TRUEnder commited on
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Push model using huggingface_hub.

Browse files
1_Pooling/config.json ADDED
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README.md ADDED
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+ ---
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+ library_name: setfit
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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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+ base_model: firqaaa/indo-sentence-bert-base
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ widget:
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+ - text: halaman 97 - 128 tidak ada , diulang halaman 65 - 96 , pembelian hari minggu
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+ tanggal 24 desember sore sekitar jam 4 pembayaran menggunakan kartu atm bri bersamaan
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+ dengan buku the puppeteer dan sirkus pohon
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+ - text: liverpool sukses di kandang tottenham
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+ - text: hai angga , untuk penerbitan tiket reschedule diharuskan melakukan pembayaran
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+ dulu ya .
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+ - text: sedih kalau umat diprovokasi supaya saling membenci .
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+ - text: berada di lokasi strategis jalan merdeka , berseberangan agak ke samping bandung
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+ indah plaza , tapat sebelah kanan jalan sebelum traffic light , parkir mobil cukup
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+ luas . saus bumbu dan lain-lain disediakan cukup lengkap di lantai bawah . di
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+ lantai atas suasana agak sepi . bakso cukup enak dan terjangkau harga nya tetapi
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+ kuah relatif kurang dan porsi tidak terlalu besar
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+ pipeline_tag: text-classification
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+ inference: true
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+ model-index:
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+ - name: SetFit with firqaaa/indo-sentence-bert-base
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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: 0.8181818181818182
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+ name: Accuracy
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+ - type: precision
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+ value: 0.8181818181818182
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+ name: Precision
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+ - type: recall
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+ value: 0.8181818181818182
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+ name: Recall
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+ - type: f1
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+ value: 0.8181818181818182
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+ name: F1
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+ ---
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+
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+ # SetFit with firqaaa/indo-sentence-bert-base
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+
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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 [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) 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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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
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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:** 3 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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+
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+ ### Model Sources
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+
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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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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 1 | <ul><li>'dirjen per kereta api - an kemenhub zulfikri memastikan tahun 2018 tarif kereta api kelas ekonomi tidak ada kenaikan untuk semua jurusan setelah ada subsidi dari pemerintah untuk pt kan'</li><li>'baik terima kasih banyak'</li><li>'kaitan kalung cantik bahan perak / silver 925'</li></ul> |
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+ | 0 | <ul><li>'jokowi tidak suka sebar isu bohong'</li><li>'masih dengan hawa dingin khas lembang , d sdl menawarkan menu ayam sebagai jagoan nya . ayam ngumpet dan sate goreng adalah 2 menu khas restoran ini . selonjoran di gazebo sambil mencari ayam yang memang seolah ngumpet untuk dimakan menjadikan sensasi tersendiri . dari segi rasa , restoran ini termasuk yang rekomendasi .'</li><li>'menu utama adalah indomie dengan variasi topping . rasanya , . ya indomie . tidak terlalu istimewa . cocok untuk tempat santai dan nongkrong anak anak muda karena penyedia aneka permainan papan . kopi gayo dan latte nya oke . roti bakar green tea juga oke .'</li></ul> |
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+ | 2 | <ul><li>'tetap tidak prabowo walau saya juga tidak suka jokowi'</li><li>'kenapa tidak rekomendasi ? 1 . pempek belum matang , tapi sudah disajikan 2 . pesan sorabi , sudah lama pakai bonus lalat 3 . pesan iga bakar coet , di menu dapat bintang 3 , realita nya tidak enak sama sekali 4 . sorabi kinca dingin , yang datang ternyata sorabi pakai sirop kopyor , nama nya kinca bukan nya air gulu merah ya ? secara keseluruhan baik , tidak puas sama pelayanan dan kualitas makanan di .'</li><li>'nabi muhammad adalah hewan gila seks .'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy | Precision | Recall | F1 |
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+ |:--------|:---------|:----------|:-------|:-------|
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+ | **all** | 0.8182 | 0.8182 | 0.8182 | 0.8182 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-32-shot")
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+ # Run inference
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+ preds = model("liverpool sukses di kandang tottenham")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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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 | 1 | 23.4167 | 79 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 32 |
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+ | 1 | 32 |
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+ | 2 | 32 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 2)
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+ - num_epochs: (6, 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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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:-------:|:-------------:|:---------------:|
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+ | **1.0** | **384** | **0.0002** | **0.1683** |
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+ | 2.0 | 768 | 0.0001 | 0.1732 |
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+ | 3.0 | 1152 | 0.0001 | 0.1739 |
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+ | 4.0 | 1536 | 0.0 | 0.174 |
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+ | 5.0 | 1920 | 0.0001 | 0.1765 |
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+ | 6.0 | 2304 | 0.0 | 0.1767 |
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+
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+ * The bold row denotes the saved checkpoint.
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+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.0.3
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+ - Sentence Transformers: 3.0.1
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+ - Transformers: 4.41.2
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+ - PyTorch: 2.3.0+cu121
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+ - Datasets: 2.19.2
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+ - Tokenizers: 0.19.1
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+
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+ ## Citation
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+
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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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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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