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.gitattributes CHANGED
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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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  *.zip filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json ADDED
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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
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+ ---
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+ library_name: setfit
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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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+ base_model: intfloat/multilingual-e5-small
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: 'query: Ναι, ας πάμε!'
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+ - text: 'query: 信じられない!それはすごいね。熊を見たなんて!'
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+ - text: 'query: Taky dobře. Potkáme se zítra?'
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+ - text: 'query: Sì, mi farebbe piacere. A tra poco!'
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+ - text: 'query: Γεια σου, πώς είσαι;'
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+ pipeline_tag: text-classification
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+ inference: true
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+ ---
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+
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+ # SetFit with intfloat/multilingual-e5-small
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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 [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small)
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+ - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 2 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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+ | 1 | <ul><li>'query: ਚੰਗਾ ਜੀ, ਫਿਰ ਮਿਲਦੇ ਹਾਂ.'</li><li>'query: Agur, gero arte.'</li><li>"query: Me'n vaig ara."</li></ul> |
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+ | 0 | <ul><li>'query: Dobro, hvala. Kaj pa ti?'</li><li>'query: हाँ अगली बार जब तुम जाओ मुझे भी ले चलो मुझे भी प्रकृति में और गतिविधियाँ करनी हैं'</li><li>'query: Mirë, faleminderit. Po ju?'</li></ul> |
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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("setfit_model_id")
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+ # Run inference
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+ preds = model("query: Ναι, ας πάμε!")
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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 | 2 | 7.4364 | 21 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 292 |
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+ | 1 | 290 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 2)
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+ - num_epochs: (1, 16)
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+ - max_steps: -1
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+ - sampling_strategy: undersampling
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+ - body_learning_rate: (1e-05, 1e-05)
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+ - head_learning_rate: 0.001
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.1
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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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+ - seed: 42
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+ - run_name: intfloat/multilingual-e5-small
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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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 | 0.3645 | - |
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+ | 0.0047 | 50 | 0.3527 | - |
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+ | 0.0094 | 100 | 0.3424 | 0.3165 |
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+ | 0.0142 | 150 | 0.3108 | - |
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+ | 0.0189 | 200 | 0.2684 | 0.2215 |
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+ | 0.0236 | 250 | 0.2197 | - |
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+ | 0.0283 | 300 | 0.1707 | 0.1792 |
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+ | 0.0331 | 350 | 0.1501 | - |
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+ | 0.0378 | 400 | 0.0865 | 0.1607 |
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+ | 0.0425 | 450 | 0.0534 | - |
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+ | 0.0472 | 500 | 0.0307 | 0.1519 |
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+ | 0.0520 | 550 | 0.0342 | - |
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+ | 0.0567 | 600 | 0.0078 | 0.1478 |
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+ | 0.0614 | 650 | 0.0144 | - |
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+ | 0.0661 | 700 | 0.0658 | 0.1399 |
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+ | 0.0709 | 750 | 0.0021 | - |
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+ | 0.0756 | 800 | 0.0009 | 0.1512 |
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+ | 0.0803 | 850 | 0.0005 | - |
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+ | 0.0850 | 900 | 0.0018 | 0.1516 |
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+ | 0.0897 | 950 | 0.0011 | - |
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+ | 0.0945 | 1000 | 0.0012 | 0.1541 |
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+ | 0.0992 | 1050 | 0.0003 | - |
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+ | 0.1039 | 1100 | 0.0003 | 0.1415 |
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+ | 0.1086 | 1150 | 0.0003 | - |
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+ | 0.1134 | 1200 | 0.0002 | 0.1442 |
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+ | 0.1181 | 1250 | 0.0006 | - |
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+ | 0.1228 | 1300 | 0.0002 | 0.1298 |
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+ | 0.1275 | 1350 | 0.0002 | - |
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+ | 0.1323 | 1400 | 0.0001 | 0.1356 |
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+ | 0.1370 | 1450 | 0.0002 | - |
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+ | 0.1417 | 1500 | 0.0003 | 0.1493 |
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+ | 0.1464 | 1550 | 0.0003 | - |
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+ | 0.1512 | 1600 | 0.0002 | 0.15 |
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+ | 0.1559 | 1650 | 0.0002 | - |
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+ | 0.1606 | 1700 | 0.0003 | 0.1469 |
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+ | 0.1653 | 1750 | 0.0001 | - |
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+ | 0.1701 | 1800 | 0.0001 | 0.1554 |
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+ | 0.1748 | 1850 | 0.0002 | - |
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+ | 0.1795 | 1900 | 0.0001 | 0.168 |
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+ | 0.1842 | 1950 | 0.0001 | - |
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+ | 0.1889 | 2000 | 0.0004 | 0.1568 |
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+ | 0.1937 | 2050 | 0.0001 | - |
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+ | 0.1984 | 2100 | 0.0001 | 0.1513 |
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+ | 0.2031 | 2150 | 0.0001 | - |
