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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: '"Die jungen Klimaaktivisten haben mit ihren Protestaktionen und Straßenblockaden |
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ein dringend benötigtes Gespräch über die Notwendigkeit von sofortigem Handeln |
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im Kampf gegen den Klimawandel angestoßen."' |
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- text: Die Bundesregierung plant, den Einsatz von Wärmepumpen durch ein neues Heizungsgesetz |
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zu fördern, was laut Experten einen wichtigen Schritt zur Erreichung der Klimaziele |
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darstellen könnte. |
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- text: ' "Das Heizungsgesetz ist nichts weiter als ein weiterer Schritt in Richtung |
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eines grünen Diktats, das die Bürger in die Kälte schickt."' |
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- text: ' Die Klima-Aktivisten von Fridays for Future und der Letzten Generation haben |
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heute in mehreren Städten Proteste organisiert, um auf den Klimawandel aufmerksam |
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zu machen.' |
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- text: ' "Die Diskussion über ein Tempolimit auf Autobahnen spaltet die Gemüter, |
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während Experten auf die potenziellen Vorteile für die Verkehrssicherheit und |
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den Klimaschutz hinweisen."' |
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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/paraphrase-multilingual-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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: 0.953405017921147 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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:** 128 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 Bundesregierung plant, bis 2024 ein sogenanntes Heizungsgesetz vorzulegen, das unter anderem eine flächendeckende Nutzung von Wärmepumpen als Teil eines umfassenden Plans zur Reduzierung der Treibhausgasemissionen im Gebäudesektor vorsehen soll.'</li><li>'"Die Bundesregierung plant, die Einführung von Wärmepumpen für Neubauten und den Austausch alter Heizungsanlagen in Bestandsgebäuden durch ein Gesetz zu forcieren, während Kritiker warnen, dass die Maßnahmen die Belastung für private Haushalte und Unternehmen erhöhen könnten."'</li><li>' Die Diskussion über ein nationales Tempolimit auf Autobahnen spaltet die Gemüter, während Experten die potenziellen Vorteile und Nachteile abwägen.'</li></ul> | |
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| opposed | <ul><li>'"Millionen von Hausbesitzern sollen zu unfreiwilligen Versuchskaninchen für die teuren und unzuverlässlichen Wärmepumpen werden, ohne dass es auch nur einen Hauch von echter Wahlmöglichkeit gibt."'</li><li>'"Die von den Grünen und Linken geträumte Tempodiktatur auf unseren Autobahnen ist nichts als ein weiterer Schritt in Richtung einer überbürokratisierten, unfreien Gesellschaft."'</li><li>'"Die geplanten Vorschriften würden vielen Familien den Traum vom Eigenheim in weite Ferne rücken, da die Kosten für die Installation einer Wärmepumpe oft ein Vielfaches dessen betragen, was ein durchschnittlicher Haushalt in einem Jahr für Heizkosten ausgibt."'</li></ul> | |
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| supportive | <ul><li>'Die Bundesregierung hat mit dem Heizungsgesetz einen wichtigen Schritt in Richtung Klimaneutralität gemacht, indem sie die Verpflichtung zur Nutzung erneuerbarer Wärmequellen bei Neubauten festlegt.'</li><li>'"Ein Tempolimit auf Autobahnen könnte nicht nur die Umweltbelastung verringern, sondern auch die Zahl der Verkehrsunfälle reduzieren und somit Menschenleben retten."'</li><li>' Eine nationale Geschwindigkeitsbegrenzung auf Autobahnen könnte nicht nur die Unfallzahlen senken, sondern auch einen wichtigen Beitrag zum Klimaschutz leisten.'</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** | 0.9534 | |
