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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: ' "Ein Tempolimit auf deutschen Autobahnen wäre ein Schlag ins Gesicht aller |
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Autofahrer, die Freiheit und Unabhängigkeit schätzen."' |
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- text: Die Bundesregierung prüft derzeit mehrere Gesetzesinitiativen, die ein generelles |
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Tempolimit auf deutschen Autobahnen vorsehen. |
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- text: ' Das Tempolimit auf Autobahnen würde die Freiheit der Autofahrer massiv einschränken!' |
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- text: '"Während sich unsere Politiker auf ihren Klimakonferenzen über die Notwendigkeit |
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neuer Heizungssysteme unterhalten, vergessen sie dabei geflissentlich, dass die |
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einfache Frau Schmidt oder der einfache Herr Müller bald jeden zweiten Lohnscheck |
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direkt in die Kasse des Heizungsexperten oder des Energiekonzerns überweisen werden."' |
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- text: ' "Das geplante Heizungsgesetz ist ein weiterer Schritt in Richtung staatlicher |
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Bevormundung und wird die Bürger in die Armut treiben."' |
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metrics: |
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- accuracy |
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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: sentence-transformers/all-MiniLM-L6-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/all-MiniLM-L6-v2 |
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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: 0.931899641577061 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-MiniLM-L6-v2 |
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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 [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
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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:** 256 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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| neutral | <ul><li>' Die Aktionen von Klima-Aktivismus-Gruppen wie Fridays for Future oder die Letzte Generation polarisieren die Öffentlichkeit, während sie gleichzeitig wichtige Diskussionen über den Klimawandel anstoßen.'</li><li>'Die Diskussion um ein generelles Tempolimit auf Autobahnen hat in den vergangenen Wochen an Fahrt gewonnen und sowohl Befürworter als auch Gegner haben ihre Positionen deutlich gemacht.'</li><li>' "Das geplante Heizungsgesetz sieht vor, dass ab 2024 in Neubauten und bei der Sanierung von Bestandsgebäuden verstärkt auf Wärmepumpen gesetzt werden soll."'</li></ul> | |
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| supportive | <ul><li>'Die Einführung eines generellen Tempolimits auf deutschen Autobahnen würde nicht nur zu einer Senkung des Kraftstoffverbrauchs und der Treibhausgasemissionen führen, sondern auch die Verkehrssicherheit erhöhen.'</li><li>' "Ein nationales Tempolimit auf Autobahnen könnte laut Experten die Verkehrssicherheit erheblich verbessern und gleichzeitig den CO2-Ausstoß reduzieren."'</li><li>' "Das geplante Heizungsgesetz könnte einen wichtigen Beitrag zur Reduzierung von CO2-Emissionen leisten und somit einen bedeutenden Schritt in Richtung Klimaneutralität darstellen."'</li></ul> | |
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| opposed | <ul><li>'Die Freiheit der Straße, ein Stück deutscher Identität, das in Gefahr geraten könnte, wenn die politischen Tempolimit-Fanatiker ihren Willen durchsetzen.'</li><li>' "Es reicht! Wann hören diese Klima-Aktivisten endlich auf, unsere Straßen zu blockieren und den Alltag der hart arbeitenden Bürger zu stören?"'</li><li>'„Die Blockaden von Straßen und Autobahnen durch die Letzte Generation sorgen für tägliche Nervosität bei Pendler und Anwohner, die sich fragen, wann diese ständigen Behinderungen endlich ein Ende haben werden.“'</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** | 0.9319 | |
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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("cbpuschmann/MiniLM-klimacoder_v0.5") |
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# Run inference |
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preds = model(" Das Tempolimit auf Autobahnen würde die Freiheit der Autofahrer massiv einschränken!") |
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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 | 11 | 25.5421 | 57 | |
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| Label | Training Sample Count | |
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|:-----------|:----------------------| |
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| neutral | 326 | |
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| opposed | 394 | |
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| supportive | 396 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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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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- l2_weight: 0.01 |
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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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| 0.0000 | 1 | 0.2393 | - | |
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| 0.0019 | 50 | 0.2748 | - | |
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| 0.0039 | 100 | 0.2607 | - | |
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| 0.0058 | 150 | 0.2486 | - | |
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| 0.0077 | 200 | 0.2465 | - | |
