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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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metrics: |
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- accuracy |
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widget: |
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- text: Aku sudah lebih tua dan hidupku sangat berbeda. Aku bisa merasakan betapa |
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takjubnya aku pagi itu |
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- text: Saya merasa cukup href http kata-kata yang tak terucapkan disimpan di dalam |
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- text: Aku melihat ke dalam dompetku dan aku merasakan hawa dingin |
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- text: Aku menurunkan Erik dengan perasaan agak tidak puas dengan malam itu |
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- text: Aku bertanya-tanya apa yang siswa lain di kelasku rasakan ketika aku tidak |
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takut untuk memberikan jawaban di luar sana |
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pipeline_tag: text-classification |
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inference: true |
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base_model: firqaaa/indo-sentence-bert-base |
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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: firqaaa/emotion-bahasa |
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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: 0.718 |
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name: Accuracy |
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--- |
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# SetFit with firqaaa/indo-sentence-bert-base |
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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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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:** [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:** 6 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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| kesedihan | <ul><li>'Saya merasa agak kecewa, saya rasa harus menyerahkan sesuatu yang tidak menarik hanya untuk memenuhi tenggat waktu'</li><li>'Aku merasa seperti aku telah cukup lalai terhadap blogku dan aku hanya mengatakan bahwa kita di sini hidup dan bahagia'</li><li>'Aku tahu dan aku selalu terkoyak karenanya karena aku merasa tidak berdaya dan tidak berguna'</li></ul> | |
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| sukacita | <ul><li>'aku mungkin tidak merasa begitu keren'</li><li>'saya merasa baik-baik saja'</li><li>'saya merasa seperti saya seorang ibu dengan mengorbankan produktivitas'</li></ul> | |
