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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: gulungan biasa menjadi gulungan luar dalam,:dibutuhkan biaya tambahan $2 untuk |
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mengubah gulungan biasa menjadi gulungan luar dalam, tetapi gulungan tersebut |
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berukuran lebih dari tiga kali lipat, dan itu bukan ha dari nasi. |
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- text: -a-bagel (baik di:ess-a-bagel (baik di sty-town atau midtown) sejauh ini merupakan |
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bagel terbaik di ny. |
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- text: mahal wadah ini pengelola:ketika kami sedang duduk makan makanan di bawah |
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standar, manajer mulai mencaci-maki beberapa karyawan karena meletakkan wadah |
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bumbu yang salah dan menjelaskan kepada mereka betapa mahal wadah ini pengelola |
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- text: staf sangat akomodatif.:staf sangat akomodatif. |
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- text: layanan luar biasa melayani:makanan india yang enak dan layanan luar biasa |
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melayani |
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pipeline_tag: text-classification |
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inference: false |
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base_model: BAAI/bge-m3 |
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model-index: |
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- name: SetFit Polarity Model with BAAI/bge-m3 |
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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: 0.7898320472083522 |
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name: Accuracy |
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--- |
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# SetFit Polarity Model with BAAI/bge-m3 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) 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. 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 body:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) |
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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:** [firqaaa/indo-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect) |
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- **SetFitABSA Polarity Model:** [firqaaa/indo-setfit-absa-bert-base-restaurants-polarity](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-polarity) |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Number of Classes:** 4 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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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| netral | <ul><li>'sangat kecil sehingga reservasi adalah suatu keharusan:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'di dekat seorang busboy dan mendesiskan rapido:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang.'</li><li>'dan mengatur ulang meja untuk enam orang:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang.'</li></ul> | |
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| negatif | <ul><li>'untuk enam orang nyonya rumah:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang nyonya rumah'</li><li>'setelah berurusan dengan pizza di bawah standar:setelah berurusan dengan pizza di bawah standar di seluruh lingkungan kensington - saya menemukan sedikit tonino.'</li><li>'mereka tidak mejikan bir, anda harus:perhatikan bahwa mereka tidak mejikan bir, anda harus membawa sendiri.'</li></ul> | |
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| positif | <ul><li>'saya tidak menyukai gnocchi.:saya tidak menyukai gnocchi.'</li><li>'dari makanan pembuka yang kami makan:dari makanan pembuka yang kami makan, dim sum, dan variasi makanan lain, tidak mungkin untuk mengkritik makanan tersebut.'</li><li>'kami makan, dim sum, dan variasi:dari makanan pembuka yang kami makan, dim sum, dan variasi makanan lain, tidak mungkin untuk mengkritik makanan tersebut.'</li></ul> | |
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| konflik | <ul><li>'makanan enak tapi jangan:makanan enak tapi jangan datang ke sini dengan perut kosong.'</li><li>'milik pihak rumah tagihan:namun, setiap perselisihan tentang ruu itu diimbangi oleh takaran minuman keras yang anda tuangkan sendiri yang merupakan milik pihak rumah tagihan'</li><li>'layanan meja bisa menjadi sedikit:layanan meja bisa menjadi sedikit lebih penuh perhatian tetapi sebagai seseorang yang juga bekerja di industri jasa, saya mengerti mereka sedang sibuk.'</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** | 0.7898 | |
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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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"firqaaa/setfit-indo-absa-restaurants-aspect", |
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"firqaaa/setfit-indo-absa-restaurants-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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## 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 | 20.6594 | 62 | |
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| Label | Training Sample Count | |
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|:--------|:----------------------| |
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| konflik | 34 | |
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| negatif | 323 | |
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| netral | 258 | |
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| positif | 853 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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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: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:----------:|:--------:|:-------------:|:---------------:| |
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| 0.0000 | 1 | 0.2345 | - | |
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| 0.0006 | 50 | 0.2337 | - | |
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| 0.0013 | 100 | 0.267 | - | |
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| 0.0019 | 150 | 0.2335 | - | |