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| 0.0097 | 250 | 0.246 | - | |
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| 0.0116 | 300 | 0.2454 | - | |
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| 0.0135 | 350 | 0.2406 | - | |
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| 0.0155 | 400 | 0.235 | - | |
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| 0.0174 | 450 | 0.2269 | - | |
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| 0.0193 | 500 | 0.2184 | - | |
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| 0.0213 | 550 | 0.2095 | - | |
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| 0.0232 | 600 | 0.1833 | - | |
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| 0.0251 | 650 | 0.1777 | - | |
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| 0.0271 | 700 | 0.1548 | - | |
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| 0.0290 | 750 | 0.1464 | - | |
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| 0.0310 | 800 | 0.1326 | - | |
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| 0.0329 | 850 | 0.1304 | - | |
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| 0.0348 | 900 | 0.1237 | - | |
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| 0.0368 | 950 | 0.1163 | - | |
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| 0.0387 | 1000 | 0.1129 | - | |
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| 0.0406 | 1050 | 0.1017 | - | |
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| 0.0426 | 1100 | 0.0907 | - | |
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| 0.0445 | 1150 | 0.0857 | - | |
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| 0.0464 | 1200 | 0.0645 | - | |
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| 0.0484 | 1250 | 0.0641 | - | |
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| 0.0503 | 1300 | 0.0514 | - | |
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| 0.0522 | 1350 | 0.0442 | - | |
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| 0.0542 | 1400 | 0.0342 | - | |
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| 0.0561 | 1450 | 0.0291 | - | |
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| 0.0580 | 1500 | 0.0243 | - | |
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| 0.0600 | 1550 | 0.0185 | - | |
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| 0.0619 | 1600 | 0.0142 | - | |
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| 0.0638 | 1650 | 0.0092 | - | |
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| 0.0658 | 1700 | 0.0112 | - | |
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| 0.0677 | 1750 | 0.0076 | - | |
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| 0.0696 | 1800 | 0.0046 | - | |
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| 0.0716 | 1850 | 0.0038 | - | |
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| 0.0735 | 1900 | 0.0025 | - | |
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| 0.0754 | 1950 | 0.0028 | - | |
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| 0.0774 | 2000 | 0.0034 | - | |
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| 0.0793 | 2050 | 0.0022 | - | |
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| 0.0812 | 2100 | 0.0028 | - | |
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| 0.0832 | 2150 | 0.0025 | - | |
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| 0.0851 | 2200 | 0.0025 | - | |
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| 0.0870 | 2250 | 0.0011 | - | |
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| 0.0890 | 2300 | 0.0013 | - | |
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| 0.0909 | 2350 | 0.0019 | - | |
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| 0.0929 | 2400 | 0.0006 | - | |
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| 0.0948 | 2450 | 0.0013 | - | |
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| 0.0967 | 2500 | 0.0005 | - | |
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| 0.0987 | 2550 | 0.0006 | - | |
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| 0.1006 | 2600 | 0.0012 | - | |
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| 0.1025 | 2650 | 0.0016 | - | |
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| 0.1045 | 2700 | 0.0005 | - | |
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| 0.1064 | 2750 | 0.0004 | - | |
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| 0.1083 | 2800 | 0.0003 | - | |
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| 0.1103 | 2850 | 0.0008 | - | |
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| 0.1122 | 2900 | 0.001 | - | |
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| 0.1141 | 2950 | 0.0018 | - | |
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| 0.1161 | 3000 | 0.0005 | - | |
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| 0.1180 | 3050 | 0.0002 | - | |
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| 0.1199 | 3100 | 0.0005 | - | |
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| 0.1219 | 3150 | 0.0006 | - | |
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| 0.1238 | 3200 | 0.0017 | - | |
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| 0.1257 | 3250 | 0.0009 | - | |
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| 0.1277 | 3300 | 0.0026 | - | |
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| 0.1296 | 3350 | 0.0008 | - | |
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| 0.1315 | 3400 | 0.0009 | - | |
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| 0.1335 | 3450 | 0.0013 | - | |
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| 0.1354 | 3500 | 0.0009 | - | |
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| 0.1373 | 3550 | 0.0011 | - | |
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| 0.1393 | 3600 | 0.0008 | - | |
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| 0.1412 | 3650 | 0.0004 | - | |
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| 0.1431 | 3700 | 0.0009 | - | |
