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Chernoffface
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fs-setfit-multilable-model

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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  1. README.md +130 -492
  2. config.json +1 -1
  3. config_sentence_transformers.json +1 -1
  4. config_setfit.json +6 -6
  5. model.safetensors +1 -1
  6. model_head.pkl +2 -2
README.md CHANGED
@@ -1,493 +1,131 @@
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- ---
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- base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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- library_name: setfit
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- metrics:
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- - accuracy
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- pipeline_tag: text-classification
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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: How much should I invest in communication activities?
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- - text: In addition, we will consider public reactions and reviews of these works.
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- - text: Grundlagen der Fachdidaktik Pädagogik
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- - text: >-
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- Die Einzelthemen umfassen: * Hard- and Software-Architecture of Modern Game
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- Systems * Time Management in Milliseconds * Asset Loading and Compression *
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- Physically Based Realtime Rendering and Animations * Handling of Large Game
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- Scenes * Audio Simulation and Mixing * Constraint-Based Physics Simulation *
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- Artificial Intelligence for Games * Multiplayer-Networking * Procedural
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- Content Creation * Integration of Scripting Languages * Optimization and
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- parallelization of CPU and GPU Code Die Übungen enthalten Theorie- und
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- Praxisanteile.
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- - text: >-
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- Wie entsteht überhaupt eine Ausstellung und in diesem Fall: eine, die
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- weniger auf den Wert des Originals als die Kreativität ihrer Besucher setzt?
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- inference: false
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- language:
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- - de
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- - en
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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:** 6 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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- ## 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("Chernoffface/fs-setfit-multilable-model")
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- # Run inference
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- preds = model("Grundlagen der Fachdidaktik Pädagogik")
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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 | 12.9119 | 131 |
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-
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- ### Training Hyperparameters
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- - batch_size: (16, 16)
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- - num_epochs: (2, 2)
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- - max_steps: -1
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- - sampling_strategy: oversampling
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- - num_iterations: 40
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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 | 0.1571 | - |
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- | 0.0063 | 50 | 0.1986 | - |
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- | 0.0127 | 100 | 0.1774 | - |
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- | 0.0190 | 150 | 0.136 | - |
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- | 0.0254 | 200 | 0.1061 | - |
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- | 0.0317 | 250 | 0.0779 | - |
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- | 0.0380 | 300 | 0.0671 | - |
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- | 0.0444 | 350 | 0.0482 | - |
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- | 0.0507 | 400 | 0.0444 | - |
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- | 0.0571 | 450 | 0.0427 | - |
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- | 0.0634 | 500 | 0.0323 | - |
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- | 0.0698 | 550 | 0.0274 | - |
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- | 0.0761 | 600 | 0.0301 | - |
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- | 0.0824 | 650 | 0.0259 | - |
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- | 0.0888 | 700 | 0.0274 | - |
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- | 0.0951 | 750 | 0.0305 | - |
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- | 0.1015 | 800 | 0.0221 | - |
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- | 0.1078 | 850 | 0.0185 | - |
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- | 0.1141 | 900 | 0.0208 | - |
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- | 0.1205 | 950 | 0.0198 | - |
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- | 0.1268 | 1000 | 0.0107 | - |