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| cinta | <ul><li>'aku merasa mencintaimu'</li><li>'aku akan merasa sangat nostalgia di usia yang begitu muda'</li><li>'Saya merasa diberkati bahwa saya tinggal di Amerika memiliki keluarga yang luar biasa dan Dorothy Kelsey adalah bagian dari hidup saya'</li></ul> | |
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| amarah | <ul><li>'Aku terlalu memikirkan cara dudukku, suaraku terdengar jika ada makanan di mulutku, dan perasaan bahwa aku harus berjalan ke semua orang agar tidak bersikap kasar'</li><li>'aku merasa memberontak sedikit kesal gila terkurung'</li><li>'Aku merasakan perasaan itu muncul kembali dari perasaan paranoid dan cemburu yang penuh kebencian yang selalu menyiksaku tanpa henti'</li></ul> | |
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| takut | <ul><li>'aku merasa seperti diserang oleh landak titanium'</li><li>'Aku membiarkan diriku memikirkan perilakuku terhadapmu saat kita masih kecil. Aku merasakan campuran aneh antara rasa bersalah dan kekaguman atas ketangguhanmu'</li><li>'saya marah karena majikan saya tidak berinvestasi pada kami sama sekali, gaji pelatihan, kenaikan hari libur bank dan rasanya seperti ketidakadilan sehingga saya merasa tidak berdaya'</li></ul> | |
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| kejutan | <ul><li>'Aku membaca bagian ol feefyefo Aku merasa takjub melihat betapa aku bisa mengoceh dan betapa transparannya aku dalam hidupku'</li><li>'saya menemukan seni di sisi lain saya merasa sangat terkesan dengan karya saya'</li><li>'aku merasa penasaran, bersemangat dan tidak sabar'</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** | 0.718 | |
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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("firqaaa/indo-setfit-bert-base-p3") |
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# Run inference |
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preds = model("Aku melihat ke dalam dompetku dan aku merasakan hawa dingin") |
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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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--> |
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<!-- |
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### Out-of-Scope Use |
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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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## Bias, Risks and Limitations |
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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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### Recommendations |
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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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## 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 | 2 | 16.7928 | 56 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| kesedihan | 300 | |