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| 0.1451 | 3750 | 0.0008 | - | |
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| 0.1470 | 3800 | 0.0012 | - | |
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| 0.1489 | 3850 | 0.001 | - | |
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| 0.1509 | 3900 | 0.0003 | - | |
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| 0.1528 | 3950 | 0.0005 | - | |
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| 0.1548 | 4000 | 0.0006 | - | |
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| 0.1567 | 4050 | 0.0007 | - | |
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| 0.1586 | 4100 | 0.0009 | - | |
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| 0.1606 | 4150 | 0.0003 | - | |
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| 0.1625 | 4200 | 0.0001 | - | |
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| 0.1644 | 4250 | 0.0011 | - | |
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| 0.1664 | 4300 | 0.0004 | - | |
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| 0.1683 | 4350 | 0.0005 | - | |
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| 0.1702 | 4400 | 0.001 | - | |
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| 0.1722 | 4450 | 0.0001 | - | |
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| 0.1741 | 4500 | 0.0001 | - | |
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| 0.1760 | 4550 | 0.0001 | - | |
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| 0.1780 | 4600 | 0.0007 | - | |
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| 0.1799 | 4650 | 0.0001 | - | |
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| 0.1818 | 4700 | 0.0 | - | |
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| 0.1838 | 4750 | 0.0 | - | |
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| 0.1857 | 4800 | 0.0001 | - | |
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| 0.1876 | 4850 | 0.0001 | - | |
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| 0.1896 | 4900 | 0.0 | - | |
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| 0.1915 | 4950 | 0.0002 | - | |
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| 0.1934 | 5000 | 0.0008 | - | |
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| 0.1954 | 5050 | 0.0006 | - | |
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| 0.1973 | 5100 | 0.0001 | - | |
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| 0.1992 | 5150 | 0.0 | - | |
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| 0.2012 | 5200 | 0.0 | - | |
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| 0.2031 | 5250 | 0.0006 | - | |
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| 0.2050 | 5300 | 0.0009 | - | |
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| 0.2070 | 5350 | 0.0001 | - | |
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| 0.2089 | 5400 | 0.0004 | - | |
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| 0.2108 | 5450 | 0.0032 | - | |
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| 0.2128 | 5500 | 0.0029 | - | |
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| 0.2147 | 5550 | 0.001 | - | |
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| 0.2167 | 5600 | 0.0014 | - | |
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| 0.2186 | 5650 | 0.0004 | - | |
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| 0.2205 | 5700 | 0.0034 | - | |
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| 0.2225 | 5750 | 0.0003 | - | |
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| 0.2244 | 5800 | 0.0002 | - | |
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| 0.2263 | 5850 | 0.0001 | - | |
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| 0.2283 | 5900 | 0.0 | - | |
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| 0.2302 | 5950 | 0.0 | - | |
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| 0.2321 | 6000 | 0.0 | - | |
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| 0.2341 | 6050 | 0.0 | - | |
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| 0.2360 | 6100 | 0.0 | - | |
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| 0.2379 | 6150 | 0.0 | - | |
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| 0.2399 | 6200 | 0.0 | - | |
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| 0.2418 | 6250 | 0.0 | - | |
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| 0.2437 | 6300 | 0.0001 | - | |
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| 0.2457 | 6350 | 0.0024 | - | |
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| 0.2476 | 6400 | 0.0009 | - | |
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| 0.2495 | 6450 | 0.0005 | - | |
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| 0.2515 | 6500 | 0.0016 | - | |
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| 0.2534 | 6550 | 0.0003 | - | |
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| 0.2553 | 6600 | 0.0001 | - | |
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| 0.2573 | 6650 | 0.0 | - | |
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| 0.2592 | 6700 | 0.0 | - | |
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| 0.2611 | 6750 | 0.0 | - | |
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| 0.2631 | 6800 | 0.0 | - | |
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| 0.2650 | 6850 | 0.0 | - | |
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| 0.2669 | 6900 | 0.0 | - | |
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| 0.2689 | 6950 | 0.0 | - | |
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| 0.2708 | 7000 | 0.0 | - | |
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| 0.2727 | 7050 | 0.0 | - | |
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| 0.2747 | 7100 | 0.0 | - | |
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| 0.2766 | 7150 | 0.0 | - | |
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| 0.2786 | 7200 | 0.0 | - | |