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- | 0.1332 | 1050 | 0.0149 | - |
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- | 0.1395 | 1100 | 0.0162 | - |
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- | 0.1458 | 1150 | 0.0119 | - |
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- | 0.1522 | 1200 | 0.0162 | - |
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- | 0.1585 | 1250 | 0.0133 | - |
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- | 0.1649 | 1300 | 0.0177 | - |
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- | 0.1712 | 1350 | 0.0102 | - |
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- | 0.1776 | 1400 | 0.0224 | - |
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- | 0.1839 | 1450 | 0.0107 | - |
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- | 0.1902 | 1500 | 0.0182 | - |
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- | 0.1966 | 1550 | 0.0137 | - |
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- | 0.2029 | 1600 | 0.0158 | - |
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- | 0.2093 | 1650 | 0.0142 | - |
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- | 0.2156 | 1700 | 0.0117 | - |
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- | 0.2219 | 1750 | 0.0161 | - |
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- | 0.2283 | 1800 | 0.0128 | - |
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- | 0.2346 | 1850 | 0.0118 | - |
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- | 0.2410 | 1900 | 0.0125 | - |
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- | 0.2473 | 1950 | 0.0135 | - |
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- | 0.2536 | 2000 | 0.0123 | - |
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- | 0.2600 | 2050 | 0.0128 | - |
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- | 0.2663 | 2100 | 0.0119 | - |
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- | 0.2727 | 2150 | 0.0074 | - |
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- | 0.2790 | 2200 | 0.0116 | - |
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- | 0.2854 | 2250 | 0.0088 | - |
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- | 0.2917 | 2300 | 0.008 | - |
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- | 0.2980 | 2350 | 0.0137 | - |
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- | 0.3044 | 2400 | 0.0087 | - |
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- | 0.3107 | 2450 | 0.0107 | - |
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- | 0.3171 | 2500 | 0.0118 | - |
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- | 0.3234 | 2550 | 0.0096 | - |
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- | 0.3297 | 2600 | 0.0073 | - |
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- | 0.3361 | 2650 | 0.0125 | - |
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- | 0.3424 | 2700 | 0.0085 | - |
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- | 0.3488 | 2750 | 0.0081 | - |
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- | 0.3551 | 2800 | 0.0097 | - |
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- | 0.3614 | 2850 | 0.0104 | - |
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- | 0.3678 | 2900 | 0.0062 | - |
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- | 0.3741 | 2950 | 0.0124 | - |
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- | 0.3805 | 3000 | 0.0115 | - |
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- | 0.3868 | 3050 | 0.012 | - |
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- | 0.3932 | 3100 | 0.0147 | - |
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- | 0.3995 | 3150 | 0.0097 | - |
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- | 0.4058 | 3200 | 0.0107 | - |
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- | 0.4122 | 3250 | 0.0074 | - |
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- | 0.4185 | 3300 | 0.013 | - |
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- | 0.4249 | 3350 | 0.0115 | - |
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- | 0.4312 | 3400 | 0.008 | - |
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- | 0.4375 | 3450 | 0.0087 | - |
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- | 0.4439 | 3500 | 0.0099 | - |
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- | 0.4502 | 3550 | 0.0076 | - |
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- | 0.4566 | 3600 | 0.0118 | - |
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- | 0.4629 | 3650 | 0.013 | - |
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- | 0.4692 | 3700 | 0.0107 | - |
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- | 0.4756 | 3750 | 0.0123 | - |
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- | 0.4819 | 3800 | 0.0101 | - |
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- | 0.4883 | 3850 | 0.0095 | - |
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- | 0.4946 | 3900 | 0.01 | - |
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- | 0.5010 | 3950 | 0.0068 | - |
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- | 0.5073 | 4000 | 0.0064 | - |
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- | 0.5136 | 4050 | 0.0096 | - |
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- | 0.5200 | 4100 | 0.0063 | - |
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- | 0.5263 | 4150 | 0.0083 | - |
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- | 0.5327 | 4200 | 0.0067 | - |
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- | 0.5390 | 4250 | 0.0095 | - |
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- | 0.5453 | 4300 | 0.0097 | - |
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- | 0.5517 | 4350 | 0.0057 | - |
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- | 0.5580 | 4400 | 0.0101 | - |
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- | 0.5644 | 4450 | 0.0101 | - |