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| sukacita | 300 | |
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| cinta | 300 | |
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| amarah | 300 | |
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| takut | 300 | |
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| kejutan | 300 | |
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### Training Hyperparameters |
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- batch_size: (128, 128) |
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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: 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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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:---------:|:-------------:|:---------------:| |
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| 0.0000 | 1 | 0.2927 | - | |
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| 0.0024 | 50 | 0.2605 | - | |
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| 0.0047 | 100 | 0.2591 | - | |
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| 0.0071 | 150 | 0.2638 | - | |
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| 0.0095 | 200 | 0.245 | - | |
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| 0.0119 | 250 | 0.226 | - | |
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| 0.0142 | 300 | 0.222 | - | |
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| 0.0166 | 350 | 0.1968 | - | |
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| 0.0190 | 400 | 0.1703 | - | |
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| 0.0213 | 450 | 0.1703 | - | |
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| 0.0237 | 500 | 0.1587 | - | |
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| 0.0261 | 550 | 0.1087 | - | |
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| 0.0284 | 600 | 0.1203 | - | |
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| 0.0308 | 650 | 0.0844 | - | |
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| 0.0332 | 700 | 0.0696 | - | |
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| 0.0356 | 750 | 0.0606 | - | |
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| 0.0379 | 800 | 0.0333 | - | |
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| 0.0403 | 850 | 0.0453 | - | |
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| 0.0427 | 900 | 0.033 | - | |
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| 0.0450 | 950 | 0.0142 | - | |
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| 0.0474 | 1000 | 0.004 | - | |
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| 0.0498 | 1050 | 0.0097 | - | |
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| 0.0521 | 1100 | 0.0065 | - | |