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| 0.2805 | 7250 | 0.0002 | - | |
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| 0.2824 | 7300 | 0.0006 | - | |
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| 0.2844 | 7350 | 0.0008 | - | |
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| 0.2863 | 7400 | 0.0013 | - | |
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| 0.2882 | 7450 | 0.0001 | - | |
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| 0.2902 | 7500 | 0.0005 | - | |
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| 0.2921 | 7550 | 0.0 | - | |
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| 0.2940 | 7600 | 0.0 | - | |
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| 0.2960 | 7650 | 0.0 | - | |
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| 0.2979 | 7700 | 0.0006 | - | |
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| 0.2998 | 7750 | 0.0 | - | |
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| 0.3018 | 7800 | 0.0 | - | |
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| 0.3037 | 7850 | 0.0 | - | |
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| 0.3056 | 7900 | 0.0 | - | |
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| 0.3076 | 7950 | 0.0 | - | |
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| 0.3095 | 8000 | 0.0 | - | |
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| 0.3114 | 8050 | 0.0 | - | |
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| 0.3134 | 8100 | 0.0 | - | |
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| 0.3153 | 8150 | 0.0 | - | |
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| 0.3172 | 8200 | 0.0 | - | |
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| 0.3192 | 8250 | 0.0 | - | |
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| 0.3211 | 8300 | 0.0 | - | |
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| 0.3230 | 8350 | 0.0 | - | |
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| 0.3250 | 8400 | 0.0 | - | |
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| 0.3269 | 8450 | 0.0 | - | |
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| 0.3288 | 8500 | 0.0 | - | |
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| 0.3308 | 8550 | 0.0 | - | |
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| 0.3327 | 8600 | 0.0 | - | |
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| 0.3346 | 8650 | 0.0004 | - | |
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| 0.3366 | 8700 | 0.0 | - | |
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| 0.3385 | 8750 | 0.0 | - | |
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| 0.3405 | 8800 | 0.0 | - | |
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| 0.3424 | 8850 | 0.0 | - | |
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| 0.3443 | 8900 | 0.0 | - | |
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| 0.3463 | 8950 | 0.0 | - | |
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| 0.3482 | 9000 | 0.0 | - | |
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| 0.3501 | 9050 | 0.0 | - | |
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| 0.3521 | 9100 | 0.0001 | - | |
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| 0.3540 | 9150 | 0.0037 | - | |
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| 0.3559 | 9200 | 0.0013 | - | |
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| 0.3579 | 9250 | 0.0007 | - | |
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| 0.3598 | 9300 | 0.0032 | - | |
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| 0.3617 | 9350 | 0.0006 | - | |
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| 0.3637 | 9400 | 0.0007 | - | |
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| 0.3656 | 9450 | 0.0 | - | |
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| 0.3675 | 9500 | 0.0006 | - | |
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| 0.3695 | 9550 | 0.0001 | - | |
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| 0.3714 | 9600 | 0.0004 | - | |
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| 0.3733 | 9650 | 0.0001 | - | |
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| 0.3753 | 9700 | 0.0001 | - | |
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| 0.3772 | 9750 | 0.0 | - | |
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| 0.3791 | 9800 | 0.0 | - | |
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| 0.3811 | 9850 | 0.0 | - | |
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| 0.3830 | 9900 | 0.0 | - | |
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| 0.3849 | 9950 | 0.0 | - | |
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| 0.3869 | 10000 | 0.0 | - | |
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| 0.3888 | 10050 | 0.0 | - | |
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| 0.3907 | 10100 | 0.0 | - | |
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| 0.3927 | 10150 | 0.0 | - | |
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| 0.3946 | 10200 | 0.0 | - | |
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| 0.3965 | 10250 | 0.0 | - | |
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| 0.3985 | 10300 | 0.0 | - | |
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| 0.4004 | 10350 | 0.0 | - | |
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| 0.4024 | 10400 | 0.0 | - | |
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| 0.4043 | 10450 | 0.0 | - | |
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| 0.4062 | 10500 | 0.0 | - | |
|
| 0.4082 | 10550 | 0.0 | - | |
|
| 0.4101 | 10600 | 0.0 | - | |
|
| 0.4120 | 10650 | 0.0 | - | |
|
| 0.4140 | 10700 | 0.0 | - | |
|
| 0.4159 | 10750 | 0.0 | - | |
|
| 0.4178 | 10800 | 0.0 | - | |
|
| 0.4198 | 10850 | 0.0 | - | |
|
| 0.4217 | 10900 | 0.0001 | - | |
|
| 0.4236 | 10950 | 0.0 | - | |
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| 0.4256 | 11000 | 0.0 | - | |
|
| 0.4275 | 11050 | 0.0007 | - | |
|
| 0.4294 | 11100 | 0.0043 | - | |
|
| 0.4314 | 11150 | 0.0011 | - | |