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- | 0.5707 | 4500 | 0.0043 | - |
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- | 0.5770 | 4550 | 0.0099 | - |
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- | 0.5834 | 4600 | 0.0091 | - |
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- | 0.5897 | 4650 | 0.0065 | - |
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- | 0.5961 | 4700 | 0.0071 | - |
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- | 0.6024 | 4750 | 0.0035 | - |
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- | 0.6088 | 4800 | 0.0088 | - |
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- | 0.6151 | 4850 | 0.0079 | - |
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- | 0.6214 | 4900 | 0.0094 | - |
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- | 0.6278 | 4950 | 0.0105 | - |
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- | 0.6341 | 5000 | 0.0091 | - |
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- | 0.6468 | 5100 | 0.0081 | - |
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- | 0.6531 | 5150 | 0.0087 | - |
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- | 0.6595 | 5200 | 0.0091 | - |
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- | 0.6658 | 5250 | 0.0071 | - |
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- | 0.6722 | 5300 | 0.0072 | - |
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- | 0.6785 | 5350 | 0.0084 | - |
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- | 0.6848 | 5400 | 0.0099 | - |
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- | 0.6912 | 5450 | 0.004 | - |
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- | 0.6975 | 5500 | 0.0038 | - |
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- | 0.7039 | 5550 | 0.0072 | - |
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- | 0.7102 | 5600 | 0.0084 | - |
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- | 0.7166 | 5650 | 0.004 | - |
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- | 0.7229 | 5700 | 0.0077 | - |
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- | 0.7292 | 5750 | 0.0066 | - |
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- | 0.7356 | 5800 | 0.0043 | - |
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- | 0.7419 | 5850 | 0.0054 | - |
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- | 0.7483 | 5900 | 0.0107 | - |
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- | 0.7546 | 5950 | 0.0046 | - |
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- | 0.7609 | 6000 | 0.0075 | - |
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- | 0.7673 | 6050 | 0.0106 | - |
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- | 0.7736 | 6100 | 0.0063 | - |
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- | 0.7800 | 6150 | 0.007 | - |
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- | 0.7863 | 6200 | 0.0066 | - |
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- | 0.7926 | 6250 | 0.0067 | - |
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- | 0.7990 | 6300 | 0.0078 | - |
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- | 0.8053 | 6350 | 0.0093 | - |
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- | 0.8117 | 6400 | 0.0055 | - |
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- | 0.8244 | 6500 | 0.0115 | - |
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- | 0.8307 | 6550 | 0.0058 | - |
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- | 0.8370 | 6600 | 0.005 | - |
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- | 0.8434 | 6650 | 0.007 | - |
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- | 0.8561 | 6750 | 0.0086 | - |
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- | 0.8751 | 6900 | 0.006 | - |
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- | 0.8878 | 7000 | 0.0103 | - |
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- | 0.9512 | 7500 | 0.0044 | - |
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- | 0.9829 | 7750 | 0.0036 | - |
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- | 1.0019 | 7900 | 0.0103 | - |
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- | 1.0082 | 7950 | 0.004 | - |
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- | 1.0399 | 8200 | 0.0077 | - |
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- | 1.0463 | 8250 | 0.0062 | - |
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- | 1.0526 | 8300 | 0.005 | - |
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- | 1.0590 | 8350 | 0.0071 | - |
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- | 1.0653 | 8400 | 0.0042 | - |
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- | 1.0780 | 8500 | 0.0048 | - |
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- | 1.1858 | 9350 | 0.0037 | - |
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- | 1.1921 | 9400 | 0.007 | - |
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- | 1.1985 | 9450 | 0.0069 | - |
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- | 1.2175 | 9600 | 0.0065 | - |
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- | 1.3697 | 10800 | 0.0038 | - |
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- | 1.3824 | 10900 | 0.0059 | - |
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- | 1.4268 | 11250 | 0.0041 | - |
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- | 1.5029 | 11850 | 0.0039 | - |
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- | 1.5155 | 11950 | 0.0052 | - |
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- | 1.5219 | 12000 | 0.0034 | - |
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- | 1.5282 | 12050 | 0.0078 | - |
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- | 1.7438 | 13750 | 0.0044 | - |
411
- | 1.7502 | 13800 | 0.0062 | - |
412
- | 1.7565 | 13850 | 0.0032 | - |
413
- | 1.7628 | 13900 | 0.0043 | - |
414
- | 1.7692 | 13950 | 0.0079 | - |
415
- | 1.7755 | 14000 | 0.0053 | - |
