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cbpuschmann
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MiniLM-klimacoder_v0.6

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Add SetFit model

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  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +723 -0
  4. config.json +26 -0
  5. config_sentence_transformers.json +10 -0
  6. config_setfit.json +8 -0
  7. model.safetensors +3 -0
  8. model_head.pkl +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. special_tokens_map.json +51 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +64 -0
  14. unigram.json +3 -0
.gitattributes CHANGED
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ unigram.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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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 selbsternannten Klimaretter von der Letzten Generation haben wieder
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+ einmal den Verkehr in der Stadt lahmgelegt und tausende Pendler in den Morgenstau
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+ getrieben."'
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+ - text: Trotz der teils massiven Behinderungen des öffentlichen Straßenverkehrs durch
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+ Aktionen, wie dem Aufkleben von Straßen oder dem Blockieren von Straßenkreuzungen,
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+ zeigte sich, dass ein Teil der Bevölkerung, die die Demonstrationen beobachtete,
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+ die Aktionen der Klima-Aktivisten unterstützt.
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+ - text: '"Die selbsternannten Klimahelden von Fridays for Future und der Letzten Generation
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+ haben wieder einmal für Chaos auf Deutschlands Straßen gesorgt und dabei nicht
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+ nur den Verkehrslärm, sondern auch die Geduld der Bürger zum Kochen gebracht."'
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+ - text: ' Die Einführung von Wärmepumpen durch das neue Heizungsgesetz ist ein wichtiger
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+ Schritt zur Reduzierung des CO2-Ausstoßes und zur Förderung nachhaltiger Energiequellen.'
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+ - text: ' "Ein nationales Tempolimit auf Autobahnen wäre ein weiterer Schritt in Richtung
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+ eines überregulierten Staates, der den Bürgern ihre Freiheit stückweise entreißt."'
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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-MiniLM-L12-v2
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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: 0.956989247311828
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+
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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-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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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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+
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+ ### Model Sources
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+
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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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+
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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-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> |
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+ | 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> |
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+ | 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> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.9570 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("cbpuschmann/MiniLM-klimacoder_v0.6")
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+ # Run inference
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+ preds = model(" \"Ein nationales Tempolimit auf Autobahnen wäre ein weiterer Schritt in Richtung eines überregulierten Staates, der den Bürgern ihre Freiheit stückweise entreißt.\"")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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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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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ <!--
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+ ### Recommendations
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+
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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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+
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+ ## Training Details
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+
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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 | 25.7025 | 53 |
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+
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+ | Label | Training Sample Count |
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+ |:-----------|:----------------------|
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+ | neutral | 318 |
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+ | opposed | 388 |
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+ | supportive | 410 |
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+
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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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+
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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 | 0.2339 | - |
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+ | 0.0019 | 50 | 0.2439 | - |
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+ | 0.0039 | 100 | 0.2407 | - |
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+ | 0.0058 | 150 | 0.2295 | - |
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+ | 0.0078 | 200 | 0.2123 | - |
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+ | 0.0097 | 250 | 0.1903 | - |
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+ | 0.0116 | 300 | 0.153 | - |
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+ | 0.0136 | 350 | 0.1322 | - |
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+ | 0.0155 | 400 | 0.116 | - |
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+ | 0.0174 | 450 | 0.0937 | - |
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+ | 0.0194 | 500 | 0.0721 | - |
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+ | 0.0213 | 550 | 0.0525 | - |
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+ | 0.0233 | 600 | 0.0388 | - |
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+ | 0.0252 | 650 | 0.0338 | - |
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+ | 0.0271 | 700 | 0.026 | - |
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+ | 0.0291 | 750 | 0.0224 | - |
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+ | 0.0310 | 800 | 0.0122 | - |
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+ | 0.0329 | 850 | 0.0088 | - |
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+ | 0.0349 | 900 | 0.0079 | - |
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+ | 0.0368 | 950 | 0.0055 | - |
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+ | 0.0388 | 1000 | 0.004 | - |
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+ | 0.0407 | 1050 | 0.0027 | - |
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+ | 0.0426 | 1100 | 0.0025 | - |
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+ | 0.0446 | 1150 | 0.0019 | - |
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+ | 0.0465 | 1200 | 0.0014 | - |
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+ | 0.0484 | 1250 | 0.0013 | - |
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+ | 0.0504 | 1300 | 0.0006 | - |
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+ | 0.0523 | 1350 | 0.0012 | - |
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+ | 0.0543 | 1400 | 0.0006 | - |
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+ | 0.0562 | 1450 | 0.0004 | - |
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+ | 0.0581 | 1500 | 0.0003 | - |
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632
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649
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650
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651
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652
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653
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654
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655
+ | 0.9514 | 24550 | 0.0 | - |
656
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657
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658
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659
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660
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661
+ | 0.9631 | 24850 | 0.0 | - |
662
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663
+ | 0.9669 | 24950 | 0.0 | - |
664
+ | 0.9689 | 25000 | 0.0 | - |
665
+ | 0.9708 | 25050 | 0.0 | - |
666
+ | 0.9728 | 25100 | 0.0 | - |
667
+ | 0.9747 | 25150 | 0.0 | - |
668
+ | 0.9766 | 25200 | 0.0 | - |
669
+ | 0.9786 | 25250 | 0.0 | - |
670
+ | 0.9805 | 25300 | 0.0 | - |
671
+ | 0.9824 | 25350 | 0.0 | - |
672
+ | 0.9844 | 25400 | 0.0 | - |
673
+ | 0.9863 | 25450 | 0.0 | - |
674
+ | 0.9883 | 25500 | 0.0 | - |
675
+ | 0.9902 | 25550 | 0.0 | - |
676
+ | 0.9921 | 25600 | 0.0 | - |
677
+ | 0.9941 | 25650 | 0.0 | - |
678
+ | 0.9960 | 25700 | 0.0 | - |
679
+ | 0.9979 | 25750 | 0.0 | - |
680
+ | 0.9999 | 25800 | 0.0 | - |
681
+
682
+ ### Framework Versions
683
+ - Python: 3.10.12
684
+ - SetFit: 1.1.0
685
+ - Sentence Transformers: 3.3.1
686
+ - Transformers: 4.42.2
687
+ - PyTorch: 2.5.1+cu121
688
+ - Datasets: 3.2.0
689
+ - Tokenizers: 0.19.1
690
+
691
+ ## Citation
692
+
693
+ ### BibTeX
694
+ ```bibtex
695
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
696
+ doi = {10.48550/ARXIV.2209.11055},
697
+ url = {https://arxiv.org/abs/2209.11055},
698
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
699
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
700
+ title = {Efficient Few-Shot Learning Without Prompts},
701
+ publisher = {arXiv},
702
+ year = {2022},
703
+ copyright = {Creative Commons Attribution 4.0 International}
704
+ }
705
+ ```
706
+
707
+ <!--
708
+ ## Glossary
709
+
710
+ *Clearly define terms in order to be accessible across audiences.*
711
+ -->
712
+
713
+ <!--
714
+ ## Model Card Authors
715
+
716
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
717
+ -->
718
+
719
+ <!--
720
+ ## Model Card Contact
721
+
722
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
723
+ -->
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
3
+ "architectures": [
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+ "BertModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 384,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 1536,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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