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MiniLM-klimacoder_v0.6

Text Classification
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MiniLM-klimacoder_v0.6 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '"Die selbsternannten Klimaretter von der Letzten Generation haben wieder
einmal den Verkehr in der Stadt lahmgelegt und tausende Pendler in den Morgenstau
getrieben."'
- text: Trotz der teils massiven Behinderungen des öffentlichen Straßenverkehrs durch
Aktionen, wie dem Aufkleben von Straßen oder dem Blockieren von Straßenkreuzungen,
zeigte sich, dass ein Teil der Bevölkerung, die die Demonstrationen beobachtete,
die Aktionen der Klima-Aktivisten unterstützt.
- text: '"Die selbsternannten Klimahelden von Fridays for Future und der Letzten Generation
haben wieder einmal für Chaos auf Deutschlands Straßen gesorgt und dabei nicht
nur den Verkehrslärm, sondern auch die Geduld der Bürger zum Kochen gebracht."'
- text: ' Die Einführung von Wärmepumpen durch das neue Heizungsgesetz ist ein wichtiger
Schritt zur Reduzierung des CO2-Ausstoßes und zur Förderung nachhaltiger Energiequellen.'
- text: ' "Ein nationales Tempolimit auf Autobahnen wäre ein weiterer Schritt in Richtung
eines überregulierten Staates, der den Bürgern ihre Freiheit stückweise entreißt."'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.956989247311828
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neutral | <ul><li>'Die Aktionen von Klima-Aktivisten, die in mehreren Städten zu Verkehrsbehinderungen geführt haben, haben in der Öffentlichkeit sowohl Unterstützung als auch Kritik ausgelöst.'</li><li>' Die Diskussion über ein nationales Tempolimit auf Autobahnen spaltet weiterhin die Gemüter, während Experten die potenziellen Vorteile und Nachteile abwägen.'</li><li>' Der Bundestag wird in den kommenden Wochen über das geplante Heizungsgesetz debattieren.'</li></ul> |
| supportive | <ul><li>' "Die Aktionen von Gruppen wie Fridays for Future und der Letzten Generation zeigen, dass die junge Generation bereit ist, für eine lebenswerte Zukunft zu kämpfen."'</li><li>' Die Einführung eines nationalen Tempolimits auf Autobahnen könnte die Verkehrssicherheit erheblich verbessern und die Zahl der Verkehrstoten reduzieren.'</li><li>'"Die jungen Aktivisten von Fridays for Future und die Letzte Generation haben mit ihren unkonventionellen Aktionen ein wichtiges Gespräch über die Dringlichkeit des Klimaschutzes angestoßen."'</li></ul> |