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| 0.0545 | 1150 | 0.0081 | - | |
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| 0.0569 | 1200 | 0.0041 | - | |
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| 0.0593 | 1250 | 0.0044 | - | |
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| 0.0616 | 1300 | 0.0013 | - | |
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| 0.0640 | 1350 | 0.0024 | - | |
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| 0.0664 | 1400 | 0.001 | - | |
|
| 0.0687 | 1450 | 0.0012 | - | |
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| 0.0711 | 1500 | 0.0013 | - | |
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| 0.0735 | 1550 | 0.0006 | - | |
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| 0.0759 | 1600 | 0.0033 | - | |
|
| 0.0782 | 1650 | 0.0006 | - | |
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| 0.0806 | 1700 | 0.0013 | - | |
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| 0.0830 | 1750 | 0.0008 | - | |
|
| 0.0853 | 1800 | 0.0006 | - | |
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| 0.0877 | 1850 | 0.0008 | - | |
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| 0.0901 | 1900 | 0.0004 | - | |
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| 0.0924 | 1950 | 0.0005 | - | |
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| 0.0948 | 2000 | 0.0004 | - | |
|
| 0.0972 | 2050 | 0.0002 | - | |
|
| 0.0996 | 2100 | 0.0002 | - | |
|
| 0.1019 | 2150 | 0.0003 | - | |
|
| 0.1043 | 2200 | 0.0006 | - | |
|
| 0.1067 | 2250 | 0.0005 | - | |
|
| 0.1090 | 2300 | 0.0003 | - | |
|
| 0.1114 | 2350 | 0.0018 | - | |
|
| 0.1138 | 2400 | 0.0003 | - | |
|
| 0.1161 | 2450 | 0.0002 | - | |
|
| 0.1185 | 2500 | 0.0018 | - | |
|
| 0.1209 | 2550 | 0.0003 | - | |
|
| 0.1233 | 2600 | 0.0008 | - | |
|
| 0.1256 | 2650 | 0.0002 | - | |
|
| 0.1280 | 2700 | 0.0007 | - | |
|
| 0.1304 | 2750 | 0.006 | - | |
|
| 0.1327 | 2800 | 0.0002 | - | |
|
| 0.1351 | 2850 | 0.0001 | - | |
|
| 0.1375 | 2900 | 0.0001 | - | |
|
| 0.1399 | 2950 | 0.0001 | - | |
|
| 0.1422 | 3000 | 0.0001 | - | |
|
| 0.1446 | 3050 | 0.0001 | - | |
|
| 0.1470 | 3100 | 0.0001 | - | |
|
| 0.1493 | 3150 | 0.0001 | - | |
|
| 0.1517 | 3200 | 0.0002 | - | |
|
| 0.1541 | 3250 | 0.0003 | - | |
|
| 0.1564 | 3300 | 0.0004 | - | |
|
| 0.1588 | 3350 | 0.0001 | - | |
|
| 0.1612 | 3400 | 0.0001 | - | |
|
| 0.1636 | 3450 | 0.0014 | - | |
|
| 0.1659 | 3500 | 0.0005 | - | |
|
| 0.1683 | 3550 | 0.0003 | - | |
|
| 0.1707 | 3600 | 0.0001 | - | |
|
| 0.1730 | 3650 | 0.0001 | - | |
|
| 0.1754 | 3700 | 0.0001 | - | |
|
| 0.1778 | 3750 | 0.0001 | - | |
|
| 0.1801 | 3800 | 0.0001 | - | |
|
| 0.1825 | 3850 | 0.0001 | - | |
|
| 0.1849 | 3900 | 0.0001 | - | |
|
| 0.1873 | 3950 | 0.0001 | - | |
|
| 0.1896 | 4000 | 0.0001 | - | |
|
| 0.1920 | 4050 | 0.0001 | - | |
|
| 0.1944 | 4100 | 0.0003 | - | |
|
| 0.1967 | 4150 | 0.0006 | - | |