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| 0.4333 | 11200 | 0.0013 | - | |
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| 0.4352 | 11250 | 0.0005 | - | |
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| 0.4372 | 11300 | 0.0004 | - | |
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| 0.4391 | 11350 | 0.0001 | - | |
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| 0.4410 | 11400 | 0.0001 | - | |
|
| 0.4430 | 11450 | 0.0 | - | |
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| 0.4449 | 11500 | 0.0001 | - | |
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| 0.4468 | 11550 | 0.0 | - | |
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| 0.4488 | 11600 | 0.0001 | - | |
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| 0.4507 | 11650 | 0.0004 | - | |
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| 0.4526 | 11700 | 0.0001 | - | |
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| 0.4546 | 11750 | 0.0 | - | |
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| 0.4565 | 11800 | 0.0013 | - | |
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| 0.4584 | 11850 | 0.0006 | - | |
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| 0.4604 | 11900 | 0.0001 | - | |
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| 0.4623 | 11950 | 0.0 | - | |
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| 0.4643 | 12000 | 0.0 | - | |
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| 0.4662 | 12050 | 0.0 | - | |
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| 0.4681 | 12100 | 0.0 | - | |
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| 0.4701 | 12150 | 0.0 | - | |
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| 0.4720 | 12200 | 0.0002 | - | |
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| 0.4739 | 12250 | 0.0 | - | |
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| 0.4759 | 12300 | 0.0 | - | |
|
| 0.4778 | 12350 | 0.0 | - | |
|
| 0.4797 | 12400 | 0.0 | - | |
|
| 0.4817 | 12450 | 0.0 | - | |
|
| 0.4836 | 12500 | 0.0 | - | |
|
| 0.4855 | 12550 | 0.0 | - | |
|
| 0.4875 | 12600 | 0.0 | - | |
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| 0.4894 | 12650 | 0.0 | - | |
|
| 0.4913 | 12700 | 0.0 | - | |
|
| 0.4933 | 12750 | 0.0 | - | |
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| 0.4952 | 12800 | 0.0 | - | |
|
| 0.4971 | 12850 | 0.0 | - | |
|
| 0.4991 | 12900 | 0.0 | - | |
|
| 0.5010 | 12950 | 0.0 | - | |
|
| 0.5029 | 13000 | 0.0 | - | |
|
| 0.5049 | 13050 | 0.0 | - | |
|
| 0.5068 | 13100 | 0.0 | - | |
|
| 0.5087 | 13150 | 0.0 | - | |
|
| 0.5107 | 13200 | 0.0 | - | |
|
| 0.5126 | 13250 | 0.0 | - | |
|
| 0.5145 | 13300 | 0.0 | - | |
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| 0.5165 | 13350 | 0.0 | - | |
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| 0.5184 | 13400 | 0.0 | - | |
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| 0.5203 | 13450 | 0.0 | - | |
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| 0.5223 | 13500 | 0.0 | - | |
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| 0.5242 | 13550 | 0.0 | - | |
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| 0.5262 | 13600 | 0.0 | - | |
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| 0.5281 | 13650 | 0.0 | - | |
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| 0.5300 | 13700 | 0.0 | - | |
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| 0.5320 | 13750 | 0.0 | - | |
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| 0.5339 | 13800 | 0.0 | - | |
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| 0.5358 | 13850 | 0.0 | - | |
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| 0.5378 | 13900 | 0.0 | - | |
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| 0.5397 | 13950 | 0.0 | - | |
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| 0.5416 | 14000 | 0.0 | - | |
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| 0.5436 | 14050 | 0.0 | - | |
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| 0.5455 | 14100 | 0.0 | - | |
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| 0.5474 | 14150 | 0.0 | - | |
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| 0.5494 | 14200 | 0.0 | - | |
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| 0.5513 | 14250 | 0.0 | - | |
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| 0.5532 | 14300 | 0.0 | - | |
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| 0.5552 | 14350 | 0.0 | - | |
|
| 0.5571 | 14400 | 0.0 | - | |
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| 0.5590 | 14450 | 0.0 | - | |
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| 0.5610 | 14500 | 0.0 | - | |
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| 0.5629 | 14550 | 0.0 | - | |
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| 0.5648 | 14600 | 0.0 | - | |
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| 0.5668 | 14650 | 0.0 | - | |
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| 0.5687 | 14700 | 0.0 | - | |
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| 0.5706 | 14750 | 0.0 | - | |
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| 0.5726 | 14800 | 0.0 | - | |
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| 0.5745 | 14850 | 0.0 | - | |
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| 0.5764 | 14900 | 0.0 | - | |
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| 0.5784 | 14950 | 0.0 | - | |
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| 0.5803 | 15000 | 0.0 | - | |
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| 0.5823 | 15050 | 0.0 | - | |
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| 0.5842 | 15100 | 0.0 | - | |
|
| 0.5861 | 15150 | 0.0009 | - | |
|
| 0.5881 | 15200 | 0.0006 | - | |
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| 0.5900 | 15250 | 0.0 | - | |
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| 0.5919 | 15300 | 0.0 | - | |
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| 0.5939 | 15350 | 0.0 | - | |