| opposed | <ul><li>'„Die Polizei musste am Freitag wiederholt mit harten Bandagen gegen die Klima-Rebellen vorgehen, die Straßen und Plätze in der Innenstadt blockierten, um für ihre Forderungen zu demonstrieren.“'</li><li>' "Ein Tempolimit auf deutschen Autobahnen würde den freiheitsliebenden Autofahrern das Herz brechen."'</li><li>'Die ständigen Straßenblockaden und Farbbeanspritzungen auf Kunstwerke haben viele Menschen in Deutschland mehr als nur gereizt - sie haben sie in ihrem täglichen Leben massiv behindert und zu einer wachsenden Ablehnung gegenüber den Klima-Aktivisten geführt.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9570 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("cbpuschmann/MiniLM-klimacoder_v0.6")
# Run inference
preds = model(" \"Ein nationales Tempolimit auf Autobahnen wäre ein weiterer Schritt in Richtung eines überregulierten Staates, der den Bürgern ihre Freiheit stückweise entreißt.\"")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 10 | 25.7025 | 53 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| neutral | 318 |
| opposed | 388 |
| supportive | 410 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2339 | - |
| 0.0019 | 50 | 0.2439 | - |
| 0.0039 | 100 | 0.2407 | - |
| 0.0058 | 150 | 0.2295 | - |
| 0.0078 | 200 | 0.2123 | - |
| 0.0097 | 250 | 0.1903 | - |
| 0.0116 | 300 | 0.153 | - |
| 0.0136 | 350 | 0.1322 | - |
| 0.0155 | 400 | 0.116 | - |
| 0.0174 | 450 | 0.0937 | - |
| 0.0194 | 500 | 0.0721 | - |
| 0.0213 | 550 | 0.0525 | - |
| 0.0233 | 600 | 0.0388 | - |
| 0.0252 | 650 | 0.0338 | - |
| 0.0271 | 700 | 0.026 | - |
| 0.0291 | 750 | 0.0224 | - |
| 0.0310 | 800 | 0.0122 | - |
| 0.0329 | 850 | 0.0088 | - |
| 0.0349 | 900 | 0.0079 | - |
| 0.0368 | 950 | 0.0055 | - |
| 0.0388 | 1000 | 0.004 | - |
| 0.0407 | 1050 | 0.0027 | - |
| 0.0426 | 1100 | 0.0025 | - |
| 0.0446 | 1150 | 0.0019 | - |
| 0.0465 | 1200 | 0.0014 | - |
| 0.0484 | 1250 | 0.0013 | - |
| 0.0504 | 1300 | 0.0006 | - |
| 0.0523 | 1350 | 0.0012 | - |
| 0.0543 | 1400 | 0.0006 | - |
| 0.0562 | 1450 | 0.0004 | - |
| 0.0581 | 1500 | 0.0003 | - |
| 0.0601 | 1550 | 0.0003 | - |
| 0.0620 | 1600 | 0.0003 | - |
| 0.0639 | 1650 | 0.0002 | - |
| 0.0659 | 1700 | 0.0007 | - |
| 0.0678 | 1750 | 0.0002 | - |
| 0.0698 | 1800 | 0.0002 | - |
| 0.0717 | 1850 | 0.0002 | - |
| 0.0736 | 1900 | 0.0003 | - |
| 0.0756 | 1950 | 0.0002 | - |
| 0.0775 | 2000 | 0.0001 | - |
| 0.0794 | 2050 | 0.0001 | - |
| 0.0814 | 2100 | 0.0001 | - |
| 0.0833 | 2150 | 0.0001 | - |
| 0.0853 | 2200 | 0.0008 | - |
| 0.0872 | 2250 | 0.0007 | - |
| 0.0891 | 2300 | 0.0007 | - |
| 0.0911 | 2350 | 0.0002 | - |
| 0.0930 | 2400 | 0.0001 | - |
| 0.0950 | 2450 | 0.0001 | - |
| 0.0969 | 2500 | 0.0014 | - |
| 0.0988 | 2550 | 0.0008 | - |
| 0.1008 | 2600 | 0.0009 | - |
| 0.1027 | 2650 | 0.0006 | - |
| 0.1046 | 2700 | 0.0008 | - |
| 0.1066 | 2750 | 0.0001 | - |
| 0.1085 | 2800 | 0.0 | - |
| 0.1105 | 2850 | 0.0 | - |
| 0.1124 | 2900 | 0.0 | - |
| 0.1143 | 2950 | 0.0 | - |
| 0.1163 | 3000 | 0.0 | - |
| 0.1182 | 3050 | 0.0 | - |
| 0.1201 | 3100 | 0.0 | - |
| 0.1221 | 3150 | 0.0 | - |
| 0.1240 | 3200 | 0.0 | - |
| 0.1260 | 3250 | 0.0 | - |
| 0.1279 | 3300 | 0.0 | - |
| 0.1298 | 3350 | 0.0 | - |
