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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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- absa |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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metrics: |
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- accuracy |
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widget: |
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- text: dan lembut, pai yang dibawa pulang menjadi basah di:Karena kulitnya yang tipis |
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dan lembut, pai yang dibawa pulang menjadi basah di dalam kotaknya. |
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- text: mungkin untuk mengkritik makanannya tersebut.:Dari makanan pembuka yang kami |
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makan, dim sum, dan variasi makanannya lainnya, tidak mungkin untuk mengkritik |
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makanannya tersebut. |
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- text: di sana untuk Spesial Sabtu Malam Setengah Harga, tetapi Selasa:Saya tidak |
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ada di sana untuk Spesial Sabtu Malam Setengah Harga, tetapi Selasa Malam. |
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- text: dan mengatur ulang meja untuk enam orang:Di sebelah kanan saya, nyonya rumah |
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berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba |
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membersihkan dan mengatur ulang meja untuk enam orang. |
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- text: Jika Anda menyukai makanannya dan nilai yang:Jika Anda menyukai makanannya |
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dan nilai yang Anda dapatkan dari beberapa restoran Chinatown, ini bukan tempat |
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untuk Anda. |
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pipeline_tag: text-classification |
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inference: false |
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model-index: |
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- name: SetFit Polarity Model |
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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: 0.6568627450980392 |
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name: Accuracy |
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--- |
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# SetFit Polarity Model |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of classifying aspect polarities. |
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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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This model was trained within the context of a larger system for ABSA, which looks like so: |
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1. Use a spaCy model to select possible aspect span candidates. |
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2. Use a SetFit model to filter these possible aspect span candidates. |
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3. **Use this SetFit model to classify the filtered aspect span candidates.** |
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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:** [Unknown](https://huggingface.co/unknown) --> |
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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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- **spaCy Model:** id_core_news_trf |
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- **SetFitABSA Aspect Model:** [zeroix07/indo-setfit-absa-model-aspect](https://huggingface.co/zeroix07/indo-setfit-absa-model-aspect) |
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- **SetFitABSA Polarity Model:** [zeroix07/indo-setfit-absa-model-polarity](https://huggingface.co/zeroix07/indo-setfit-absa-model-polarity) |
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- **Maximum Sequence Length:** 8192 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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### 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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| positif | <ul><li>'faktor penebusan adalah makanannya, yang berada:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'</li><li>'makanannya benar-benar luar biasa:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li><li>'biasa, dengan dapur yang sangat mumpuni:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li></ul> | |
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| netral | <ul><li>'itu ada di menu atau tidak.:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li><li>'bisa mencicipi kedua daging tersebut).:Favorit kami yang disepakati adalah orrechiete dengan sosis dan ayam (biasanya para pelayan berbaik hati membagi hidangan menjadi dua sehingga Anda bisa mencicipi kedua daging tersebut).'</li><li>'jika Anda suka pizza berkulit tipis.:Pizza adalah yang terbaik jika Anda suka pizza berkulit tipis.'</li></ul> | |
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| negatif | <ul><li>'yang masuk ke koki.:Semua uang digunakan untuk dekorasi interior, tidak ada satupun yang masuk ke koki.'</li><li>'masuk akal meskipun layanannya buruk.:Harganya masuk akal meskipun layanannya buruk.'</li><li>'mayones, lupa roti panggang kami, meninggalkan:Mereka tidak memiliki mayones, lupa roti panggang kami, meninggalkan bahan-bahan (yaitu keju dalam telur dadar), di bawah suhu panas dan daging terlalu matang sehingga hancur di piring ketika Anda menyentuhnya.'</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** | 0.6569 | |
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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 AbsaModel |
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# Download from the 🤗 Hub |
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model = AbsaModel.from_pretrained( |
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"zeroix07/indo-setfit-absa-model-aspect", |
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"zeroix07/indo-setfit-absa-model-polarity", |
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) |
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# Run inference |
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preds = model("The food was great, but the venue is just way too busy.") |
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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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## 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 | 5 | 21.6519 | 45 | |
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| Label | Training Sample Count | |
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|:--------|:----------------------| |
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| konflik | 0 | |
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| negatif | 48 | |
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| netral | 69 | |
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| positif | 64 | |