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| 0.0025 | 200 | 0.2368 | - | |
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| 0.0032 | 250 | 0.2199 | - | |
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| 0.0038 | 300 | 0.2325 | - | |
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| 0.0045 | 350 | 0.2071 | - | |
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| 0.0051 | 400 | 0.2229 | - | |
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| 0.0057 | 450 | 0.1153 | - | |
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| 0.0064 | 500 | 0.1771 | 0.1846 | |
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| 0.0070 | 550 | 0.1612 | - | |
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| 0.0076 | 600 | 0.1487 | - | |
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| 0.0083 | 650 | 0.147 | - | |
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| 0.0089 | 700 | 0.1982 | - | |
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| 0.0096 | 750 | 0.1579 | - | |
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| 0.0102 | 800 | 0.1148 | - | |
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| 0.0108 | 850 | 0.1008 | - | |
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| 0.0115 | 900 | 0.2035 | - | |
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| 0.0121 | 950 | 0.1348 | - | |
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| **0.0127** | **1000** | **0.0974** | **0.182** | |
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| 0.0134 | 1050 | 0.121 | - | |
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| 0.0140 | 1100 | 0.1949 | - | |
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| 0.0147 | 1150 | 0.2424 | - | |
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| 0.0153 | 1200 | 0.0601 | - | |
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| 0.0159 | 1250 | 0.0968 | - | |
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| 0.0166 | 1300 | 0.0137 | - | |
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| 0.0172 | 1350 | 0.034 | - | |
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| 0.0178 | 1400 | 0.1217 | - | |
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| 0.0185 | 1450 | 0.0454 | - | |
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| 0.0191 | 1500 | 0.0397 | 0.2216 | |
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| 0.0198 | 1550 | 0.0226 | - | |
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| 0.0204 | 1600 | 0.0939 | - | |
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| 0.0210 | 1650 | 0.0537 | - | |
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| 0.0217 | 1700 | 0.0566 | - | |
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| 0.0223 | 1750 | 0.162 | - | |
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| 0.0229 | 1800 | 0.0347 | - | |
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| 0.0236 | 1850 | 0.103 | - | |
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| 0.0242 | 1900 | 0.0615 | - | |
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| 0.0249 | 1950 | 0.0589 | - | |
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| 0.0255 | 2000 | 0.1668 | 0.2132 | |
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| 0.0261 | 2050 | 0.1809 | - | |
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| 0.0268 | 2100 | 0.0579 | - | |
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| 0.0274 | 2150 | 0.088 | - | |
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| 0.0280 | 2200 | 0.1047 | - | |
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| 0.0287 | 2250 | 0.1255 | - | |
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| 0.0293 | 2300 | 0.0312 | - | |
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| 0.0300 | 2350 | 0.0097 | - | |
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| 0.0306 | 2400 | 0.0973 | - | |
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| 0.0312 | 2450 | 0.0066 | - | |
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| 0.0319 | 2500 | 0.0589 | 0.2591 | |
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| 0.0325 | 2550 | 0.0529 | - | |
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| 0.0331 | 2600 | 0.0169 | - | |
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| 0.0338 | 2650 | 0.0455 | - | |
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| 0.0344 | 2700 | 0.0609 | - | |
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| 0.0350 | 2750 | 0.1151 | - | |
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| 0.0357 | 2800 | 0.0031 | - | |
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| 0.0363 | 2850 | 0.0546 | - | |
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| 0.0370 | 2900 | 0.0051 | - | |
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| 0.0376 | 2950 | 0.0679 | - | |
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| 0.0382 | 3000 | 0.0046 | 0.2646 | |
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| 0.0389 | 3050 | 0.011 | - | |
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| 0.0395 | 3100 | 0.0701 | - | |
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| 0.0401 | 3150 | 0.0011 | - | |
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| 0.0408 | 3200 | 0.011 | - | |
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| 0.0414 | 3250 | 0.0026 | - | |
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| 0.0421 | 3300 | 0.0027 | - | |
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| 0.0427 | 3350 | 0.0012 | - | |
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| 0.0433 | 3400 | 0.0454 | - | |
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| 0.0440 | 3450 | 0.0011 | - | |
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| 0.0446 | 3500 | 0.0012 | 0.2602 | |
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* The bold row denotes the saved checkpoint. |
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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: 2.2.2 |
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- spaCy: 3.7.4 |
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- Transformers: 4.36.2 |
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- PyTorch: 2.1.2+cu121 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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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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