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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/paraphrase-multilingual-mpnet-klimacoder_v0.8") |
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# Run inference |
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preds = model(" \"Das Heizungsgesetz ist nichts weiter als ein weiterer Schritt in Richtung eines grünen Diktats, das die Bürger in die Kälte schickt.\"") |
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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 | 10 | 25.6541 | 57 | |
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| Label | Training Sample Count | |
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|:-----------|:----------------------| |
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| neutral | 321 | |
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| opposed | 391 | |
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| supportive | 404 | |
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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 | 0.1985 | - | |
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| 0.0019 | 50 | 0.2445 | - | |
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| 0.0039 | 100 | 0.2321 | - | |
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| 0.0058 | 150 | 0.2012 | - | |
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| 0.0077 | 200 | 0.1614 | - | |
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| 0.0097 | 250 | 0.1188 | - | |
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| 0.0116 | 300 | 0.0849 | - | |
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| 0.0136 | 350 | 0.0563 | - | |
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| 0.0155 | 400 | 0.0374 | - | |
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| 0.0174 | 450 | 0.0216 | - | |
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| 0.0194 | 500 | 0.0144 | - | |
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| 0.0213 | 550 | 0.0099 | - | |
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| 0.0232 | 600 | 0.0061 | - | |
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| 0.0252 | 650 | 0.007 | - | |
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| 0.0271 | 700 | 0.0026 | - | |
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| 0.0290 | 750 | 0.0017 | - | |
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| 0.0310 | 800 | 0.0012 | - | |
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| 0.0329 | 850 | 0.0014 | - | |
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| 0.0349 | 900 | 0.002 | - | |
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| 0.0368 | 950 | 0.0008 | - | |
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| 0.0387 | 1000 | 0.0009 | - | |
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| 0.0407 | 1050 | 0.0003 | - | |
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| 0.0426 | 1100 | 0.0007 | - | |
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| 0.0445 | 1150 | 0.0008 | - | |
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| 0.0465 | 1200 | 0.0006 | - | |
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| 0.0484 | 1250 | 0.0002 | - | |
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| 0.0503 | 1300 | 0.0001 | - | |
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| 0.0523 | 1350 | 0.0001 | - | |
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| 0.0542 | 1400 | 0.0001 | - | |
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| 0.0562 | 1450 | 0.0001 | - | |
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| 0.0581 | 1500 | 0.0007 | - | |
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| 0.0600 | 1550 | 0.0005 | - | |
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| 0.0620 | 1600 | 0.0007 | - | |
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| 0.0639 | 1650 | 0.0012 | - | |
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| 0.0658 | 1700 | 0.0007 | - | |
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| 0.0678 | 1750 | 0.0038 | - | |
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| 0.0697 | 1800 | 0.0018 | - | |
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| 0.0716 | 1850 | 0.0049 | - | |
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| 0.0736 | 1900 | 0.0061 | - | |
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| 0.0755 | 1950 | 0.0038 | - | |