| 0.1318 | 3400 | 0.0 | - |
| 0.1337 | 3450 | 0.0 | - |
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| 0.1376 | 3550 | 0.0 | - |
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| 0.1492 | 3850 | 0.0 | - |
| 0.1511 | 3900 | 0.0 | - |
| 0.1531 | 3950 | 0.0 | - |
| 0.1550 | 4000 | 0.001 | - |
| 0.1570 | 4050 | 0.0012 | - |
| 0.1589 | 4100 | 0.0042 | - |
| 0.1608 | 4150 | 0.0023 | - |
| 0.1628 | 4200 | 0.001 | - |
| 0.1647 | 4250 | 0.001 | - |
| 0.1666 | 4300 | 0.0001 | - |
| 0.1686 | 4350 | 0.0 | - |
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| 0.1763 | 4550 | 0.0003 | - |
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| 0.1821 | 4700 | 0.0005 | - |
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| 0.2519 | 6500 | 0.0 | - |
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| 0.2713 | 7000 | 0.0 | - |
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| 0.2771 | 7150 | 0.0 | - |
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| 0.2810 | 7250 | 0.0 | - |
| 0.2829 | 7300 | 0.0 | - |
| 0.2849 | 7350 | 0.0 | - |
| 0.2868 | 7400 | 0.0 | - |
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| 0.2907 | 7500 | 0.0 | - |
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| 0.2945 | 7600 | 0.0 | - |
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| 0.3100 | 8000 | 0.0 | - |
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| 0.3159 | 8150 | 0.0 | - |
| 0.3178 | 8200 | 0.0 | - |
| 0.3197 | 8250 | 0.0 | - |
| 0.3217 | 8300 | 0.0 | - |
| 0.3236 | 8350 | 0.0 | - |
| 0.3255 | 8400 | 0.0 | - |
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| 0.3294 | 8500 | 0.0 | - |
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| 0.3333 | 8600 | 0.0 | - |
| 0.3352 | 8650 | 0.0 | - |
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| 0.3546 | 9150 | 0.0 | - |
| 0.3565 | 9200 | 0.0042 | - |
| 0.3585 | 9250 | 0.0083 | - |
| 0.3604 | 9300 | 0.0071 | - |
| 0.3624 | 9350 | 0.0011 | - |
| 0.3643 | 9400 | 0.0008 | - |
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| 0.4186 | 10800 | 0.0 | - |
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| 0.4263 | 11000 | 0.0 | - |
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| 0.4651 | 12000 | 0.0 | - |
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| 0.4689 | 12100 | 0.0 | - |
| 0.4709 | 12150 | 0.0 | - |
| 0.4728 | 12200 | 0.0 | - |
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| 0.4767 | 12300 | 0.0 | - |
| 0.4786 | 12350 | 0.0 | - |
| 0.4806 | 12400 | 0.0 | - |
| 0.4825 | 12450 | 0.0 | - |
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| 0.4883 | 12600 | 0.0 | - |
| 0.4903 | 12650 | 0.0 | - |
| 0.4922 | 12700 | 0.0 | - |
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| 0.4961 | 12800 | 0.0 | - |
| 0.4980 | 12850 | 0.0 | - |
