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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: Etunimi Sukunimi nyt unohdat, että punakapinaan oli sekaantunut myös venäläisiä |
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kommunisteja. Tukivat punakapinallisia asetoimituksin ja lähettämällä upseereita |
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johtamaan kapinaa. Yhteisen kielen puute vain onneksi häiritsi kapinallista työskentelyä. |
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Historiaa sinun kannattaa lukea vähän enemmän. Venäjä on hyökännyt Suomeen kremlin |
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johdolla useastikin. Alkaen jo tsarien ajoista. Pikku ja isoviha esim. |
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- text: 'Etunimi Sukunimi poistitko kommenttisi? Kirjoitin tällaisen vastauksen. Eipä |
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tuolla mitään lähteitä ollut mainittu. Ainoastaan tämä jutun perässä: "Pääkirjoitukset |
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ovat HS:n kannanottoja ajankohtaiseen aiheeseen. Kirjoitukset laatii HS:n pääkirjoitustoimitus, |
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ja ne heijastavat lehden periaatelinjaa."' |
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- text: Voi olla, mutta ennen sen hävityn sodan loppua kuolee paljon ukrainalaisia |
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ja myös venäläisiä eikä Putinia kavereineen saada siitä koskaan vastuuseen 😡 |
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- text: Etunimi Sukunimi 🙋♀️ |
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- text: Koska kolme rokotetta on täysi rokotesarja, niin todennäköisesti kolmesti |
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rokotettuja. Nyt kun niitä tarjotaan kuitenkin kaikille yli 18-vuotiaille. |
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metrics: |
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- metric |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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base_model: TurkuNLP/bert-base-finnish-cased-v1 |
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model-index: |
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- name: SetFit with TurkuNLP/bert-base-finnish-cased-v1 |
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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: metric |
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value: 0.8974712156530338 |
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name: Metric |
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--- |
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# SetFit with TurkuNLP/bert-base-finnish-cased-v1 |
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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 [TurkuNLP/bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) 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:** [TurkuNLP/bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) |
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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:** 2 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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| 0 | <ul><li>'Etunimi Sukunimi herra senkun aloittaa keräyksen♥️'</li><li>'Etunimi Sukunimi venäjän syy hintojen nousu vai syytätkö sodastakin Suomen hallitusta ? 😖'</li><li>'Etunimi Etunimi Alkkiomäki, mikä on vastaus sössöttäjille, mahtava kuulla vastauksesi??'</li></ul> | |
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| 1 | <ul><li>'Etunimi Sukunimi Olikhaan se virve'</li><li>'Etunimi Sukunimi onneks sentään ryyppäämään pääsee, eikä tule siihen ikäviä taukoja'</li><li>'EU-maan ja länteen kallellaan olevan Suomen kimppuun käyminen olisi poliittisesti liian riskaapeliä jopa Putinille, kun Venäjällä on sisäisiäkin ongelmia ihan riittävästi. Täällä on tehty selväksi ettei mikään venäläisten "sotilaallinen apu" ole tervetullutta, eikä Suomen poliittinen tilanne uhkaa Venäjää millään lailla. Valko-Venäjä (eli Lukashenka) on jonkin sortin valtioliitossa Venäjän kanssa ja Ukraina ei kuulu mihinkään valtioliittoon. Ne ovat olleet siis ns. vapaata riistaa Venäjälle.'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Metric | |
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|:--------|:-------| |
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| **all** | 0.8975 | |
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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("Finnish-actions/SetFit-FinBERT1-A2-statement") |
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# Run inference |
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preds = model("Etunimi Sukunimi 🙋♀️") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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*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 | 1 | 20.3115 | 213 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 213 | |
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| 1 | 750 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (4, 4) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 6 |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- evaluation_strategy: epoch |
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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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| 0.0014 | 1 | 0.2419 | - | |
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| 0.0692 | 50 | 0.2635 | - | |
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| 0.1383 | 100 | 0.2284 | - | |
