metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Mit dem geplanten Heizungsgesetz setzt die Regierung einen wichtigen
Schritt in Richtung eines klimafreundlichen Wärmemarktes. Die
flächendeckende Einführung von Wärmepumpen soll den Verbrauch von fossilen
Energieträgern reduzieren und den Ausstoß an Treibhausgasen senken. Damit
trägt das Gesetz zu einer umweltfreundlicheren Heizungsinfrastruktur bei.
- text: >-
"Das Heizungsgesetz: Eine teure, ineffiziente und überbordete Lösung für
unsere Energieprobleme? Die geplanten Wärmepumpen in jedem Haus wirken
sich negativ auf die Umwelt aus und werden wahrscheinlich Millionen von
Steuergeldern verschlingen. Wir brauchen eine realistische Energiewende,
nicht ein teures Experiment."
- text: >-
Die Bundesregierung hat ein Gesetz zur Förderung der flächendeckenden
Einführung von Wärmepumpen verabschiedet, das darauf abzielt, den
CO2-Ausstoß im Gebäudesektor zu reduzieren. Kritiker bemängeln mögliche
hohe Kosten und technische Herausforderungen, während Befürworter die
Maßnahme als wichtigen Schritt zur Erreichung der Klimaziele sehen.
- text: >-
In verschiedenen Städten Deutschlands haben sich wiederum Menschen
versammelt, um für den Klimaschutz zu demonstrieren. Die Teilnehmer von
Fridays for Future und der Letzten Generation fordern die Regierung auf,
ambitioniertere Maßnahmen gegen den Klimawandel zu ergreifen. Ihre
Forderungen richten sich an die politischen Entscheidungsträger, um eine
bessere Zukunft für kommende Generationen zu schaffen.
- text: >-
Der Bundestag debattiert erneut über die Einführung eines generellen
Tempolimits auf deutschen Autobahnen. Befürworter betonen die positiven
Auswirkungen auf Verkehrssicherheit und Umwelt, während Kritiker die
Einschränkung individueller Freiheit und mögliche wirtschaftliche Folgen
anführen. Die Entscheidung bleibt umstritten und spiegelt die vielfältigen
Interessen in der Gesellschaft wider.
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: 0.9771428571428571
name: Accuracy
SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
supportive |
|
neutral |
|
opposed |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9771 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("cbpuschmann/paraphrase-multilingual-minilm-klimacoder_v0.10")
# Run inference
preds = model("\"Das Heizungsgesetz: Eine teure, ineffiziente und überbordete Lösung für unsere Energieprobleme? Die geplanten Wärmepumpen in jedem Haus wirken sich negativ auf die Umwelt aus und werden wahrscheinlich Millionen von Steuergeldern verschlingen. Wir brauchen eine realistische Energiewende, nicht ein teures Experiment.\"")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 24 | 44.1537 | 73 |
Label | Training Sample Count |
---|---|
neutral | 500 |
opposed | 549 |
supportive | 526 |
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 | 0.2419 | - |
0.0010 | 50 | 0.2541 | - |
0.0019 | 100 | 0.2489 | - |
0.0029 | 150 | 0.2404 | - |
0.0039 | 200 | 0.2281 | - |
0.0048 | 250 | 0.2168 | - |
0.0058 | 300 | 0.193 | - |