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| 0.0775 | 2000 | 0.0037 | - | |
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| 0.0794 | 2050 | 0.0006 | - | |
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| 0.0813 | 2100 | 0.0001 | - | |
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| 0.0833 | 2150 | 0.0 | - | |
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| 0.0852 | 2200 | 0.0 | - | |
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| 0.0871 | 2250 | 0.0 | - | |
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| 0.0891 | 2300 | 0.0 | - | |
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| 0.0910 | 2350 | 0.0 | - | |
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| 0.0929 | 2400 | 0.0 | - | |
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| 0.0949 | 2450 | 0.0 | - | |
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| 0.0968 | 2500 | 0.0 | - | |
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| 0.0987 | 2550 | 0.0 | - | |
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| 0.1007 | 2600 | 0.0 | - | |
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| 0.1026 | 2650 | 0.0 | - | |
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| 0.1046 | 2700 | 0.0 | - | |
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| 0.1065 | 2750 | 0.0 | - | |
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| 0.1084 | 2800 | 0.0 | - | |
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| 0.1104 | 2850 | 0.0 | - | |
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| 0.1123 | 2900 | 0.0 | - | |
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| 0.1142 | 2950 | 0.0 | - | |
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| 0.1162 | 3000 | 0.0 | - | |
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| 0.1181 | 3050 | 0.0 | - | |
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| 0.1200 | 3100 | 0.0 | - | |
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| 0.1220 | 3150 | 0.0 | - | |
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| 0.1239 | 3200 | 0.0 | - | |
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| 0.1259 | 3250 | 0.0 | - | |
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| 0.1278 | 3300 | 0.0 | - | |
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| 0.1297 | 3350 | 0.0 | - | |
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| 0.1317 | 3400 | 0.0 | - | |
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| 0.1336 | 3450 | 0.0 | - | |
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| 0.1355 | 3500 | 0.0 | - | |
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| 0.1375 | 3550 | 0.0 | - | |
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| 0.1394 | 3600 | 0.0 | - | |
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| 0.1413 | 3650 | 0.0 | - | |
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| 0.1433 | 3700 | 0.0 | - | |
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| 0.1452 | 3750 | 0.0 | - | |
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| 0.1472 | 3800 | 0.0 | - | |
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| 0.1491 | 3850 | 0.0 | - | |
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| 0.1510 | 3900 | 0.0 | - | |
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| 0.1530 | 3950 | 0.0 | - | |
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| 0.1549 | 4000 | 0.0 | - | |
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| 0.1568 | 4050 | 0.0 | - | |
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| 0.1588 | 4100 | 0.0 | - | |
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| 0.1607 | 4150 | 0.0 | - | |
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| 0.1626 | 4200 | 0.0 | - | |
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| 0.1646 | 4250 | 0.0 | - | |
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| 0.1665 | 4300 | 0.0 | - | |
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| 0.1685 | 4350 | 0.0 | - | |
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| 0.1704 | 4400 | 0.0 | - | |
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| 0.1723 | 4450 | 0.0 | - | |
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| 0.1743 | 4500 | 0.0 | - | |
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| 0.1762 | 4550 | 0.0 | - | |