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| 0.5038 | 13000 | 0.0 | - |
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| 0.5775 | 14900 | 0.0 | - |
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| 0.5910 | 15250 | 0.0 | - |
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| 0.6472 | 16700 | 0.0 | - |
| 0.6491 | 16750 | 0.0 | - |
| 0.6511 | 16800 | 0.0 | - |
| 0.6530 | 16850 | 0.0 | - |
| 0.6550 | 16900 | 0.0 | - |
| 0.6569 | 16950 | 0.0 | - |
| 0.6588 | 17000 | 0.0 | - |
| 0.6608 | 17050 | 0.0 | - |
| 0.6627 | 17100 | 0.0 | - |
| 0.6647 | 17150 | 0.0 | - |
| 0.6666 | 17200 | 0.0 | - |
| 0.6685 | 17250 | 0.0 | - |
| 0.6705 | 17300 | 0.0 | - |
| 0.6724 | 17350 | 0.0 | - |
| 0.6743 | 17400 | 0.0 | - |
| 0.6763 | 17450 | 0.0 | - |
| 0.6782 | 17500 | 0.0 | - |
| 0.6802 | 17550 | 0.0 | - |
| 0.6821 | 17600 | 0.0 | - |
| 0.6840 | 17650 | 0.0 | - |
| 0.6860 | 17700 | 0.0 | - |
| 0.6879 | 17750 | 0.0 | - |
| 0.6898 | 17800 | 0.0 | - |
| 0.6918 | 17850 | 0.0 | - |
| 0.6937 | 17900 | 0.0 | - |
| 0.6957 | 17950 | 0.0 | - |
| 0.6976 | 18000 | 0.0 | - |
| 0.6995 | 18050 | 0.0 | - |
| 0.7015 | 18100 | 0.0 | - |
| 0.7034 | 18150 | 0.0 | - |
| 0.7053 | 18200 | 0.0 | - |
| 0.7073 | 18250 | 0.0 | - |
| 0.7092 | 18300 | 0.0 | - |
| 0.7112 | 18350 | 0.0 | - |
| 0.7131 | 18400 | 0.0 | - |
| 0.7150 | 18450 | 0.0 | - |
| 0.7170 | 18500 | 0.0 | - |
| 0.7189 | 18550 | 0.0 | - |
| 0.7208 | 18600 | 0.0 | - |
| 0.7228 | 18650 | 0.0 | - |
| 0.7247 | 18700 | 0.0 | - |
| 0.7267 | 18750 | 0.0 | - |
| 0.7286 | 18800 | 0.0 | - |
| 0.7305 | 18850 | 0.0 | - |
| 0.7325 | 18900 | 0.0 | - |
| 0.7344 | 18950 | 0.0 | - |
| 0.7363 | 19000 | 0.0 | - |
| 0.7383 | 19050 | 0.0 | - |
| 0.7402 | 19100 | 0.0 | - |
| 0.7422 | 19150 | 0.0 | - |
| 0.7441 | 19200 | 0.0 | - |
| 0.7460 | 19250 | 0.0 | - |
| 0.7480 | 19300 | 0.0 | - |
| 0.7499 | 19350 | 0.0 | - |
| 0.7519 | 19400 | 0.0 | - |
| 0.7538 | 19450 | 0.0 | - |
| 0.7557 | 19500 | 0.0 | - |
| 0.7577 | 19550 | 0.0 | - |
| 0.7596 | 19600 | 0.0 | - |
| 0.7615 | 19650 | 0.0 | - |
| 0.7635 | 19700 | 0.0 | - |
| 0.7654 | 19750 | 0.0 | - |
| 0.7674 | 19800 | 0.0 | - |
| 0.7693 | 19850 | 0.0 | - |
| 0.7712 | 19900 | 0.0 | - |
| 0.7732 | 19950 | 0.0 | - |
| 0.7751 | 20000 | 0.0 | - |
| 0.7770 | 20050 | 0.0 | - |
| 0.7790 | 20100 | 0.0 | - |
| 0.7809 | 20150 | 0.0 | - |
| 0.7829 | 20200 | 0.0 | - |
| 0.7848 | 20250 | 0.0 | - |
| 0.7867 | 20300 | 0.0 | - |
| 0.7887 | 20350 | 0.0 | - |
| 0.7906 | 20400 | 0.0 | - |
| 0.7925 | 20450 | 0.0 | - |
| 0.7945 | 20500 | 0.0 | - |
| 0.7964 | 20550 | 0.0 | - |
| 0.7984 | 20600 | 0.0 | - |
| 0.8003 | 20650 | 0.0 | - |
| 0.8022 | 20700 | 0.0 | - |
| 0.8042 | 20750 | 0.0 | - |
| 0.8061 | 20800 | 0.0 | - |
| 0.8080 | 20850 | 0.0 | - |
| 0.8100 | 20900 | 0.0 | - |
| 0.8119 | 20950 | 0.0 | - |
| 0.8139 | 21000 | 0.0 | - |
| 0.8158 | 21050 | 0.0 | - |