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| 0.1991 | 4200 | 0.0001 | - | |
|
| 0.2015 | 4250 | 0.0 | - | |
|
| 0.2038 | 4300 | 0.0 | - | |
|
| 0.2062 | 4350 | 0.0001 | - | |
|
| 0.2086 | 4400 | 0.0 | - | |
|
| 0.2110 | 4450 | 0.0 | - | |
|
| 0.2133 | 4500 | 0.0001 | - | |
|
| 0.2157 | 4550 | 0.0002 | - | |
|
| 0.2181 | 4600 | 0.0003 | - | |
|
| 0.2204 | 4650 | 0.0018 | - | |
|
| 0.2228 | 4700 | 0.0003 | - | |
|
| 0.2252 | 4750 | 0.0145 | - | |
|
| 0.2276 | 4800 | 0.0001 | - | |
|
| 0.2299 | 4850 | 0.0006 | - | |
|
| 0.2323 | 4900 | 0.0001 | - | |
|
| 0.2347 | 4950 | 0.0007 | - | |
|
| 0.2370 | 5000 | 0.0001 | - | |
|
| 0.2394 | 5050 | 0.0 | - | |
|
| 0.2418 | 5100 | 0.0 | - | |
|
| 0.2441 | 5150 | 0.0001 | - | |
|
| 0.2465 | 5200 | 0.0003 | - | |
|
| 0.2489 | 5250 | 0.0 | - | |
|
| 0.2513 | 5300 | 0.0 | - | |
|
| 0.2536 | 5350 | 0.0 | - | |
|
| 0.2560 | 5400 | 0.0 | - | |
|
| 0.2584 | 5450 | 0.0004 | - | |
|
| 0.2607 | 5500 | 0.0 | - | |
|
| 0.2631 | 5550 | 0.0 | - | |
|
| 0.2655 | 5600 | 0.0 | - | |
|
| 0.2678 | 5650 | 0.0 | - | |
|
| 0.2702 | 5700 | 0.0 | - | |
|
| 0.2726 | 5750 | 0.0002 | - | |
|
| 0.2750 | 5800 | 0.0 | - | |
|
| 0.2773 | 5850 | 0.0 | - | |
|
| 0.2797 | 5900 | 0.0 | - | |
|
| 0.2821 | 5950 | 0.0 | - | |
|
| 0.2844 | 6000 | 0.0 | - | |
|
| 0.2868 | 6050 | 0.0 | - | |
|
| 0.2892 | 6100 | 0.0 | - | |
|
| 0.2916 | 6150 | 0.0 | - | |
|
| 0.2939 | 6200 | 0.0 | - | |
|
| 0.2963 | 6250 | 0.0 | - | |
|
| 0.2987 | 6300 | 0.0001 | - | |
|
| 0.3010 | 6350 | 0.0003 | - | |
|
| 0.3034 | 6400 | 0.0048 | - | |
|
| 0.3058 | 6450 | 0.0 | - | |
|
| 0.3081 | 6500 | 0.0 | - | |
|
| 0.3105 | 6550 | 0.0 | - | |
|
| 0.3129 | 6600 | 0.0 | - | |
|
| 0.3153 | 6650 | 0.0 | - | |
|
| 0.3176 | 6700 | 0.0 | - | |
|
| 0.3200 | 6750 | 0.0 | - | |
|
| 0.3224 | 6800 | 0.0 | - | |
|
| 0.3247 | 6850 | 0.0 | - | |
|
| 0.3271 | 6900 | 0.0 | - | |
|
| 0.3295 | 6950 | 0.0 | - | |
|
| 0.3318 | 7000 | 0.0 | - | |
|
| 0.3342 | 7050 | 0.0 | - | |
|
| 0.3366 | 7100 | 0.0 | - | |
|
| 0.3390 | 7150 | 0.0011 | - | |
|
| 0.3413 | 7200 | 0.0002 | - | |
|
| 0.3437 | 7250 | 0.0 | - | |
|
| 0.3461 | 7300 | 0.0 | - | |
|
| 0.3484 | 7350 | 0.0001 | - | |
|
| 0.3508 | 7400 | 0.0001 | - | |
|
| 0.3532 | 7450 | 0.0002 | - | |
|
| 0.3556 | 7500 | 0.0 | - | |
|
| 0.3579 | 7550 | 0.0 | - | |
|
| 0.3603 | 7600 | 0.0 | - | |
|
| 0.3627 | 7650 | 0.0 | - | |
|
| 0.3650 | 7700 | 0.0 | - | |
|
| 0.3674 | 7750 | 0.0 | - | |
|
| 0.3698 | 7800 | 0.0001 | - | |
|
| 0.3721 | 7850 | 0.0 | - | |
|
| 0.3745 | 7900 | 0.0 | - | |
|
| 0.3769 | 7950 | 0.0 | - | |
|
| 0.3793 | 8000 | 0.0 | - | |
|
| 0.3816 | 8050 | 0.0 | - | |
|
| 0.3840 | 8100 | 0.0 | - | |
|
| 0.3864 | 8150 | 0.0 | - | |
|
| 0.3887 | 8200 | 0.0 | - | |
|
| 0.3911 | 8250 | 0.0 | - | |
|
| 0.3935 | 8300 | 0.0 | - | |
|
| 0.3958 | 8350 | 0.0 | - | |
|
| 0.3982 | 8400 | 0.0 | - | |
|
| 0.4006 | 8450 | 0.0 | - | |
|
| 0.4030 | 8500 | 0.0 | - | |
|
| 0.4053 | 8550 | 0.0001 | - | |