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| 0.1781 | 4600 | 0.0 | - | |
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| 0.1801 | 4650 | 0.0 | - | |
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| 0.1820 | 4700 | 0.0 | - | |
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| 0.1839 | 4750 | 0.0 | - | |
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| 0.1859 | 4800 | 0.0 | - | |
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| 0.1878 | 4850 | 0.0 | - | |
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| 0.1898 | 4900 | 0.0 | - | |
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| 0.1917 | 4950 | 0.0 | - | |
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| 0.1936 | 5000 | 0.0 | - | |
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| 0.1956 | 5050 | 0.0 | - | |
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| 0.1975 | 5100 | 0.0 | - | |
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| 0.1994 | 5150 | 0.0 | - | |
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| 0.2014 | 5200 | 0.0 | - | |
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| 0.2033 | 5250 | 0.0 | - | |
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| 0.2052 | 5300 | 0.0 | - | |
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| 0.2072 | 5350 | 0.0 | - | |
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| 0.2091 | 5400 | 0.0 | - | |
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| 0.2111 | 5450 | 0.0 | - | |
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| 0.2130 | 5500 | 0.0 | - | |
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| 0.2149 | 5550 | 0.0 | - | |
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| 0.2169 | 5600 | 0.0 | - | |
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| 0.2188 | 5650 | 0.0 | - | |
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| 0.2207 | 5700 | 0.0 | - | |
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| 0.2227 | 5750 | 0.0 | - | |
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| 0.2246 | 5800 | 0.0 | - | |
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| 0.2265 | 5850 | 0.0 | - | |
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| 0.2285 | 5900 | 0.0 | - | |
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| 0.2304 | 5950 | 0.0 | - | |
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| 0.2324 | 6000 | 0.0 | - | |
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| 0.2343 | 6050 | 0.0 | - | |
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| 0.2362 | 6100 | 0.0 | - | |
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| 0.2382 | 6150 | 0.0 | - | |
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| 0.2401 | 6200 | 0.0 | - | |
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| 0.2420 | 6250 | 0.0 | - | |
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| 0.2440 | 6300 | 0.0 | - | |
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| 0.2459 | 6350 | 0.0 | - | |
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| 0.2478 | 6400 | 0.0 | - | |
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| 0.2498 | 6450 | 0.0 | - | |
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| 0.2517 | 6500 | 0.0 | - | |
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| 0.2536 | 6550 | 0.0 | - | |
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| 0.2556 | 6600 | 0.0 | - | |
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| 0.2575 | 6650 | 0.0 | - | |
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| 0.2595 | 6700 | 0.0 | - | |
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| 0.2614 | 6750 | 0.0 | - | |
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| 0.2633 | 6800 | 0.0 | - | |
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| 0.2653 | 6850 | 0.0 | - | |
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| 0.2672 | 6900 | 0.0 | - | |
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| 0.2691 | 6950 | 0.0 | - | |
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| 0.2711 | 7000 | 0.0 | - | |
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| 0.2730 | 7050 | 0.0 | - | |
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| 0.2749 | 7100 | 0.0 | - | |
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| 0.2769 | 7150 | 0.0 | - | |
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| 0.2788 | 7200 | 0.0 | - | |