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| 0.5958 | 15400 | 0.0 | - | |
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| 0.5977 | 15450 | 0.0 | - | |
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| 0.5997 | 15500 | 0.0 | - | |
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| 0.6016 | 15550 | 0.0 | - | |
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| 0.6035 | 15600 | 0.0 | - | |
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| 0.6055 | 15650 | 0.0 | - | |
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| 0.6074 | 15700 | 0.0 | - | |
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| 0.6093 | 15750 | 0.0006 | - | |
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| 0.6113 | 15800 | 0.0007 | - | |
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| 0.6132 | 15850 | 0.0 | - | |
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| 0.6151 | 15900 | 0.0 | - | |
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| 0.6171 | 15950 | 0.0 | - | |
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| 0.6190 | 16000 | 0.0 | - | |
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| 0.6209 | 16050 | 0.0 | - | |
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| 0.6229 | 16100 | 0.0 | - | |
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| 0.6248 | 16150 | 0.0 | - | |
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| 0.6267 | 16200 | 0.0 | - | |
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| 0.6287 | 16250 | 0.0 | - | |
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| 0.6306 | 16300 | 0.0 | - | |
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| 0.6325 | 16350 | 0.0 | - | |
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| 0.6345 | 16400 | 0.0 | - | |
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| 0.6364 | 16450 | 0.0 | - | |
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| 0.6383 | 16500 | 0.0 | - | |
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| 0.6403 | 16550 | 0.0 | - | |
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| 0.6422 | 16600 | 0.0 | - | |
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| 0.6442 | 16650 | 0.0 | - | |
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| 0.6461 | 16700 | 0.0 | - | |
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| 0.6480 | 16750 | 0.0 | - | |
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| 0.6500 | 16800 | 0.0 | - | |
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| 0.6519 | 16850 | 0.0 | - | |
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| 0.6538 | 16900 | 0.0 | - | |
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| 0.6558 | 16950 | 0.0 | - | |
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| 0.6577 | 17000 | 0.0 | - | |
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| 0.6596 | 17050 | 0.0 | - | |
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| 0.6616 | 17100 | 0.0 | - | |
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| 0.6635 | 17150 | 0.0 | - | |
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| 0.6654 | 17200 | 0.0 | - | |
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| 0.6674 | 17250 | 0.0 | - | |
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| 0.6693 | 17300 | 0.0 | - | |
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| 0.6712 | 17350 | 0.0 | - | |
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| 0.6732 | 17400 | 0.0 | - | |
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| 0.6751 | 17450 | 0.0 | - | |
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| 0.6770 | 17500 | 0.0 | - | |
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| 0.6790 | 17550 | 0.0 | - | |
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| 0.6809 | 17600 | 0.0 | - | |
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| 0.6828 | 17650 | 0.0 | - | |
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| 0.6848 | 17700 | 0.0 | - | |
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| 0.6867 | 17750 | 0.0 | - | |
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| 0.6886 | 17800 | 0.0 | - | |
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| 0.6906 | 17850 | 0.0 | - | |
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| 0.6925 | 17900 | 0.0 | - | |
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| 0.6944 | 17950 | 0.0 | - | |
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| 0.6964 | 18000 | 0.0 | - | |
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| 0.6983 | 18050 | 0.0007 | - | |
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| 0.7002 | 18100 | 0.0 | - | |
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| 0.7022 | 18150 | 0.0 | - | |
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| 0.7041 | 18200 | 0.0 | - | |
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| 0.7061 | 18250 | 0.0 | - | |
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| 0.7080 | 18300 | 0.0 | - | |
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| 0.7099 | 18350 | 0.0 | - | |
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| 0.7119 | 18400 | 0.0 | - | |
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| 0.7138 | 18450 | 0.0 | - | |
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| 0.7157 | 18500 | 0.0001 | - | |
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| 0.7177 | 18550 | 0.0 | - | |
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| 0.7196 | 18600 | 0.0 | - | |
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| 0.7215 | 18650 | 0.0004 | - | |
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| 0.7235 | 18700 | 0.0 | - | |
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| 0.7254 | 18750 | 0.0 | - | |
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| 0.7273 | 18800 | 0.0 | - | |
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| 0.7293 | 18850 | 0.0 | - | |
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| 0.7312 | 18900 | 0.0 | - | |
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| 0.7331 | 18950 | 0.0 | - | |