|
| 0.4077 | 8600 | 0.0001 | - | |
|
| 0.4101 | 8650 | 0.0008 | - | |
|
| 0.4124 | 8700 | 0.0001 | - | |
|
| 0.4148 | 8750 | 0.0 | - | |
|
| 0.4172 | 8800 | 0.0 | - | |
|
| 0.4196 | 8850 | 0.0001 | - | |
|
| 0.4219 | 8900 | 0.0 | - | |
|
| 0.4243 | 8950 | 0.0 | - | |
|
| 0.4267 | 9000 | 0.0 | - | |
|
| 0.4290 | 9050 | 0.0 | - | |
|
| 0.4314 | 9100 | 0.0 | - | |
|
| 0.4338 | 9150 | 0.0 | - | |
|
| 0.4361 | 9200 | 0.0 | - | |
|
| 0.4385 | 9250 | 0.0 | - | |
|
| 0.4409 | 9300 | 0.0 | - | |
|
| 0.4433 | 9350 | 0.0 | - | |
|
| 0.4456 | 9400 | 0.0 | - | |
|
| 0.4480 | 9450 | 0.0 | - | |
|
| 0.4504 | 9500 | 0.0 | - | |
|
| 0.4527 | 9550 | 0.0 | - | |
|
| 0.4551 | 9600 | 0.0 | - | |
|
| 0.4575 | 9650 | 0.0 | - | |
|
| 0.4598 | 9700 | 0.0 | - | |
|
| 0.4622 | 9750 | 0.0001 | - | |
|
| 0.4646 | 9800 | 0.0 | - | |
|
| 0.4670 | 9850 | 0.0 | - | |
|
| 0.4693 | 9900 | 0.0 | - | |
|
| 0.4717 | 9950 | 0.0 | - | |
|
| 0.4741 | 10000 | 0.0 | - | |
|
| 0.4764 | 10050 | 0.0 | - | |
|
| 0.4788 | 10100 | 0.0006 | - | |
|
| 0.4812 | 10150 | 0.0 | - | |
|
| 0.4835 | 10200 | 0.0 | - | |
|
| 0.4859 | 10250 | 0.0 | - | |
|
| 0.4883 | 10300 | 0.0 | - | |
|
| 0.4907 | 10350 | 0.0 | - | |
|
| 0.4930 | 10400 | 0.0 | - | |
|
| 0.4954 | 10450 | 0.0 | - | |
|
| 0.4978 | 10500 | 0.0 | - | |
|
| 0.5001 | 10550 | 0.0 | - | |
|
| 0.5025 | 10600 | 0.0 | - | |
|
| 0.5049 | 10650 | 0.0 | - | |
|
| 0.5073 | 10700 | 0.0 | - | |
|
| 0.5096 | 10750 | 0.0 | - | |
|
| 0.5120 | 10800 | 0.0 | - | |
|
| 0.5144 | 10850 | 0.0 | - | |
|
| 0.5167 | 10900 | 0.0 | - | |
|
| 0.5191 | 10950 | 0.0 | - | |
|
| 0.5215 | 11000 | 0.0 | - | |
|
| 0.5238 | 11050 | 0.0 | - | |
|
| 0.5262 | 11100 | 0.0 | - | |
|
| 0.5286 | 11150 | 0.0 | - | |
|
| 0.5310 | 11200 | 0.0 | - | |
|
| 0.5333 | 11250 | 0.0 | - | |
|
| 0.5357 | 11300 | 0.0 | - | |
|
| 0.5381 | 11350 | 0.0 | - | |
|
| 0.5404 | 11400 | 0.0 | - | |
|
| 0.5428 | 11450 | 0.0 | - | |
|
| 0.5452 | 11500 | 0.0 | - | |
|
| 0.5475 | 11550 | 0.0 | - | |
|
| 0.5499 | 11600 | 0.0 | - | |
|
| 0.5523 | 11650 | 0.0001 | - | |
|
| 0.5547 | 11700 | 0.0 | - | |
|
| 0.5570 | 11750 | 0.0043 | - | |
|
| 0.5594 | 11800 | 0.0 | - | |
|
| 0.5618 | 11850 | 0.0 | - | |
|
| 0.5641 | 11900 | 0.0 | - | |
|
| 0.5665 | 11950 | 0.0 | - | |
|
| 0.5689 | 12000 | 0.0 | - | |
|
| 0.5713 | 12050 | 0.0 | - | |
|
| 0.5736 | 12100 | 0.0 | - | |
|
| 0.5760 | 12150 | 0.0 | - | |
|
| 0.5784 | 12200 | 0.0 | - | |
|
| 0.5807 | 12250 | 0.0029 | - | |
|
| 0.5831 | 12300 | 0.0 | - | |
|
| 0.5855 | 12350 | 0.0 | - | |
|
| 0.5878 | 12400 | 0.0 | - | |
|
| 0.5902 | 12450 | 0.0 | - | |
|
| 0.5926 | 12500 | 0.0 | - | |
|
| 0.5950 | 12550 | 0.0 | - | |
|
| 0.5973 | 12600 | 0.0 | - | |
|
| 0.5997 | 12650 | 0.0 | - | |
|
| 0.6021 | 12700 | 0.0 | - | |
|
| 0.6044 | 12750 | 0.0 | - | |
|
| 0.6068 | 12800 | 0.0 | - | |
|
| 0.6092 | 12850 | 0.0 | - | |
|
| 0.6115 | 12900 | 0.0 | - | |
|
| 0.6139 | 12950 | 0.0 | - | |