0.0068 | 350 | 0.1604 | - |
0.0077 | 400 | 0.1304 | - |
0.0087 | 450 | 0.1218 | - |
0.0097 | 500 | 0.1046 | - |
0.0107 | 550 | 0.0978 | - |
0.0116 | 600 | 0.0733 | - |
0.0126 | 650 | 0.061 | - |
0.0136 | 700 | 0.0496 | - |
0.0145 | 750 | 0.0397 | - |
0.0155 | 800 | 0.0331 | - |
0.0165 | 850 | 0.0329 | - |
0.0174 | 900 | 0.0254 | - |
0.0184 | 950 | 0.0194 | - |
0.0194 | 1000 | 0.0154 | - |
0.0203 | 1050 | 0.0111 | - |
0.0213 | 1100 | 0.0112 | - |
0.0223 | 1150 | 0.0107 | - |
0.0232 | 1200 | 0.0065 | - |
0.0242 | 1250 | 0.0046 | - |
0.0252 | 1300 | 0.0059 | - |
0.0261 | 1350 | 0.0033 | - |
0.0271 | 1400 | 0.003 | - |
0.0281 | 1450 | 0.0024 | - |
0.0290 | 1500 | 0.0018 | - |
0.0300 | 1550 | 0.001 | - |
0.0310 | 1600 | 0.0011 | - |
0.0320 | 1650 | 0.0012 | - |
0.0329 | 1700 | 0.0007 | - |
0.0339 | 1750 | 0.0007 | - |
0.0349 | 1800 | 0.0005 | - |
0.0358 | 1850 | 0.0004 | - |
0.0368 | 1900 | 0.0003 | - |
0.0378 | 1950 | 0.0006 | - |
0.0387 | 2000 | 0.0004 | - |
0.0397 | 2050 | 0.0003 | - |
0.0407 | 2100 | 0.0002 | - |
0.0416 | 2150 | 0.0003 | - |
0.0426 | 2200 | 0.0005 | - |
0.0436 | 2250 | 0.0005 | - |
0.0445 | 2300 | 0.0001 | - |
0.0455 | 2350 | 0.0003 | - |
0.0465 | 2400 | 0.0003 | - |
0.0474 | 2450 | 0.0002 | - |
0.0484 | 2500 | 0.0003 | - |
0.0494 | 2550 | 0.0001 | - |
0.0503 | 2600 | 0.0002 | - |
0.0513 | 2650 | 0.0003 | - |
0.0523 | 2700 | 0.0004 | - |
0.0533 | 2750 | 0.0007 | - |
0.0542 | 2800 | 0.0001 | - |
0.0552 | 2850 | 0.0002 | - |
0.0562 | 2900 | 0.0001 | - |
0.0571 | 2950 | 0.0001 | - |
0.0581 | 3000 | 0.0001 | - |
0.0591 | 3050 | 0.0001 | - |
0.0600 | 3100 | 0.0001 | - |
0.0610 | 3150 | 0.0 | - |
0.0620 | 3200 | 0.0 | - |
0.0629 | 3250 | 0.0 | - |
0.0639 | 3300 | 0.0001 | - |
0.0649 | 3350 | 0.0006 | - |
0.0658 | 3400 | 0.0 | - |
0.0668 | 3450 | 0.0 | - |
0.0678 | 3500 | 0.0001 | - |
0.0687 | 3550 | 0.0 | - |
0.0697 | 3600 | 0.0 | - |
0.0707 | 3650 | 0.0001 | - |
0.0716 | 3700 | 0.0001 | - |
0.0726 | 3750 | 0.0 | - |
0.0736 | 3800 | 0.0 | - |
0.0746 | 3850 | 0.0 | - |
0.0755 | 3900 | 0.0 | - |
0.0765 | 3950 | 0.0 | - |
0.0775 | 4000 | 0.0 | - |
0.0784 | 4050 | 0.0 | - |
0.0794 | 4100 | 0.0 | - |
0.0804 | 4150 | 0.0 | - |
0.0813 | 4200 | 0.0 | - |
0.0823 | 4250 | 0.0 | - |
0.0833 | 4300 | 0.0 | - |
0.0842 | 4350 | 0.0027 | - |
0.0852 | 4400 | 0.0021 | - |
0.0862 | 4450 | 0.0013 | - |
0.0871 | 4500 | 0.0022 | - |
0.0881 | 4550 | 0.004 | - |
0.0891 | 4600 | 0.0017 | - |
0.0900 | 4650 | 0.0054 | - |
0.0910 | 4700 | 0.0019 | - |
0.0920 | 4750 | 0.0009 | - |
0.0929 | 4800 | 0.0001 | - |
0.0939 | 4850 | 0.0 | - |
0.0949 | 4900 | 0.0 | - |
0.0959 | 4950 | 0.0 | - |
0.0968 | 5000 | 0.0 | - |
0.0978 | 5050 | 0.0 | - |
0.0988 | 5100 | 0.0 | - |
0.0997 | 5150 | 0.0 | - |
0.1007 | 5200 | 0.0 | - |
0.1017 | 5250 | 0.0 | - |
0.1026 | 5300 | 0.0 | - |
0.1036 | 5350 | 0.0 | - |
0.1046 | 5400 | 0.0 | - |
0.1055 | 5450 | 0.0 | - |
0.1065 | 5500 | 0.0 | - |
0.1075 | 5550 | 0.0 | - |
0.1084 | 5600 | 0.0 | - |
0.1094 | 5650 | 0.0 | - |
0.1104 | 5700 | 0.0 | - |
0.1113 | 5750 | 0.0 | - |