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| 0.2075 | 150 | 0.1852 | - | |
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| 0.2766 | 200 | 0.1301 | - | |
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| 0.3458 | 250 | 0.0882 | - | |
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| 0.4149 | 300 | 0.0549 | - | |
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| 0.4841 | 350 | 0.0318 | - | |
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| 0.5533 | 400 | 0.0325 | - | |
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| 0.6224 | 450 | 0.0097 | - | |
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| 0.6916 | 500 | 0.0061 | - | |
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| 0.7607 | 550 | 0.0021 | - | |
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| 0.8299 | 600 | 0.0015 | - | |
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| 0.8990 | 650 | 0.0004 | - | |
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| 0.9682 | 700 | 0.0006 | - | |
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| 1.0 | 723 | - | 0.2629 | |
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| 1.0373 | 750 | 0.0001 | - | |
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| 1.1065 | 800 | 0.0004 | - | |
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| 1.1757 | 850 | 0.0001 | - | |
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| 1.2448 | 900 | 0.0001 | - | |
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| 1.3140 | 950 | 0.0001 | - | |
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| 1.3831 | 1000 | 0.0001 | - | |
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| 1.4523 | 1050 | 0.0001 | - | |
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| 1.5214 | 1100 | 0.0001 | - | |
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| 1.5906 | 1150 | 0.0001 | - | |
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| 1.6598 | 1200 | 0.0001 | - | |
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| 1.7289 | 1250 | 0.0001 | - | |
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| 1.7981 | 1300 | 0.0 | - | |
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| 1.8672 | 1350 | 0.0 | - | |
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| 1.9364 | 1400 | 0.0 | - | |
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| 2.0 | 1446 | - | 0.2636 | |
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| 2.0055 | 1450 | 0.0 | - | |
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| 2.0747 | 1500 | 0.0 | - | |
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| 2.1438 | 1550 | 0.0001 | - | |
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| 2.2130 | 1600 | 0.0012 | - | |
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| 2.2822 | 1650 | 0.0001 | - | |
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| 2.3513 | 1700 | 0.0 | - | |
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| 2.4205 | 1750 | 0.0 | - | |
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| 2.4896 | 1800 | 0.0 | - | |
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| 2.5588 | 1850 | 0.0 | - | |
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| 2.6279 | 1900 | 0.0 | - | |
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| 2.6971 | 1950 | 0.0 | - | |
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| 2.7663 | 2000 | 0.0 | - | |
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| 2.8354 | 2050 | 0.0 | - | |
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| 2.9046 | 2100 | 0.0 | - | |
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| 2.9737 | 2150 | 0.0 | - | |
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| 3.0 | 2169 | - | 0.2614 | |
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| 3.0429 | 2200 | 0.0 | - | |
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| 3.1120 | 2250 | 0.0 | - | |
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| 3.1812 | 2300 | 0.0 | - | |
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| 3.2503 | 2350 | 0.0 | - | |
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| 3.3195 | 2400 | 0.0 | - | |
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| 3.3887 | 2450 | 0.0 | - | |
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| 3.4578 | 2500 | 0.0 | - | |
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| 3.5270 | 2550 | 0.0 | - | |
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| 3.5961 | 2600 | 0.0 | - | |
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| 3.6653 | 2650 | 0.0 | - | |
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| 3.7344 | 2700 | 0.0 | - | |
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| 3.8036 | 2750 | 0.0 | - | |
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| 3.8728 | 2800 | 0.0 | - | |
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| 3.9419 | 2850 | 0.0 | - | |
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| 4.0 | 2892 | - | 0.2617 | |
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### Framework Versions |
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- Python: 3.11.9 |
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- SetFit: 1.1.3 |
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- Sentence Transformers: 3.2.0 |
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- Transformers: 4.44.0 |
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- PyTorch: 2.4.0+cu124 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.19.1 |
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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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