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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: Mujhe apne galtiyon ka ehsaas hai aur main unke liye maafi chahta hoon. |
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- text: Mujhe yeh step samajhne mein dikkat ho rahi hai, kya aap madad kar sakte hain? |
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- text: Mujhe abhi tak kuch update kyun nahi mila, yeh bahut frustrating hai. |
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- text: Is app ka loading time mujhe thoda zyada lagta hai. |
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- text: Kya aap mujhe is event ki timing bata sakte hain? |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli |
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model-index: |
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- name: SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.32 |
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name: Accuracy |
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--- |
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# SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 19 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 6 | <ul><li>'Chalo, alvida bolte hain!'</li><li>'Phir se baat karte hain!'</li><li>'Adieu, aapka din shubh ho!'</li></ul> | |
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| 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> | |
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| 10 | <ul><li>'Main aapki baat se sehmat hoon.'</li><li>'Mujhe yeh batayein ki meri booking sahi hai na?'</li></ul> | |
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| 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> | |
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| 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> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.32 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("rbojja/FT-mDeBERTa-v3-base-mnli-xnli") |
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# Run inference |
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preds = model("Kya aap mujhe is event ki timing bata sakte hain?") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:-------|:----| |
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| Word count | 3 | 9.76 | 15 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 6 | |
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| 1 | 3 | |
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| 2 | 3 | |
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| 3 | 5 | |
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| 4 | 7 | |
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| 5 | 3 | |
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| 6 | 6 | |
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| 7 | 8 | |
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| 8 | 6 | |
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| 9 | 2 | |
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| 10 | 2 | |
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| 11 | 5 | |
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| 12 | 6 | |
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| 13 | 5 | |
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| 14 | 9 | |
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| 15 | 9 | |
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| 16 | 9 | |
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| 17 | 3 | |
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| 18 | 3 | |
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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- num_epochs: (1, 16) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0017 | 1 | 0.2335 | - | |
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| 0.0853 | 50 | 0.2514 | - | |
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| 0.1706 | 100 | 0.1619 | - | |
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| 0.2560 | 150 | 0.1124 | - | |
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| 0.3413 | 200 | 0.078 | - | |
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| 0.4266 | 250 | 0.0623 | - | |
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| 0.5119 | 300 | 0.0576 | - | |
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| 0.5973 | 350 | 0.0421 | - | |
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| 0.6826 | 400 | 0.0391 | - | |
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| 0.7679 | 450 | 0.0386 | - | |
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| 0.8532 | 500 | 0.0302 | - | |
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| 0.9386 | 550 | 0.0245 | - | |
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### Framework Versions |
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- Python: 3.10.16 |
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- SetFit: 1.1.1 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.46.3 |
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- PyTorch: 2.5.1+cpu |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.20.3 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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