|
| 0.6163 | 13000 | 0.0 | - | |
|
| 0.6187 | 13050 | 0.0 | - | |
|
| 0.6210 | 13100 | 0.0 | - | |
|
| 0.6234 | 13150 | 0.0001 | - | |
|
| 0.6258 | 13200 | 0.0 | - | |
|
| 0.6281 | 13250 | 0.0 | - | |
|
| 0.6305 | 13300 | 0.0 | - | |
|
| 0.6329 | 13350 | 0.0 | - | |
|
| 0.6353 | 13400 | 0.0001 | - | |
|
| 0.6376 | 13450 | 0.0 | - | |
|
| 0.6400 | 13500 | 0.0 | - | |
|
| 0.6424 | 13550 | 0.0 | - | |
|
| 0.6447 | 13600 | 0.0 | - | |
|
| 0.6471 | 13650 | 0.0 | - | |
|
| 0.6495 | 13700 | 0.0 | - | |
|
| 0.6518 | 13750 | 0.0 | - | |
|
| 0.6542 | 13800 | 0.0 | - | |
|
| 0.6566 | 13850 | 0.0 | - | |
|
| 0.6590 | 13900 | 0.0 | - | |
|
| 0.6613 | 13950 | 0.0 | - | |
|
| 0.6637 | 14000 | 0.0 | - | |
|
| 0.6661 | 14050 | 0.0 | - | |
|
| 0.6684 | 14100 | 0.0 | - | |
|
| 0.6708 | 14150 | 0.0 | - | |
|
| 0.6732 | 14200 | 0.0 | - | |
|
| 0.6755 | 14250 | 0.0 | - | |
|
| 0.6779 | 14300 | 0.0 | - | |
|
| 0.6803 | 14350 | 0.0 | - | |
|
| 0.6827 | 14400 | 0.0 | - | |
|
| 0.6850 | 14450 | 0.0 | - | |
|
| 0.6874 | 14500 | 0.0 | - | |
|
| 0.6898 | 14550 | 0.0 | - | |
|
| 0.6921 | 14600 | 0.0 | - | |
|
| 0.6945 | 14650 | 0.0 | - | |
|
| 0.6969 | 14700 | 0.0 | - | |
|
| 0.6993 | 14750 | 0.0 | - | |
|
| 0.7016 | 14800 | 0.0 | - | |
|
| 0.7040 | 14850 | 0.0 | - | |
|
| 0.7064 | 14900 | 0.0 | - | |
|
| 0.7087 | 14950 | 0.0 | - | |
|
| 0.7111 | 15000 | 0.0 | - | |
|
| 0.7135 | 15050 | 0.0 | - | |
|
| 0.7158 | 15100 | 0.0 | - | |
|
| 0.7182 | 15150 | 0.0 | - | |
|
| 0.7206 | 15200 | 0.0 | - | |
|
| 0.7230 | 15250 | 0.0 | - | |
|
| 0.7253 | 15300 | 0.0 | - | |
|
| 0.7277 | 15350 | 0.0 | - | |
|
| 0.7301 | 15400 | 0.0 | - | |
|
| 0.7324 | 15450 | 0.0 | - | |
|
| 0.7348 | 15500 | 0.0 | - | |
|
| 0.7372 | 15550 | 0.0 | - | |
|
| 0.7395 | 15600 | 0.0 | - | |
|
| 0.7419 | 15650 | 0.0 | - | |
|
| 0.7443 | 15700 | 0.0 | - | |
|
| 0.7467 | 15750 | 0.0 | - | |
|
| 0.7490 | 15800 | 0.0 | - | |
|
| 0.7514 | 15850 | 0.0 | - | |
|
| 0.7538 | 15900 | 0.0 | - | |
|
| 0.7561 | 15950 | 0.0 | - | |
|
| 0.7585 | 16000 | 0.0 | - | |
|
| 0.7609 | 16050 | 0.0 | - | |
|
| 0.7633 | 16100 | 0.0 | - | |
|
| 0.7656 | 16150 | 0.0 | - | |
|
| 0.7680 | 16200 | 0.0 | - | |
|
| 0.7704 | 16250 | 0.0 | - | |
|
| 0.7727 | 16300 | 0.0 | - | |
|
| 0.7751 | 16350 | 0.0 | - | |
|
| 0.7775 | 16400 | 0.0 | - | |
|
| 0.7798 | 16450 | 0.0 | - | |
|
| 0.7822 | 16500 | 0.0 | - | |
|
| 0.7846 | 16550 | 0.0 | - | |
|
| 0.7870 | 16600 | 0.0 | - | |
|
| 0.7893 | 16650 | 0.0 | - | |
|
| 0.7917 | 16700 | 0.0 | - | |
|
| 0.7941 | 16750 | 0.0 | - | |
|
| 0.7964 | 16800 | 0.0 | - | |
|
| 0.7988 | 16850 | 0.0 | - | |
|
| 0.8012 | 16900 | 0.0 | - | |
|
| 0.8035 | 16950 | 0.0 | - | |
|
| 0.8059 | 17000 | 0.0 | - | |
|
| 0.8083 | 17050 | 0.0 | - | |
|
| 0.8107 | 17100 | 0.0 | - | |
|
| 0.8130 | 17150 | 0.0 | - | |
|
| 0.8154 | 17200 | 0.0 | - | |
|
| 0.8178 | 17250 | 0.0 | - | |
|
| 0.8201 | 17300 | 0.0 | - | |
|
| 0.8225 | 17350 | 0.0 | - | |