0.1123 | 5800 | 0.0 | - |
0.1133 | 5850 | 0.0 | - |
0.1142 | 5900 | 0.0 | - |
0.1152 | 5950 | 0.0 | - |
0.1162 | 6000 | 0.0 | - |
0.1172 | 6050 | 0.0 | - |
0.1181 | 6100 | 0.0 | - |
0.1191 | 6150 | 0.0 | - |
0.1201 | 6200 | 0.0 | - |
0.1210 | 6250 | 0.0 | - |
0.1220 | 6300 | 0.0 | - |
0.1230 | 6350 | 0.0 | - |
0.1239 | 6400 | 0.0 | - |
0.1249 | 6450 | 0.0 | - |
0.1259 | 6500 | 0.0 | - |
0.1268 | 6550 | 0.0 | - |
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0.1297 | 6700 | 0.0 | - |
0.1307 | 6750 | 0.0 | - |
0.1317 | 6800 | 0.0 | - |
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0.1355 | 7000 | 0.0 | - |
0.1365 | 7050 | 0.0 | - |
0.1375 | 7100 | 0.0 | - |
0.1385 | 7150 | 0.0 | - |
0.1394 | 7200 | 0.0 | - |
0.1404 | 7250 | 0.0 | - |
0.1414 | 7300 | 0.0 | - |
0.1423 | 7350 | 0.0 | - |
0.1433 | 7400 | 0.0 | - |
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0.1472 | 7600 | 0.0 | - |
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0.1530 | 7900 | 0.0 | - |
0.1539 | 7950 | 0.0 | - |
0.1549 | 8000 | 0.0 | - |
0.1559 | 8050 | 0.0 | - |
0.1568 | 8100 | 0.0 | - |
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0.1588 | 8200 | 0.0 | - |
0.1598 | 8250 | 0.0 | - |
0.1607 | 8300 | 0.0 | - |
0.1617 | 8350 | 0.0 | - |
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0.1665 | 8600 | 0.0 | - |
0.1675 | 8650 | 0.0 | - |
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0.1694 | 8750 | 0.0 | - |
0.1704 | 8800 | 0.0 | - |
0.1714 | 8850 | 0.0 | - |
0.1723 | 8900 | 0.0 | - |
0.1733 | 8950 | 0.0 | - |
0.1743 | 9000 | 0.0 | - |
0.1752 | 9050 | 0.0 | - |
0.1762 | 9100 | 0.0 | - |
0.1772 | 9150 | 0.0 | - |
0.1781 | 9200 | 0.0 | - |
0.1791 | 9250 | 0.0 | - |
0.1801 | 9300 | 0.0 | - |
0.1811 | 9350 | 0.0 | - |
0.1820 | 9400 | 0.0 | - |
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0.1907 | 9850 | 0.0 | - |
0.1917 | 9900 | 0.0 | - |
0.1927 | 9950 | 0.0 | - |
0.1936 | 10000 | 0.0 | - |
0.1946 | 10050 | 0.0 | - |
0.1956 | 10100 | 0.0 | - |
0.1965 | 10150 | 0.0 | - |
0.1975 | 10200 | 0.0 | - |
0.1985 | 10250 | 0.0 | - |
0.1994 | 10300 | 0.0 | - |
0.2004 | 10350 | 0.0 | - |
0.2014 | 10400 | 0.0 | - |
0.2024 | 10450 | 0.0 | - |
0.2033 | 10500 | 0.0 | - |
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0.2053 | 10600 | 0.0 | - |
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0.2479 | 12800 | 0.0 | - |
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0.2508 | 12950 | 0.0001 | - |
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0.3873 | 20000 | 0.0165 | - |
0.3882 | 20050 | 0.0065 | - |
0.3892 | 20100 | 0.0014 | - |
0.3902 | 20150 | 0.002 | - |
0.3911 | 20200 | 0.0011 | - |
0.3921 | 20250 | 0.0 | - |
0.3931 | 20300 | 0.0014 | - |
0.3941 | 20350 | 0.0 | - |
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0.8859 | 45750 | 0.0 | - |
0.8869 | 45800 | 0.0 | - |
0.8878 | 45850 | 0.0 | - |
0.8888 | 45900 | 0.0 | - |
0.8898 | 45950 | 0.0 | - |
0.8907 | 46000 | 0.0 | - |
0.8917 | 46050 | 0.0 | - |
0.8927 | 46100 | 0.0 | - |
0.8936 | 46150 | 0.0 | - |