416
- | 1.7819 | 14050 | 0.0044 | - |
417
- | 1.7882 | 14100 | 0.0064 | - |
418
- | 1.7945 | 14150 | 0.0051 | - |
419
- | 1.8009 | 14200 | 0.0088 | - |
420
- | 1.8072 | 14250 | 0.0048 | - |
421
- | 1.8136 | 14300 | 0.0044 | - |
422
- | 1.8199 | 14350 | 0.0071 | - |
423
- | 1.8263 | 14400 | 0.0058 | - |
424
- | 1.8326 | 14450 | 0.007 | - |
425
- | 1.8389 | 14500 | 0.0028 | - |
426
- | 1.8453 | 14550 | 0.0046 | - |
427
- | 1.8516 | 14600 | 0.0061 | - |
428
- | 1.8580 | 14650 | 0.0054 | - |
429
- | 1.8643 | 14700 | 0.004 | - |
430
- | 1.8706 | 14750 | 0.0034 | - |
431
- | 1.8770 | 14800 | 0.0044 | - |
432
- | 1.8833 | 14850 | 0.0033 | - |
433
- | 1.8897 | 14900 | 0.007 | - |
434
- | 1.8960 | 14950 | 0.0044 | - |
435
- | 1.9023 | 15000 | 0.0045 | - |
436
- | 1.9087 | 15050 | 0.0045 | - |
437
- | 1.9150 | 15100 | 0.0093 | - |
438
- | 1.9214 | 15150 | 0.0036 | - |
439
- | 1.9277 | 15200 | 0.0055 | - |
440
- | 1.9341 | 15250 | 0.0037 | - |
441
- | 1.9404 | 15300 | 0.0043 | - |
442
- | 1.9467 | 15350 | 0.0034 | - |
443
- | 1.9531 | 15400 | 0.0068 | - |
444
- | 1.9594 | 15450 | 0.0058 | - |
445
- | 1.9658 | 15500 | 0.0069 | - |
446
- | 1.9721 | 15550 | 0.0081 | - |
447
- | 1.9784 | 15600 | 0.0061 | - |
448
- | 1.9848 | 15650 | 0.0039 | - |
449
- | 1.9911 | 15700 | 0.0065 | - |
450
- | 1.9975 | 15750 | 0.0048 | - |
451
-
452
- ### Framework Versions
453
- - Python: 3.12.3
454
- - SetFit: 1.1.0
455
- - Sentence Transformers: 3.2.0
456
- - Transformers: 4.45.2
457
- - PyTorch: 2.5.0+cu121
458
- - Datasets: 3.0.1
459
- - Tokenizers: 0.20.1
460
-
461
- ## Citation
462
-
463
- ### BibTeX
464
- ```bibtex
465
- @article{https://doi.org/10.48550/arxiv.2209.11055,
466
- doi = {10.48550/ARXIV.2209.11055},
467
- url = {https://arxiv.org/abs/2209.11055},
468
- author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
469
- keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
470
- title = {Efficient Few-Shot Learning Without Prompts},
471
- publisher = {arXiv},
472
- year = {2022},
473
- copyright = {Creative Commons Attribution 4.0 International}
474
- }
475
- ```
476
-
477
- <!--
478
- ## Glossary
479
-
480
- *Clearly define terms in order to be accessible across audiences.*
481
- -->
482
-
483
- <!--
484
- ## Model Card Authors
485
-
486
- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
487
- -->
488
-
489
- <!--
490
- ## Model Card Contact
491
-
492
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
493
  -->
 
1
+ ---
2
+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
3
+ library_name: setfit
4
+ metrics:
5
+ - accuracy
6
+ pipeline_tag: text-classification
7
+ tags:
8
+ - setfit
9
+ - sentence-transformers
10
+ - text-classification
11
+ - generated_from_setfit_trainer
12
+ widget: []
13
+ inference: true
14
+ ---
15
+
16
+ # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
17
+
18
+ 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 OneVsRestClassifier instance is used for classification.
19
+
20
+ The model has been trained using an efficient few-shot learning technique that involves:
21
+
22
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
23
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
24
+
25
+ ## Model Details
26
+
27
+ ### Model Description
28
+ - **Model Type:** SetFit
29
+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
30
+ - **Classification head:** a OneVsRestClassifier instance
31
+ - **Maximum Sequence Length:** 128 tokens
32
+ - **Number of Classes:** 6 classes
33
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
34
+ <!-- - **Language:** Unknown -->
35
+ <!-- - **License:** Unknown -->
36
+
37
+ ### Model Sources
38
+
39
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
40
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
41
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
42
+
43
+ ## Uses
44
+
45
+ ### Direct Use for Inference
46
+
47
+ First install the SetFit library:
48
+
49
+ ```bash
50
+ pip install setfit
51
+ ```
52
+
53
+ Then you can load this model and run inference.
54
+
55
+ ```python
56
+ from setfit import SetFitModel
57
+
58
+ # Download from the 🤗 Hub
59
+ model = SetFitModel.from_pretrained("Chernoffface/fs-setfit-multilable-model")
60
+ # Run inference
61
+ preds = model("I loved the spiderman movie!")
62
+ ```
63
+
64
+ <!--
65
+ ### Downstream Use
66
+
67
+ *List how someone could finetune this model on their own dataset.*
68
+ -->
69
+
70
+ <!--
71
+ ### Out-of-Scope Use
72
+
73
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
74
+ -->
75
+
76
+ <!--
77
+ ## Bias, Risks and Limitations
78
+
79
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
80
+ -->
81
+
82
+ <!--
83
+ ### Recommendations
84
+
85
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
86
+ -->
87
+
88
+ ## Training Details
89
+
90
+ ### Framework Versions
91
+ - Python: 3.12.7
92
+ - SetFit: 1.1.0
93
+ - Sentence Transformers: 3.2.1
94
+ - Transformers: 4.45.2
95
+ - PyTorch: 2.5.0+cu121
96
+ - Datasets: 2.19.1
97
+ - Tokenizers: 0.20.1
98
+
99
+ ## Citation
100
+
101
+ ### BibTeX
102
+ ```bibtex
103
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
104
+ doi = {10.48550/ARXIV.2209.11055},
105
+ url = {https://arxiv.org/abs/2209.11055},
106
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
107
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
108
+ title = {Efficient Few-Shot Learning Without Prompts},
109
+ publisher = {arXiv},
110
+ year = {2022},
111
+ copyright = {Creative Commons Attribution 4.0 International}
112
+ }
113
+ ```
114
+
115
+ <!--
116
+ ## Glossary
117
+
118
+ *Clearly define terms in order to be accessible across audiences.*
119
+ -->
120
+
121
+ <!--
122
+ ## Model Card Authors
123
+
124
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
125
+ -->
126
+
127
+ <!--
128
+ ## Model Card Contact
129
+
130
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
131
  -->
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