| 0.8177 | 21100 | 0.0 | - |
| 0.8197 | 21150 | 0.0 | - |
| 0.8216 | 21200 | 0.0 | - |
| 0.8235 | 21250 | 0.0 | - |
| 0.8255 | 21300 | 0.0 | - |
| 0.8274 | 21350 | 0.0 | - |
| 0.8294 | 21400 | 0.0 | - |
| 0.8313 | 21450 | 0.0 | - |
| 0.8332 | 21500 | 0.0 | - |
| 0.8352 | 21550 | 0.0 | - |
| 0.8371 | 21600 | 0.0 | - |
| 0.8390 | 21650 | 0.0 | - |
| 0.8410 | 21700 | 0.0 | - |
| 0.8429 | 21750 | 0.0 | - |
| 0.8449 | 21800 | 0.0 | - |
| 0.8468 | 21850 | 0.0 | - |
| 0.8487 | 21900 | 0.0 | - |
| 0.8507 | 21950 | 0.0 | - |
| 0.8526 | 22000 | 0.0 | - |
| 0.8546 | 22050 | 0.0 | - |
| 0.8565 | 22100 | 0.0 | - |
| 0.8584 | 22150 | 0.0 | - |
| 0.8604 | 22200 | 0.0 | - |
| 0.8623 | 22250 | 0.0 | - |
| 0.8642 | 22300 | 0.0 | - |
| 0.8662 | 22350 | 0.0 | - |
| 0.8681 | 22400 | 0.0 | - |
| 0.8701 | 22450 | 0.0 | - |
| 0.8720 | 22500 | 0.0 | - |
| 0.8739 | 22550 | 0.0 | - |
| 0.8759 | 22600 | 0.0 | - |
| 0.8778 | 22650 | 0.0 | - |
| 0.8797 | 22700 | 0.0 | - |
| 0.8817 | 22750 | 0.0 | - |
| 0.8836 | 22800 | 0.0 | - |
| 0.8856 | 22850 | 0.0 | - |
| 0.8875 | 22900 | 0.0 | - |
| 0.8894 | 22950 | 0.0 | - |
| 0.8914 | 23000 | 0.0 | - |
| 0.8933 | 23050 | 0.0 | - |
| 0.8952 | 23100 | 0.0 | - |
| 0.8972 | 23150 | 0.0 | - |
| 0.8991 | 23200 | 0.0 | - |
| 0.9011 | 23250 | 0.0 | - |
| 0.9030 | 23300 | 0.0 | - |
| 0.9049 | 23350 | 0.0 | - |
| 0.9069 | 23400 | 0.0 | - |
| 0.9088 | 23450 | 0.0 | - |
| 0.9107 | 23500 | 0.0 | - |
| 0.9127 | 23550 | 0.0 | - |
| 0.9146 | 23600 | 0.0 | - |
| 0.9166 | 23650 | 0.0 | - |
| 0.9185 | 23700 | 0.0 | - |
| 0.9204 | 23750 | 0.0 | - |
| 0.9224 | 23800 | 0.0 | - |
| 0.9243 | 23850 | 0.0 | - |
| 0.9262 | 23900 | 0.0 | - |
| 0.9282 | 23950 | 0.0 | - |
| 0.9301 | 24000 | 0.0 | - |
| 0.9321 | 24050 | 0.0 | - |
| 0.9340 | 24100 | 0.0 | - |
| 0.9359 | 24150 | 0.0 | - |
| 0.9379 | 24200 | 0.0 | - |
| 0.9398 | 24250 | 0.0 | - |
| 0.9418 | 24300 | 0.0 | - |
| 0.9437 | 24350 | 0.0 | - |
| 0.9456 | 24400 | 0.0 | - |
| 0.9476 | 24450 | 0.0 | - |
| 0.9495 | 24500 | 0.0 | - |
| 0.9514 | 24550 | 0.0 | - |
| 0.9534 | 24600 | 0.0 | - |
| 0.9553 | 24650 | 0.0 | - |
| 0.9573 | 24700 | 0.0 | - |
| 0.9592 | 24750 | 0.0 | - |
| 0.9611 | 24800 | 0.0 | - |
| 0.9631 | 24850 | 0.0 | - |
| 0.9650 | 24900 | 0.0 | - |
| 0.9669 | 24950 | 0.0 | - |
| 0.9689 | 25000 | 0.0 | - |
| 0.9708 | 25050 | 0.0 | - |
| 0.9728 | 25100 | 0.0 | - |
| 0.9747 | 25150 | 0.0 | - |
| 0.9766 | 25200 | 0.0 | - |
| 0.9786 | 25250 | 0.0 | - |
| 0.9805 | 25300 | 0.0 | - |
| 0.9824 | 25350 | 0.0 | - |
| 0.9844 | 25400 | 0.0 | - |
| 0.9863 | 25450 | 0.0 | - |
| 0.9883 | 25500 | 0.0 | - |
| 0.9902 | 25550 | 0.0 | - |
| 0.9921 | 25600 | 0.0 | - |
| 0.9941 | 25650 | 0.0 | - |
| 0.9960 | 25700 | 0.0 | - |
| 0.9979 | 25750 | 0.0 | - |
| 0.9999 | 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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