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| 0.7351 | 19000 | 0.0 | - | |
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| 0.7370 | 19050 | 0.0 | - | |
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| 0.7389 | 19100 | 0.0 | - | |
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| 0.7409 | 19150 | 0.0 | - | |
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| 0.7428 | 19200 | 0.0 | - | |
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| 0.7447 | 19250 | 0.0 | - | |
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| 0.7467 | 19300 | 0.0 | - | |
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| 0.7486 | 19350 | 0.0 | - | |
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| 0.7505 | 19400 | 0.0 | - | |
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| 0.7525 | 19450 | 0.0 | - | |
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| 0.7544 | 19500 | 0.0 | - | |
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| 0.7563 | 19550 | 0.0 | - | |
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| 0.7583 | 19600 | 0.0 | - | |
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| 0.7602 | 19650 | 0.0 | - | |
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| 0.7621 | 19700 | 0.0 | - | |
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| 0.7641 | 19750 | 0.0 | - | |
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| 0.7660 | 19800 | 0.0 | - | |
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| 0.7680 | 19850 | 0.0 | - | |
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| 0.7699 | 19900 | 0.0 | - | |
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| 0.7718 | 19950 | 0.0 | - | |
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| 0.7738 | 20000 | 0.0 | - | |
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| 0.7757 | 20050 | 0.0 | - | |
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| 0.7776 | 20100 | 0.0 | - | |
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| 0.7796 | 20150 | 0.0 | - | |
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| 0.7815 | 20200 | 0.0 | - | |
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| 0.7834 | 20250 | 0.0 | - | |
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| 0.7854 | 20300 | 0.0 | - | |
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| 0.7873 | 20350 | 0.0 | - | |
|
| 0.7892 | 20400 | 0.0 | - | |
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| 0.7912 | 20450 | 0.0 | - | |
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| 0.7931 | 20500 | 0.0 | - | |
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| 0.7950 | 20550 | 0.0 | - | |
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| 0.7970 | 20600 | 0.0 | - | |
|
| 0.7989 | 20650 | 0.0 | - | |
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| 0.8008 | 20700 | 0.0 | - | |
|
| 0.8028 | 20750 | 0.0 | - | |
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| 0.8047 | 20800 | 0.0 | - | |
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| 0.8066 | 20850 | 0.0 | - | |
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| 0.8086 | 20900 | 0.0 | - | |
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| 0.8105 | 20950 | 0.0 | - | |
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| 0.8124 | 21000 | 0.0 | - | |
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| 0.8144 | 21050 | 0.0 | - | |
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| 0.8163 | 21100 | 0.0 | - | |
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| 0.8182 | 21150 | 0.0 | - | |
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| 0.8202 | 21200 | 0.0 | - | |
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| 0.8221 | 21250 | 0.0 | - | |
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| 0.8240 | 21300 | 0.0 | - | |
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| 0.8260 | 21350 | 0.0 | - | |
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| 0.8279 | 21400 | 0.0 | - | |
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| 0.8299 | 21450 | 0.0 | - | |
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| 0.8318 | 21500 | 0.0 | - | |
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| 0.8337 | 21550 | 0.0 | - | |
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| 0.8357 | 21600 | 0.0 | - | |
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| 0.8376 | 21650 | 0.0 | - | |
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| 0.8395 | 21700 | 0.0 | - | |
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| 0.8415 | 21750 | 0.0 | - | |
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| 0.8434 | 21800 | 0.0 | - | |
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| 0.8453 | 21850 | 0.0 | - | |
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| 0.8473 | 21900 | 0.0 | - | |
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| 0.8492 | 21950 | 0.0 | - | |
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| 0.8511 | 22000 | 0.0 | - | |
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| 0.8531 | 22050 | 0.0 | - | |
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| 0.8550 | 22100 | 0.0 | - | |
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| 0.8569 | 22150 | 0.0 | - | |
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| 0.8589 | 22200 | 0.0 | - | |
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| 0.8608 | 22250 | 0.0 | - | |
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| 0.8627 | 22300 | 0.0 | - | |
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| 0.8647 | 22350 | 0.0 | - | |
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| 0.8666 | 22400 | 0.0 | - | |
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| 0.8685 | 22450 | 0.0 | - | |
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| 0.8705 | 22500 | 0.0 | - | |
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| 0.8724 | 22550 | 0.0 | - | |
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| 0.8743 | 22600 | 0.0 | - | |