|
| 0.8249 | 17400 | 0.0 | - | |
|
| 0.8272 | 17450 | 0.0 | - | |
|
| 0.8296 | 17500 | 0.0 | - | |
|
| 0.8320 | 17550 | 0.0 | - | |
|
| 0.8344 | 17600 | 0.0 | - | |
|
| 0.8367 | 17650 | 0.0 | - | |
|
| 0.8391 | 17700 | 0.0 | - | |
|
| 0.8415 | 17750 | 0.0 | - | |
|
| 0.8438 | 17800 | 0.0 | - | |
|
| 0.8462 | 17850 | 0.0 | - | |
|
| 0.8486 | 17900 | 0.0 | - | |
|
| 0.8510 | 17950 | 0.0 | - | |
|
| 0.8533 | 18000 | 0.0 | - | |
|
| 0.8557 | 18050 | 0.0 | - | |
|
| 0.8581 | 18100 | 0.0 | - | |
|
| 0.8604 | 18150 | 0.0 | - | |
|
| 0.8628 | 18200 | 0.0 | - | |
|
| 0.8652 | 18250 | 0.0 | - | |
|
| 0.8675 | 18300 | 0.0 | - | |
|
| 0.8699 | 18350 | 0.0 | - | |
|
| 0.8723 | 18400 | 0.0 | - | |
|
| 0.8747 | 18450 | 0.0 | - | |
|
| 0.8770 | 18500 | 0.0 | - | |
|
| 0.8794 | 18550 | 0.0 | - | |
|
| 0.8818 | 18600 | 0.0 | - | |
|
| 0.8841 | 18650 | 0.0 | - | |
|
| 0.8865 | 18700 | 0.0 | - | |
|
| 0.8889 | 18750 | 0.0 | - | |
|
| 0.8912 | 18800 | 0.0 | - | |
|
| 0.8936 | 18850 | 0.0 | - | |
|
| 0.8960 | 18900 | 0.0 | - | |
|
| 0.8984 | 18950 | 0.0 | - | |
|
| 0.9007 | 19000 | 0.0 | - | |
|
| 0.9031 | 19050 | 0.0 | - | |
|
| 0.9055 | 19100 | 0.0 | - | |
|
| 0.9078 | 19150 | 0.0 | - | |
|
| 0.9102 | 19200 | 0.0 | - | |
|
| 0.9126 | 19250 | 0.0 | - | |
|
| 0.9150 | 19300 | 0.0 | - | |
|
| 0.9173 | 19350 | 0.0 | - | |
|
| 0.9197 | 19400 | 0.0 | - | |
|
| 0.9221 | 19450 | 0.0 | - | |
|
| 0.9244 | 19500 | 0.0 | - | |
|
| 0.9268 | 19550 | 0.0 | - | |
|
| 0.9292 | 19600 | 0.0 | - | |
|
| 0.9315 | 19650 | 0.0 | - | |
|
| 0.9339 | 19700 | 0.0 | - | |
|
| 0.9363 | 19750 | 0.0 | - | |
|
| 0.9387 | 19800 | 0.0 | - | |
|
| 0.9410 | 19850 | 0.0 | - | |
|
| 0.9434 | 19900 | 0.0 | - | |
|
| 0.9458 | 19950 | 0.0 | - | |
|
| 0.9481 | 20000 | 0.0 | - | |
|
| 0.9505 | 20050 | 0.0 | - | |
|
| 0.9529 | 20100 | 0.0 | - | |
|
| 0.9552 | 20150 | 0.0 | - | |
|
| 0.9576 | 20200 | 0.0 | - | |
|
| 0.9600 | 20250 | 0.0 | - | |
|
| 0.9624 | 20300 | 0.0 | - | |
|
| 0.9647 | 20350 | 0.0 | - | |
|
| 0.9671 | 20400 | 0.0 | - | |
|
| 0.9695 | 20450 | 0.0 | - | |
|
| 0.9718 | 20500 | 0.0 | - | |
|
| 0.9742 | 20550 | 0.0 | - | |
|
| 0.9766 | 20600 | 0.0 | - | |
|
| 0.9790 | 20650 | 0.0 | - | |
|
| 0.9813 | 20700 | 0.0 | - | |
|
| 0.9837 | 20750 | 0.0 | - | |
|
| 0.9861 | 20800 | 0.0 | - | |
|
| 0.9884 | 20850 | 0.0 | - | |
|
| 0.9908 | 20900 | 0.0 | - | |
|
| 0.9932 | 20950 | 0.0 | - | |
|
| 0.9955 | 21000 | 0.0 | - | |
|
| 0.9979 | 21050 | 0.0 | - | |
|
| **1.0** | **21094** | **-** | **0.2251** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
### Framework Versions |
|
- Python: 3.10.13 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 2.2.2 |
|
- Transformers: 4.36.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Datasets: 2.16.1 |
|
- Tokenizers: 0.15.0 |
|
|
|
## 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} |
|
} |
|
``` |
|
|
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