0.8946 | 46200 | 0.0 | - |
0.8956 | 46250 | 0.0 | - |
0.8965 | 46300 | 0.0 | - |
0.8975 | 46350 | 0.0 | - |
0.8985 | 46400 | 0.0 | - |
0.8994 | 46450 | 0.0 | - |
0.9004 | 46500 | 0.0 | - |
0.9014 | 46550 | 0.0 | - |
0.9023 | 46600 | 0.0 | - |
0.9033 | 46650 | 0.0 | - |
0.9043 | 46700 | 0.0 | - |
0.9053 | 46750 | 0.0 | - |
0.9062 | 46800 | 0.0 | - |
0.9072 | 46850 | 0.0 | - |
0.9082 | 46900 | 0.0 | - |
0.9091 | 46950 | 0.0 | - |
0.9101 | 47000 | 0.0 | - |
0.9111 | 47050 | 0.0 | - |
0.9120 | 47100 | 0.0 | - |
0.9130 | 47150 | 0.0 | - |
0.9140 | 47200 | 0.0 | - |
0.9149 | 47250 | 0.0 | - |
0.9159 | 47300 | 0.0 | - |
0.9169 | 47350 | 0.0 | - |
0.9178 | 47400 | 0.0 | - |
0.9188 | 47450 | 0.0 | - |
0.9198 | 47500 | 0.0 | - |
0.9207 | 47550 | 0.0 | - |
0.9217 | 47600 | 0.0 | - |
0.9227 | 47650 | 0.0 | - |
0.9236 | 47700 | 0.0 | - |
0.9246 | 47750 | 0.0 | - |
0.9256 | 47800 | 0.0 | - |
0.9266 | 47850 | 0.0 | - |
0.9275 | 47900 | 0.0 | - |
0.9285 | 47950 | 0.0 | - |
0.9295 | 48000 | 0.0 | - |
0.9304 | 48050 | 0.0 | - |
0.9314 | 48100 | 0.0 | - |
0.9324 | 48150 | 0.0 | - |
0.9333 | 48200 | 0.0 | - |
0.9343 | 48250 | 0.0 | - |
0.9353 | 48300 | 0.0 | - |
0.9362 | 48350 | 0.0 | - |
0.9372 | 48400 | 0.0 | - |
0.9382 | 48450 | 0.0 | - |
0.9391 | 48500 | 0.0 | - |
0.9401 | 48550 | 0.0 | - |
0.9411 | 48600 | 0.0 | - |
0.9420 | 48650 | 0.0 | - |
0.9430 | 48700 | 0.0 | - |
0.9440 | 48750 | 0.0 | - |
0.9449 | 48800 | 0.0 | - |
0.9459 | 48850 | 0.0 | - |
0.9469 | 48900 | 0.0 | - |
0.9479 | 48950 | 0.0 | - |
0.9488 | 49000 | 0.0 | - |
0.9498 | 49050 | 0.0 | - |
0.9508 | 49100 | 0.0 | - |
0.9517 | 49150 | 0.0 | - |
0.9527 | 49200 | 0.0 | - |
0.9537 | 49250 | 0.0 | - |
0.9546 | 49300 | 0.0 | - |
0.9556 | 49350 | 0.0 | - |
0.9566 | 49400 | 0.0 | - |
0.9575 | 49450 | 0.0 | - |
0.9585 | 49500 | 0.0 | - |
0.9595 | 49550 | 0.0 | - |
0.9604 | 49600 | 0.0 | - |
0.9614 | 49650 | 0.0 | - |
0.9624 | 49700 | 0.0 | - |
0.9633 | 49750 | 0.0 | - |
0.9643 | 49800 | 0.0 | - |
0.9653 | 49850 | 0.0 | - |
0.9662 | 49900 | 0.0 | - |
0.9672 | 49950 | 0.0 | - |
0.9682 | 50000 | 0.0 | - |
0.9692 | 50050 | 0.0 | - |
0.9701 | 50100 | 0.0 | - |
0.9711 | 50150 | 0.0 | - |
0.9721 | 50200 | 0.0 | - |
0.9730 | 50250 | 0.0 | - |
0.9740 | 50300 | 0.0 | - |
0.9750 | 50350 | 0.0 | - |
0.9759 | 50400 | 0.0 | - |
0.9769 | 50450 | 0.0 | - |
0.9779 | 50500 | 0.0 | - |
0.9788 | 50550 | 0.0 | - |
0.9798 | 50600 | 0.0 | - |
0.9808 | 50650 | 0.0 | - |
0.9817 | 50700 | 0.0 | - |
0.9827 | 50750 | 0.0 | - |
0.9837 | 50800 | 0.0 | - |
0.9846 | 50850 | 0.0 | - |
0.9856 | 50900 | 0.0 | - |
0.9866 | 50950 | 0.0 | - |
0.9875 | 51000 | 0.0 | - |
0.9885 | 51050 | 0.0 | - |
0.9895 | 51100 | 0.0 | - |
0.9905 | 51150 | 0.0 | - |
0.9914 | 51200 | 0.0 | - |
0.9924 | 51250 | 0.0 | - |
0.9934 | 51300 | 0.0 | - |
0.9943 | 51350 | 0.0 | - |
0.9953 | 51400 | 0.0 | - |
0.9963 | 51450 | 0.0 | - |
0.9972 | 51500 | 0.0 | - |
0.9982 | 51550 | 0.0 | - |
0.9992 | 51600 | 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
@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}
}