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20250909_model_g20_multilabel_MiniLM-L12-all-labels-artificial-governance-multi-output

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Model card Files
xet
Community
lgsilvaesilva commited on
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Files changed (14) hide show
  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +433 -0
  4. config.json +25 -0
  5. config_sentence_transformers.json +14 -0
  6. config_setfit.json +4 -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 +65 -0
  14. unigram.json +3 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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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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  *.zip filter=lfs diff=lfs merge=lfs -text
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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
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,433 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: Emergency insurance payouts complement humanitarian assistance by providing
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+ timely financial resources that facilitate quicker recovery from climate disasters.
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+ - text: "c) Establish strategic and operational partnerships and alliances with private,\
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+ \ public and civil society \norganizations in food and nutrition."
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+ - text: 'COVID-19: The Development Program for Drinking Water Supply and Sanitation
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+ Systems of the Kyrgyz Republic until 2026 was approved.
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+
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+
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+ The Program is aimed at increasing the provision of drinking water of standard
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+ quality, improving the health and quality of life of the population of the republic,
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+ reducing the harmful effects on the environment through the construction, reconstruction,
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+ and modernization of drinking water supply and sanitation systems.'
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+ - text: "The program mainly aims at \nthe construction of rural roads, capacity building\
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+ \ of local bodies, and \nawareness raising activities."
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+ - text: "Mr. Speaker, the PF Government \n\nremains committed to ensuring that all\
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+ \ \n\nZambians have access to clean water supply \n\nand sanitation services."
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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: false
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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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 MultiOutputClassifier 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 MultiOutputClassifier instance
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+ - **Maximum Sequence Length:** 128 tokens
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+ <!-- - **Number of Classes:** Unknown -->
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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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+ ## 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("faodl/20250909_model_g20_multilabel_MiniLM-L12-all-labels-artificial-governance-multi-output")
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+ # Run inference
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+ preds = model("The program mainly aims at
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+ the construction of rural roads, capacity building of local bodies, and
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+ awareness raising activities.")
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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 | 1 | 41.6795 | 1753 |
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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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+ - num_iterations: 50
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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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+ | 0.0001 | 1 | 0.184 | - |
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+ | 0.0039 | 50 | 0.1927 | - |
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+ | 0.0078 | 100 | 0.1729 | - |
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+ | 0.0117 | 150 | 0.1484 | - |
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+ | 0.0156 | 200 | 0.1301 | - |
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+ | 0.0196 | 250 | 0.1134 | - |
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+ | 0.0235 | 300 | 0.1079 | - |
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+ | 0.0274 | 350 | 0.1021 | - |
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+ | 0.0313 | 400 | 0.0876 | - |
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+ | 0.0352 | 450 | 0.0834 | - |
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+ | 0.0391 | 500 | 0.0886 | - |
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+ | 0.0430 | 550 | 0.0728 | - |
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+ | 0.0469 | 600 | 0.0775 | - |
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+ | 0.0508 | 650 | 0.0811 | - |
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+ | 0.0548 | 700 | 0.0745 | - |
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+ | 0.0587 | 750 | 0.0753 | - |
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+ | 0.0626 | 800 | 0.0745 | - |
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+ | 0.0665 | 850 | 0.07 | - |
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+ | 0.0704 | 900 | 0.0702 | - |
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+ | 0.0743 | 950 | 0.0707 | - |
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+ | 0.0782 | 1000 | 0.0702 | - |
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+ | 0.0821 | 1050 | 0.0607 | - |
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+ | 0.0860 | 1100 | 0.067 | - |
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+ | 0.0899 | 1150 | 0.065 | - |
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+ | 0.0939 | 1200 | 0.0659 | - |
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+ | 0.0978 | 1250 | 0.066 | - |
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+ | 0.1017 | 1300 | 0.066 | - |
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+ | 0.1056 | 1350 | 0.06 | - |
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+ | 0.1095 | 1400 | 0.0609 | - |
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+ | 0.1134 | 1450 | 0.0587 | - |
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+ | 0.2346 | 3000 | 0.038 | - |
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+ | 0.2386 | 3050 | 0.0325 | - |
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+ | 0.2425 | 3100 | 0.0345 | - |
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+ | 0.2464 | 3150 | 0.0341 | - |
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+ | 0.2503 | 3200 | 0.0415 | - |
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+ | 0.2581 | 3300 | 0.0355 | - |
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+ | 0.2620 | 3350 | 0.033 | - |
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+ | 0.3285 | 4200 | 0.0271 | - |
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364
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365
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366
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368
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+ | 0.9386 | 12000 | 0.0175 | - |
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+ | 0.9934 | 12700 | 0.0149 | - |
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+ | 0.9973 | 12750 | 0.0172 | - |
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+
392
+ ### Framework Versions
393
+ - Python: 3.12.11
394
+ - SetFit: 1.1.3
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+ - Sentence Transformers: 5.1.0
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+ - Transformers: 4.56.1
397
+ - PyTorch: 2.8.0+cu126
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+ - Datasets: 4.0.0
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+ - Tokenizers: 0.22.0
400
+
401
+ ## Citation
402
+
403
+ ### BibTeX
404
+ ```bibtex
405
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
406
+ doi = {10.48550/ARXIV.2209.11055},
407
+ url = {https://arxiv.org/abs/2209.11055},
408
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
409
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
411
+ publisher = {arXiv},
412
+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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