayi hai.'</li></ul>                                                       |
| 18    | <ul><li>'Mujhe yeh bilkul pasand nahi hai ki meri baat ignore ki gayi.'</li><li>'Kam ke liye mera dosto ka support bahut sukhdayak hai.'</li><li>'Aaj ka din kaafi udaas beete raha hai.'</li></ul>                                                |
| 14    | <ul><li>'Kya main kal ki delivery ko agle hafte reschedule kar sakta/sakti hoon?'</li><li>'Mujhe refund ke liye kya documents chahiye?'</li><li>'Kya main appointment ko dobara set kar sakta/sakti hoon?'</li></ul>                               |
| 7     | <ul><li>'Main aapko dhanyavad dena chahta hoon, aapne meri madad ki.'</li><li>'Aapne jo kiya, uske liye aapko sabse pehle prashansha milni chahiye.'</li><li>'Aapka samay dene ke liye abhaar.'</li></ul>                                          |
| 3     | <ul><li>'Mujhe kisi event ke tickets ka status check karna hai.'</li><li>'Kya aap mujhe customer support number de sakte hain?'</li><li>'Main apne account ka balance kaise check kar sakta/sakti hoon?'</li></ul>                                 |
| 5     | <ul><li>'Alvida, tumhara din acha rahe!'</li><li>'Hello! Aaj aap kaise hain?'</li><li>'Swagat hai! Kya main aapki kuch madad kar sakta hoon?'</li></ul>                                                                                            |
| 0     | <ul><li>'Mujhe kuch samajh nahi aa raha hai, kya mujhe thoda aur samjha sakte hain?'</li><li>'Agar main aisa karoon, to kya kuch badal jaayega? Main sure nahi hoon.'</li><li>'Yeh product ki warranty ki details clear nahi hain.'</li></ul>      |
| 6     | <ul><li>'Chalo, alvida bolte hain!'</li><li>'Phir se baat karte hain!'</li><li>'Adieu, aapka din shubh ho!'</li></ul>                                                                                                                              |
| 17    | <ul><li>'Mere account mein login karne mein dikkat aa rahi hai, madad karein.'</li><li>'Mujhe apne account mein login karne mein madad chahiye.'</li><li>'Kya aap mujhe terms and conditions ke details de sakte hain?'</li></ul>                  |
| 10    | <ul><li>'Main aapki baat se sehmat hoon.'</li><li>'Mujhe yeh batayein ki meri booking sahi hai na?'</li></ul>                                                                                                                                      |
| 9     | <ul><li>'Kya aap mujhe yeh concept aur clear kar sakte hain?'</li><li>'Mujhe yeh samajhne mein dikkat ho rahi hai, kya aap vyakhya de sakte hain?'</li></ul>                                                                                       |
| 1     | <ul><li>'Aaj dosto ke sath waqt bitana bahut acha laga.'</li><li>'Aaj baarish me bheegna bahut refreshing tha, mujhe yeh moment pasand aaya.'</li><li>'Aapka support bahut madadgar raha.'</li></ul>                                               |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.32     |

## 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("rbojja/FT-mDeBERTa-v3-base-mnli-xnli")
# Run inference
preds = model("Kya aap mujhe is event ki timing bata sakte hain?")
```

<!--
### 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   | 3   | 9.76   | 15  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 6                     |
| 1     | 3                     |
| 2     | 3                     |
| 3     | 5                     |
| 4     | 7                     |
| 5     | 3                     |
| 6     | 6                     |
| 7     | 8                     |
| 8     | 6                     |
| 9     | 2                     |
| 10    | 2                     |
| 11    | 5                     |
| 12    | 6                     |
| 13    | 5                     |
| 14    | 9                     |
| 15    | 9                     |
| 16    | 9                     |
| 17    | 3                     |
| 18    | 3                     |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0017 | 1    | 0.2335        | -               |
| 0.0853 | 50   | 0.2514        | -               |
| 0.1706 | 100  | 0.1619        | -               |
| 0.2560 | 150  | 0.1124        | -               |
| 0.3413 | 200  | 0.078         | -               |
| 0.4266 | 250  | 0.0623        | -               |
| 0.5119 | 300  | 0.0576        | -               |
| 0.5973 | 350  | 0.0421        | -               |
| 0.6826 | 400  | 0.0391        | -               |
| 0.7679 | 450  | 0.0386        | -               |
| 0.8532 | 500  | 0.0302        | -               |
| 0.9386 | 550  | 0.0245        | -               |

### Framework Versions
- Python: 3.10.16
- SetFit: 1.1.1
- Sentence Transformers: 3.3.1
- Transformers: 4.46.3
- PyTorch: 2.5.1+cpu
- Datasets: 3.2.0
- Tokenizers: 0.20.3

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->