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### Training Hyperparameters |
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- batch_size: (6, 6) |
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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: True |
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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: 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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| 0.0003 | 1 | 0.2985 | - | |
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| 0.0139 | 50 | 0.14 | - | |
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| 0.0278 | 100 | 0.0913 | - | |
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| 0.0417 | 150 | 0.0447 | - | |
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| 0.0556 | 200 | 0.0932 | - | |
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| 0.0694 | 250 | 0.2864 | - | |
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| 0.0833 | 300 | 0.2556 | - | |
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| 0.0972 | 350 | 0.1447 | - | |
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| 0.1111 | 400 | 0.0084 | - | |
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| 0.125 | 450 | 0.003 | - | |
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| 0.1389 | 500 | 0.0035 | - | |
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| 0.1528 | 550 | 0.0074 | - | |
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| 0.1667 | 600 | 0.0031 | - | |
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| 0.1806 | 650 | 0.0014 | - | |
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| 0.1944 | 700 | 0.002 | - | |
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| 0.2083 | 750 | 0.0006 | - | |
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| 0.2222 | 800 | 0.0005 | - | |
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| 0.2361 | 850 | 0.0005 | - | |
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| 0.25 | 900 | 0.0005 | - | |
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| 0.2639 | 950 | 0.0015 | - | |
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| 0.2778 | 1000 | 0.0007 | - | |
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| 0.2917 | 1050 | 0.0006 | - | |
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| 0.3056 | 1100 | 0.0006 | - | |
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| 0.3194 | 1150 | 0.0007 | - | |
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| 0.3333 | 1200 | 0.0091 | - | |
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| 0.3472 | 1250 | 0.0004 | - | |
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| 0.3611 | 1300 | 0.0003 | - | |
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| 0.375 | 1350 | 0.0005 | - | |
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| 0.3889 | 1400 | 0.0006 | - | |
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| 0.4028 | 1450 | 0.0434 | - | |
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| 0.4167 | 1500 | 0.0006 | - | |
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| 0.4306 | 1550 | 0.0003 | - | |
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| 0.4444 | 1600 | 0.0005 | - | |
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| 0.4583 | 1650 | 0.0004 | - | |
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| 0.4722 | 1700 | 0.0021 | - | |
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| 0.4861 | 1750 | 0.0012 | - | |
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| 0.5 | 1800 | 0.0004 | - | |
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| 0.5139 | 1850 | 0.0005 | - | |
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| 0.5278 | 1900 | 0.0004 | - | |
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| 0.5417 | 1950 | 0.0003 | - | |
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| 0.5556 | 2000 | 0.0003 | - | |
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| 0.5694 | 2050 | 0.0005 | - | |
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| 0.5833 | 2100 | 0.0004 | - | |
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| 0.5972 | 2150 | 0.0004 | - | |
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| 0.6111 | 2200 | 0.0005 | - | |
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| 0.625 | 2250 | 0.0004 | - | |
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| 0.6389 | 2300 | 0.0005 | - | |
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| 0.6528 | 2350 | 0.0004 | - | |
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| 0.6667 | 2400 | 0.0003 | - | |
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| 0.6806 | 2450 | 0.0004 | - | |
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| 0.6944 | 2500 | 0.0007 | - | |
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| 0.7083 | 2550 | 0.0003 | - | |
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| 0.7222 | 2600 | 0.0003 | - | |
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| 0.7361 | 2650 | 0.101 | - | |
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| 0.75 | 2700 | 0.0003 | - | |
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| 0.7639 | 2750 | 0.0004 | - | |
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| 0.7778 | 2800 | 0.0004 | - | |
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| 0.7917 | 2850 | 0.0003 | - | |
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| 0.8056 | 2900 | 0.0004 | - | |
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| 0.8194 | 2950 | 0.0899 | - | |
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| 0.8333 | 3000 | 0.0003 | - | |
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| 0.8472 | 3050 | 0.0002 | - | |
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| 0.8611 | 3100 | 0.0002 | - | |
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| 0.875 | 3150 | 0.0003 | - | |
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| 0.8889 | 3200 | 0.0002 | - | |
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| 0.9028 | 3250 | 0.0003 | - | |
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| 0.9167 | 3300 | 0.0004 | - | |
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| 0.9306 | 3350 | 0.0003 | - | |
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| 0.9444 | 3400 | 0.0003 | - | |
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| 0.9583 | 3450 | 0.0547 | - | |
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| 0.9722 | 3500 | 0.0003 | - | |
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| 0.9861 | 3550 | 0.0004 | - | |
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| 1.0 | 3600 | 0.0002 | - | |
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### Framework Versions |
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- Python: 3.10.13 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- spaCy: 3.7.4 |
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- Transformers: 4.36.2 |
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- PyTorch: 2.1.2 |
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- Datasets: 2.18.0 |
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- Tokenizers: 0.15.2 |
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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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