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| 0.2808 | 7250 | 0.0 | - | |
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| 0.2827 | 7300 | 0.0 | - | |
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| 0.2846 | 7350 | 0.0 | - | |
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| 0.2866 | 7400 | 0.0 | - | |
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| 0.2885 | 7450 | 0.0 | - | |
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| 0.2904 | 7500 | 0.0 | - | |
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| 0.2924 | 7550 | 0.0 | - | |
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| 0.2943 | 7600 | 0.0 | - | |
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| 0.2962 | 7650 | 0.0 | - | |
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| 0.2982 | 7700 | 0.0 | - | |
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| 0.3001 | 7750 | 0.0 | - | |
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| 0.3021 | 7800 | 0.0 | - | |
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| 0.3040 | 7850 | 0.0 | - | |
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| 0.3059 | 7900 | 0.0 | - | |
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| 0.3079 | 7950 | 0.0 | - | |
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| 0.3098 | 8000 | 0.0 | - | |
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| 0.3117 | 8050 | 0.0 | - | |
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| 0.3137 | 8100 | 0.0 | - | |
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| 0.3156 | 8150 | 0.0 | - | |
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| 0.3175 | 8200 | 0.0 | - | |
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| 0.3195 | 8250 | 0.0 | - | |
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| 0.3214 | 8300 | 0.0 | - | |
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| 0.3234 | 8350 | 0.0 | - | |
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| 0.3253 | 8400 | 0.0 | - | |
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| 0.3272 | 8450 | 0.0 | - | |
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| 0.3292 | 8500 | 0.0 | - | |
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| 0.3311 | 8550 | 0.0 | - | |
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| 0.3330 | 8600 | 0.0 | - | |
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| 0.3350 | 8650 | 0.0 | - | |
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| 0.3369 | 8700 | 0.0 | - | |
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| 0.3388 | 8750 | 0.0 | - | |
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| 0.3408 | 8800 | 0.0 | - | |
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| 0.3427 | 8850 | 0.0 | - | |
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| 0.3447 | 8900 | 0.0 | - | |
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| 0.3466 | 8950 | 0.0 | - | |
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| 0.3485 | 9000 | 0.0 | - | |
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| 0.3505 | 9050 | 0.0 | - | |
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| 0.3524 | 9100 | 0.0 | - | |
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| 0.3543 | 9150 | 0.0 | - | |
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| 0.3563 | 9200 | 0.0 | - | |
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| 0.3582 | 9250 | 0.0 | - | |
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| 0.3601 | 9300 | 0.0 | - | |
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| 0.3621 | 9350 | 0.0 | - | |
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| 0.3640 | 9400 | 0.0 | - | |
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| 0.3660 | 9450 | 0.0 | - | |
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| 0.3679 | 9500 | 0.0 | - | |
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| 0.3698 | 9550 | 0.0 | - | |
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| 0.3718 | 9600 | 0.0 | - | |
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| 0.3737 | 9650 | 0.0 | - | |
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| 0.3756 | 9700 | 0.0 | - | |
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| 0.3776 | 9750 | 0.0 | - | |
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| 0.3795 | 9800 | 0.0 | - | |