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| 0.8763 | 22650 | 0.0 | - | |
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| 0.8782 | 22700 | 0.0 | - | |
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| 0.8801 | 22750 | 0.0 | - | |
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| 0.8821 | 22800 | 0.0 | - | |
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| 0.8840 | 22850 | 0.0 | - | |
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| 0.8859 | 22900 | 0.0 | - | |
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| 0.8879 | 22950 | 0.0 | - | |
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| 0.8898 | 23000 | 0.0 | - | |
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| 0.8918 | 23050 | 0.0 | - | |
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| 0.8937 | 23100 | 0.0 | - | |
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| 0.8956 | 23150 | 0.0 | - | |
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| 0.8976 | 23200 | 0.0 | - | |
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| 0.8995 | 23250 | 0.0 | - | |
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| 0.9014 | 23300 | 0.0 | - | |
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| 0.9034 | 23350 | 0.0 | - | |
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| 0.9053 | 23400 | 0.0 | - | |
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| 0.9072 | 23450 | 0.0 | - | |
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| 0.9092 | 23500 | 0.0 | - | |
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| 0.9111 | 23550 | 0.0 | - | |
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| 0.9130 | 23600 | 0.0 | - | |
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| 0.9150 | 23650 | 0.0 | - | |
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| 0.9169 | 23700 | 0.0 | - | |
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| 0.9188 | 23750 | 0.0 | - | |
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| 0.9208 | 23800 | 0.0 | - | |
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| 0.9227 | 23850 | 0.0 | - | |
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| 0.9246 | 23900 | 0.0 | - | |
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| 0.9266 | 23950 | 0.0 | - | |
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| 0.9285 | 24000 | 0.0 | - | |
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| 0.9304 | 24050 | 0.0 | - | |
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| 0.9324 | 24100 | 0.0 | - | |
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| 0.9343 | 24150 | 0.0 | - | |
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| 0.9362 | 24200 | 0.0 | - | |
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| 0.9382 | 24250 | 0.0 | - | |
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| 0.9401 | 24300 | 0.0 | - | |
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| 0.9420 | 24350 | 0.0 | - | |
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| 0.9440 | 24400 | 0.0 | - | |
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| 0.9459 | 24450 | 0.0 | - | |
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| 0.9478 | 24500 | 0.0 | - | |
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| 0.9498 | 24550 | 0.0 | - | |
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| 0.9517 | 24600 | 0.0 | - | |
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| 0.9537 | 24650 | 0.0 | - | |
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| 0.9556 | 24700 | 0.0 | - | |
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| 0.9575 | 24750 | 0.0 | - | |
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| 0.9595 | 24800 | 0.0 | - | |
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| 0.9614 | 24850 | 0.0 | - | |
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| 0.9633 | 24900 | 0.0 | - | |
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| 0.9653 | 24950 | 0.0 | - | |
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| 0.9672 | 25000 | 0.0 | - | |
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| 0.9691 | 25050 | 0.0 | - | |
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| 0.9711 | 25100 | 0.0 | - | |
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| 0.9730 | 25150 | 0.0 | - | |
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| 0.9749 | 25200 | 0.0 | - | |
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| 0.9769 | 25250 | 0.0 | - | |
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| 0.9788 | 25300 | 0.0 | - | |
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| 0.9807 | 25350 | 0.0 | - | |
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| 0.9827 | 25400 | 0.0 | - | |
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| 0.9846 | 25450 | 0.0 | - | |
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| 0.9865 | 25500 | 0.0 | - | |
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| 0.9885 | 25550 | 0.0 | - | |
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| 0.9904 | 25600 | 0.0 | - | |
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| 0.9923 | 25650 | 0.0 | - | |
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| 0.9943 | 25700 | 0.0 | - | |
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| 0.9962 | 25750 | 0.0 | - | |
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| 0.9981 | 25800 | 0.0 | - | |
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|
### Framework Versions |
|
- Python: 3.10.12 |
|
- SetFit: 1.1.0 |
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- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.42.2 |
|
- PyTorch: 2.5.1+cu121 |
|
- Datasets: 3.2.0 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
|
|
|
### BibTeX |
|
```bibtex |
|
@article{https://doi.org/10.48550/arxiv.2209.11055, |
|
doi = {10.48550/ARXIV.2209.11055}, |
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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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