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| 0.3814 | 9850 | 0.0 | - | |
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| 0.3834 | 9900 | 0.0 | - | |
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| 0.3853 | 9950 | 0.0 | - | |
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| 0.3873 | 10000 | 0.0 | - | |
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| 0.3892 | 10050 | 0.0 | - | |
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| 0.3911 | 10100 | 0.0 | - | |
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| 0.3931 | 10150 | 0.0 | - | |
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| 0.3950 | 10200 | 0.0 | - | |
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| 0.3969 | 10250 | 0.0 | - | |
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| 0.3989 | 10300 | 0.0 | - | |
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| 0.4008 | 10350 | 0.0 | - | |
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| 0.4027 | 10400 | 0.0 | - | |
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| 0.4047 | 10450 | 0.0 | - | |
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| 0.4066 | 10500 | 0.0 | - | |
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| 0.4086 | 10550 | 0.0 | - | |
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| 0.4105 | 10600 | 0.0 | - | |
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| 0.4124 | 10650 | 0.0 | - | |
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| 0.4144 | 10700 | 0.0 | - | |
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| 0.4163 | 10750 | 0.0 | - | |
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| 0.4182 | 10800 | 0.0 | - | |
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| 0.4202 | 10850 | 0.0 | - | |
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| 0.4221 | 10900 | 0.0 | - | |
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| 0.4240 | 10950 | 0.0 | - | |
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| 0.4260 | 11000 | 0.0 | - | |
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| 0.4279 | 11050 | 0.0 | - | |
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| 0.4298 | 11100 | 0.0 | - | |
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| 0.4318 | 11150 | 0.0 | - | |
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| 0.4337 | 11200 | 0.0 | - | |
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| 0.4357 | 11250 | 0.0 | - | |
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| 0.4376 | 11300 | 0.0 | - | |
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| 0.4395 | 11350 | 0.0 | - | |
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| 0.4415 | 11400 | 0.0 | - | |
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| 0.4434 | 11450 | 0.0 | - | |
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| 0.4453 | 11500 | 0.0 | - | |
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| 0.4473 | 11550 | 0.0 | - | |
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| 0.4492 | 11600 | 0.0 | - | |
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| 0.4511 | 11650 | 0.0 | - | |
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| 0.4531 | 11700 | 0.0 | - | |
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| 0.4550 | 11750 | 0.0 | - | |
|
| 0.4570 | 11800 | 0.0 | - | |
|
| 0.4589 | 11850 | 0.0109 | - | |
|
| 0.4608 | 11900 | 0.0218 | - | |
|
| 0.4628 | 11950 | 0.0073 | - | |
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| 0.4647 | 12000 | 0.0056 | - | |
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| 0.4666 | 12050 | 0.0037 | - | |
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| 0.4686 | 12100 | 0.0011 | - | |
|
| 0.4705 | 12150 | 0.0002 | - | |
|
| 0.4724 | 12200 | 0.0014 | - | |
|
| 0.4744 | 12250 | 0.0031 | - | |
|
| 0.4763 | 12300 | 0.0013 | - | |
|
| 0.4783 | 12350 | 0.0012 | - | |
|
| 0.4802 | 12400 | 0.0022 | - | |
|
| 0.4821 | 12450 | 0.0003 | - | |
|
| 0.4841 | 12500 | 0.0 | - | |
|
| 0.4860 | 12550 | 0.0 | - | |
|
| 0.4879 | 12600 | 0.0 | - | |
|
| 0.4899 | 12650 | 0.0 | - | |
|
| 0.4918 | 12700 | 0.0 | - | |
|
| 0.4937 | 12750 | 0.0 | - | |
|
| 0.4957 | 12800 | 0.0 | - | |
|
| 0.4976 | 12850 | 0.0 | - | |
|
| 0.4996 | 12900 | 0.0 | - | |
|
| 0.5015 | 12950 | 0.0 | - | |
|
| 0.5034 | 13000 | 0.0 | - | |
|
| 0.5054 | 13050 | 0.0 | - | |
|
| 0.5073 | 13100 | 0.0 | - | |
|
| 0.5092 | 13150 | 0.0 | - | |
|
| 0.5112 | 13200 | 0.0 | - | |
|
| 0.5131 | 13250 | 0.0 | - | |
|
| 0.5150 | 13300 | 0.0 | - | |
|
| 0.5170 | 13350 | 0.0 | - | |
|
| 0.5189 | 13400 | 0.0 | - | |
|
| 0.5209 | 13450 | 0.0 | - | |
|
| 0.5228 | 13500 | 0.0 | - | |
|
| 0.5247 | 13550 | 0.0 | - | |
|
| 0.5267 | 13600 | 0.0 | - | |
|
| 0.5286 | 13650 | 0.0 | - | |
|
| 0.5305 | 13700 | 0.0 | - | |
|
| 0.5325 | 13750 | 0.0 | - | |
|
| 0.5344 | 13800 | 0.0 | - | |
|
| 0.5363 | 13850 | 0.0 | - | |
|
| 0.5383 | 13900 | 0.0 | - | |
|
| 0.5402 | 13950 | 0.0 | - | |
|
| 0.5422 | 14000 | 0.0 | - | |
|
| 0.5441 | 14050 | 0.0 | - | |
|
| 0.5460 | 14100 | 0.0 | - | |
|
| 0.5480 | 14150 | 0.0 | - | |
|
| 0.5499 | 14200 | 0.0 | - | |
|
| 0.5518 | 14250 | 0.0 | - | |
|
| 0.5538 | 14300 | 0.0 | - | |
|
| 0.5557 | 14350 | 0.0 | - | |
|
| 0.5576 | 14400 | 0.0 | - | |
|
| 0.5596 | 14450 | 0.0 | - | |
|
| 0.5615 | 14500 | 0.0 | - | |
|
| 0.5635 | 14550 | 0.0 | - | |
|
| 0.5654 | 14600 | 0.0 | - | |
|
| 0.5673 | 14650 | 0.0 | - | |
|
| 0.5693 | 14700 | 0.0 | - | |
|
| 0.5712 | 14750 | 0.0 | - | |
|
| 0.5731 | 14800 | 0.0 | - | |
|
| 0.5751 | 14850 | 0.0 | - | |
|
| 0.5770 | 14900 | 0.0 | - | |
|
| 0.5789 | 14950 | 0.0 | - | |
|
| 0.5809 | 15000 | 0.0 | - | |
|
| 0.5828 | 15050 | 0.0 | - | |
|
| 0.5848 | 15100 | 0.0 | - | |
|
| 0.5867 | 15150 | 0.0 | - | |
|
| 0.5886 | 15200 | 0.0 | - | |
|
| 0.5906 | 15250 | 0.0 | - | |
|
| 0.5925 | 15300 | 0.0 | - | |
|
| 0.5944 | 15350 | 0.0 | - | |
|
| 0.5964 | 15400 | 0.0 | - | |
|
| 0.5983 | 15450 | 0.0 | - | |
|
| 0.6002 | 15500 | 0.0 | - | |
|
| 0.6022 | 15550 | 0.0 | - | |
|
| 0.6041 | 15600 | 0.0 | - | |
|
| 0.6060 | 15650 | 0.0 | - | |
|
| 0.6080 | 15700 | 0.0 | - | |
|
| 0.6099 | 15750 | 0.0 | - | |
|
| 0.6119 | 15800 | 0.0 | - | |
|
| 0.6138 | 15850 | 0.0 | - | |
|
| 0.6157 | 15900 | 0.0 | - | |
|
| 0.6177 | 15950 | 0.0 | - | |
|
| 0.6196 | 16000 | 0.0 | - | |
|
| 0.6215 | 16050 | 0.0 | - | |
|
| 0.6235 | 16100 | 0.0 | - | |
|
| 0.6254 | 16150 | 0.0002 | - | |
|
| 0.6273 | 16200 | 0.0 | - | |
|
| 0.6293 | 16250 | 0.0002 | - | |
|
| 0.6312 | 16300 | 0.0034 | - | |
|
| 0.6332 | 16350 | 0.0062 | - | |
|
| 0.6351 | 16400 | 0.0034 | - | |
|
| 0.6370 | 16450 | 0.0001 | - | |
|
| 0.6390 | 16500 | 0.0004 | - | |
|
| 0.6409 | 16550 | 0.0 | - | |
|
| 0.6428 | 16600 | 0.0 | - | |
|
| 0.6448 | 16650 | 0.0 | - | |
|
| 0.6467 | 16700 | 0.0 | - | |
|
| 0.6486 | 16750 | 0.0 | - | |
|
| 0.6506 | 16800 | 0.0 | - | |
|
| 0.6525 | 16850 | 0.0 | - | |
|
| 0.6545 | 16900 | 0.0 | - | |
|
| 0.6564 | 16950 | 0.0 | - | |
|
| 0.6583 | 17000 | 0.0 | - | |
|
| 0.6603 | 17050 | 0.0 | - | |
|
| 0.6622 | 17100 | 0.0 | - | |
|
| 0.6641 | 17150 | 0.0 | - | |
|
| 0.6661 | 17200 | 0.0 | - | |
|
| 0.6680 | 17250 | 0.0 | - | |
|
| 0.6699 | 17300 | 0.0 | - | |
|
| 0.6719 | 17350 | 0.0 | - | |
|
| 0.6738 | 17400 | 0.0 | - | |
|
| 0.6758 | 17450 | 0.0 | - | |
|
| 0.6777 | 17500 | 0.0 | - | |
|
| 0.6796 | 17550 | 0.0 | - | |
|
| 0.6816 | 17600 | 0.0 | - | |
|
| 0.6835 | 17650 | 0.0 | - | |
|
| 0.6854 | 17700 | 0.0 | - | |
|
| 0.6874 | 17750 | 0.0 | - | |
|
| 0.6893 | 17800 | 0.0 | - | |
|
| 0.6912 | 17850 | 0.0 | - | |
|
| 0.6932 | 17900 | 0.0 | - | |
|
| 0.6951 | 17950 | 0.0 | - | |
|
| 0.6971 | 18000 | 0.0 | - | |
|
| 0.6990 | 18050 | 0.0 | - | |
|
| 0.7009 | 18100 | 0.0 | - | |
|
| 0.7029 | 18150 | 0.0 | - | |
|
| 0.7048 | 18200 | 0.0 | - | |
|
| 0.7067 | 18250 | 0.0 | - | |
|
| 0.7087 | 18300 | 0.0 | - | |
|
| 0.7106 | 18350 | 0.0 | - | |
|
| 0.7125 | 18400 | 0.0 | - | |
|
| 0.7145 | 18450 | 0.0 | - | |
|
| 0.7164 | 18500 | 0.0 | - | |
|
| 0.7184 | 18550 | 0.0 | - | |
|
| 0.7203 | 18600 | 0.0 | - | |
|
| 0.7222 | 18650 | 0.0 | - | |
|
| 0.7242 | 18700 | 0.0 | - | |
|
| 0.7261 | 18750 | 0.0 | - | |
|
| 0.7280 | 18800 | 0.0 | - | |
|
| 0.7300 | 18850 | 0.0 | - | |
|
| 0.7319 | 18900 | 0.0 | - | |
|
| 0.7338 | 18950 | 0.0 | - | |
|
| 0.7358 | 19000 | 0.0 | - | |
|
| 0.7377 | 19050 | 0.0 | - | |
|
| 0.7397 | 19100 | 0.0 | - | |
|
| 0.7416 | 19150 | 0.0 | - | |
|
| 0.7435 | 19200 | 0.0 | - | |
|
| 0.7455 | 19250 | 0.0 | - | |
|
| 0.7474 | 19300 | 0.0 | - | |
|
| 0.7493 | 19350 | 0.0 | - | |
|
| 0.7513 | 19400 | 0.0 | - | |
|
| 0.7532 | 19450 | 0.0 | - | |
|
| 0.7551 | 19500 | 0.0 | - | |
|
| 0.7571 | 19550 | 0.0 | - | |
|
| 0.7590 | 19600 | 0.0 | - | |
|
| 0.7609 | 19650 | 0.0 | - | |
|
| 0.7629 | 19700 | 0.0 | - | |
|
| 0.7648 | 19750 | 0.0 | - | |
|
| 0.7668 | 19800 | 0.0 | - | |
|
| 0.7687 | 19850 | 0.0 | - | |
|
| 0.7706 | 19900 | 0.0 | - | |
|
| 0.7726 | 19950 | 0.0 | - | |
|
| 0.7745 | 20000 | 0.0 | - | |
|
| 0.7764 | 20050 | 0.0 | - | |
|
| 0.7784 | 20100 | 0.0 | - | |
|
| 0.7803 | 20150 | 0.0 | - | |
|
| 0.7822 | 20200 | 0.0 | - | |
|
| 0.7842 | 20250 | 0.0 | - | |
|
| 0.7861 | 20300 | 0.0 | - | |
|
| 0.7881 | 20350 | 0.0 | - | |
|
| 0.7900 | 20400 | 0.0 | - | |
|
| 0.7919 | 20450 | 0.0 | - | |
|
| 0.7939 | 20500 | 0.0 | - | |
|
| 0.7958 | 20550 | 0.0 | - | |
|
| 0.7977 | 20600 | 0.0 | - | |
|
| 0.7997 | 20650 | 0.0 | - | |
|
| 0.8016 | 20700 | 0.0 | - | |
|
| 0.8035 | 20750 | 0.0 | - | |
|
| 0.8055 | 20800 | 0.0 | - | |
|
| 0.8074 | 20850 | 0.0 | - | |
|
| 0.8094 | 20900 | 0.0 | - | |
|
| 0.8113 | 20950 | 0.0 | - | |
|
| 0.8132 | 21000 | 0.0 | - | |
|
| 0.8152 | 21050 | 0.0 | - | |
|
| 0.8171 | 21100 | 0.0 | - | |
|
| 0.8190 | 21150 | 0.0 | - | |
|
| 0.8210 | 21200 | 0.0 | - | |
|
| 0.8229 | 21250 | 0.0 | - | |
|
| 0.8248 | 21300 | 0.0 | - | |
|
| 0.8268 | 21350 | 0.0 | - | |
|
| 0.8287 | 21400 | 0.0 | - | |
|
| 0.8307 | 21450 | 0.0 | - | |
|
| 0.8326 | 21500 | 0.0 | - | |
|
| 0.8345 | 21550 | 0.0 | - | |
|
| 0.8365 | 21600 | 0.0 | - | |
|
| 0.8384 | 21650 | 0.0 | - | |
|
| 0.8403 | 21700 | 0.0 | - | |
|
| 0.8423 | 21750 | 0.0 | - | |
|
| 0.8442 | 21800 | 0.0 | - | |
|
| 0.8461 | 21850 | 0.0 | - | |
|
| 0.8481 | 21900 | 0.0 | - | |
|
| 0.8500 | 21950 | 0.0 | - | |
|
| 0.8520 | 22000 | 0.0 | - | |
|
| 0.8539 | 22050 | 0.0 | - | |
|
| 0.8558 | 22100 | 0.0 | - | |
|
| 0.8578 | 22150 | 0.0 | - | |
|
| 0.8597 | 22200 | 0.0 | - | |
|
| 0.8616 | 22250 | 0.0 | - | |
|
| 0.8636 | 22300 | 0.0 | - | |
|
| 0.8655 | 22350 | 0.0 | - | |
|
| 0.8674 | 22400 | 0.0 | - | |
|
| 0.8694 | 22450 | 0.0 | - | |
|
| 0.8713 | 22500 | 0.0 | - | |
|
| 0.8733 | 22550 | 0.0 | - | |
|
| 0.8752 | 22600 | 0.0 | - | |
|
| 0.8771 | 22650 | 0.0 | - | |
|
| 0.8791 | 22700 | 0.0 | - | |
|
| 0.8810 | 22750 | 0.0 | - | |
|
| 0.8829 | 22800 | 0.0 | - | |
|
| 0.8849 | 22850 | 0.0 | - | |
|
| 0.8868 | 22900 | 0.0 | - | |
|
| 0.8887 | 22950 | 0.0 | - | |
|
| 0.8907 | 23000 | 0.0 | - | |
|
| 0.8926 | 23050 | 0.0 | - | |
|
| 0.8946 | 23100 | 0.0 | - | |
|
| 0.8965 | 23150 | 0.0 | - | |
|
| 0.8984 | 23200 | 0.0 | - | |
|
| 0.9004 | 23250 | 0.0 | - | |
|
| 0.9023 | 23300 | 0.0 | - | |
|
| 0.9042 | 23350 | 0.0 | - | |
|
| 0.9062 | 23400 | 0.0 | - | |
|
| 0.9081 | 23450 | 0.0 | - | |
|
| 0.9100 | 23500 | 0.0 | - | |
|
| 0.9120 | 23550 | 0.0 | - | |
|
| 0.9139 | 23600 | 0.0 | - | |
|
| 0.9159 | 23650 | 0.0 | - | |
|
| 0.9178 | 23700 | 0.0 | - | |
|
| 0.9197 | 23750 | 0.0 | - | |
|
| 0.9217 | 23800 | 0.0 | - | |
|
| 0.9236 | 23850 | 0.0 | - | |
|
| 0.9255 | 23900 | 0.0 | - | |
|
| 0.9275 | 23950 | 0.0 | - | |
|
| 0.9294 | 24000 | 0.0 | - | |
|
| 0.9313 | 24050 | 0.0 | - | |
|
| 0.9333 | 24100 | 0.0 | - | |
|
| 0.9352 | 24150 | 0.0 | - | |
|
| 0.9371 | 24200 | 0.0 | - | |
|
| 0.9391 | 24250 | 0.0 | - | |
|
| 0.9410 | 24300 | 0.0 | - | |
|
| 0.9430 | 24350 | 0.0 | - | |
|
| 0.9449 | 24400 | 0.0 | - | |
|
| 0.9468 | 24450 | 0.0 | - | |
|
| 0.9488 | 24500 | 0.0 | - | |
|
| 0.9507 | 24550 | 0.0 | - | |
|
| 0.9526 | 24600 | 0.0 | - | |
|
| 0.9546 | 24650 | 0.0 | - | |
|
| 0.9565 | 24700 | 0.0 | - | |
|
| 0.9584 | 24750 | 0.0 | - | |
|
| 0.9604 | 24800 | 0.0 | - | |
|
| 0.9623 | 24850 | 0.0 | - | |
|
| 0.9643 | 24900 | 0.0 | - | |
|
| 0.9662 | 24950 | 0.0 | - | |
|
| 0.9681 | 25000 | 0.0 | - | |
|
| 0.9701 | 25050 | 0.0 | - | |
|
| 0.9720 | 25100 | 0.0 | - | |
|
| 0.9739 | 25150 | 0.0 | - | |
|
| 0.9759 | 25200 | 0.0 | - | |
|
| 0.9778 | 25250 | 0.0 | - | |
|
| 0.9797 | 25300 | 0.0 | - | |
|
| 0.9817 | 25350 | 0.0 | - | |
|
| 0.9836 | 25400 | 0.0 | - | |
|
| 0.9856 | 25450 | 0.0 | - | |
|
| 0.9875 | 25500 | 0.0 | - | |
|
| 0.9894 | 25550 | 0.0 | - | |
|
| 0.9914 | 25600 | 0.0 | - | |
|
| 0.9933 | 25650 | 0.0 | - | |
|
| 0.9952 | 25700 | 0.0 | - | |
|
| 0.9972 | 25750 | 0.0 | - | |
|
| 0.9991 | 25800 | 0.0 | - | |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- SetFit: 1.1.0 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.42.2 |
|
- PyTorch: 2.5.1+cu121 |
|
- Datasets: 3.2.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## 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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