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+ | 0.2078 | 2200 | 0.0003 | 0.1503 |
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+ | 0.2126 | 2250 | 0.0002 | - |
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+ | 0.2173 | 2300 | 0.0604 | 0.155 |
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+ | 0.2220 | 2350 | 0.0001 | - |
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+ | 0.2267 | 2400 | 0.0002 | 0.1739 |
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+ | 0.2315 | 2450 | 0.0006 | - |
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+ | 0.2362 | 2500 | 0.0002 | 0.1558 |
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+ | 0.2409 | 2550 | 0.0002 | - |
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+ | 0.2456 | 2600 | 0.0001 | 0.1393 |
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+ | 0.2504 | 2650 | 0.0004 | - |
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+ | 0.2551 | 2700 | 0.0003 | 0.1642 |
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+ | 0.2598 | 2750 | 0.0002 | - |
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+ | 0.2645 | 2800 | 0.0002 | 0.1776 |
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+ | 0.2692 | 2850 | 0.0 | - |
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+ | 0.2740 | 2900 | 0.0002 | 0.1794 |
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+ | 0.2787 | 2950 | 0.0001 | - |
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+ | 0.2834 | 3000 | 0.0001 | 0.183 |
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+ | 0.2881 | 3050 | 0.0001 | - |
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+ | 0.2929 | 3100 | 0.0001 | 0.1805 |
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+ | 0.2976 | 3150 | 0.0001 | - |
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+ | 0.3023 | 3200 | 0.0001 | 0.1757 |
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+ | 0.3070 | 3250 | 0.0001 | - |
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+ | 0.3118 | 3300 | 0.0001 | 0.1302 |
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+ | 0.3165 | 3350 | 0.0001 | - |
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+ | 0.3212 | 3400 | 0.0001 | 0.1348 |
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+ | 0.3259 | 3450 | 0.0001 | - |
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+ | 0.3307 | 3500 | 0.0005 | 0.1623 |
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+ | 0.3354 | 3550 | 0.0 | - |
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+ | 0.3401 | 3600 | 0.0 | 0.1286 |
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+ | 0.3448 | 3650 | 0.0 | - |
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+ | 0.3496 | 3700 | 0.0001 | 0.1736 |
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+ | 0.3543 | 3750 | 0.0 | - |
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+ | 0.3590 | 3800 | 0.0 | 0.127 |
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+ | 0.3637 | 3850 | 0.0 | - |
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+ | 0.3684 | 3900 | 0.0001 | 0.1231 |
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+ | 0.3732 | 3950 | 0.0 | - |
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+ | **0.3779** | **4000** | **0.0001** | **0.1261** |
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+ | 0.3826 | 4050 | 0.0001 | - |
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+ | 0.3873 | 4100 | 0.0 | 0.1216 |
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+ | 0.3921 | 4150 | 0.0 | - |
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+ | 0.3968 | 4200 | 0.0 | 0.1404 |
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+ | 0.4015 | 4250 | 0.0 | - |
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+ | 0.4062 | 4300 | 0.0 | 0.1466 |
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+ | 0.4110 | 4350 | 0.0 | - |
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+ | 0.4157 | 4400 | 0.0 | 0.1482 |
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+ | 0.4204 | 4450 | 0.0 | - |
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+ | 0.4251 | 4500 | 0.0 | 0.1547 |
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+ | 0.4299 | 4550 | 0.0 | - |
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+ | 0.4346 | 4600 | 0.0 | 0.1566 |
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+ | 0.4393 | 4650 | 0.0 | - |
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+ | 0.4440 | 4700 | 0.0 | 0.1684 |
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+ | 0.4487 | 4750 | 0.0 | - |
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+ | 0.4535 | 4800 | 0.0 | 0.1746 |
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+ | 0.4582 | 4850 | 0.0 | - |
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+ | 0.4629 | 4900 | 0.0 | 0.167 |
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+ | 0.4676 | 4950 | 0.0 | - |
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+ | 0.4724 | 5000 | 0.0001 | 0.1683 |
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+ | 0.4771 | 5050 | 0.0 | - |
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+ | 0.4818 | 5100 | 0.0 | 0.1693 |
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+ | 0.4865 | 5150 | 0.0 | - |
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+ | 0.4913 | 5200 | 0.0 | 0.1694 |
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+ | 0.4960 | 5250 | 0.0 | - |
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+ | 0.5007 | 5300 | 0.0 | 0.162 |
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+ | 0.5054 | 5350 | 0.0 | - |
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+ | 0.5102 | 5400 | 0.0 | 0.1388 |
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+ | 0.5149 | 5450 | 0.0 | - |
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+ | 0.5196 | 5500 | 0.0 | 0.1353 |
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+ | 0.5243 | 5550 | 0.0 | - |
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+ | 0.5291 | 5600 | 0.0 | 0.1401 |
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+ | 0.5338 | 5650 | 0.0 | - |
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+ | 0.5385 | 5700 | 0.0 | 0.1466 |
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+ | 0.5432 | 5750 | 0.0 | - |
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+ | 0.5479 | 5800 | 0.0 | 0.1529 |
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+ | 0.5527 | 5850 | 0.0 | - |
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+ | 0.5574 | 5900 | 0.0 | 0.1488 |
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+ | 0.5621 | 5950 | 0.0 | - |
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+ | 0.5668 | 6000 | 0.0 | 0.147 |
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+ | 0.5716 | 6050 | 0.0 | - |
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+ | 0.5763 | 6100 | 0.0 | 0.1493 |
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+ | 0.5810 | 6150 | 0.0 | - |
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+ | 0.5857 | 6200 | 0.0 | 0.1525 |
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+ | 0.5905 | 6250 | 0.0 | - |
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+ | 0.5952 | 6300 | 0.0 | 0.1505 |
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+ | 0.5999 | 6350 | 0.0 | - |
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+ | 0.6046 | 6400 | 0.0 | 0.1554 |
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+ | 0.6094 | 6450 | 0.0 | - |
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+ | 0.6141 | 6500 | 0.0 | 0.1546 |
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+ | 0.6188 | 6550 | 0.0 | - |
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+ | 0.6235 | 6600 | 0.0 | 0.1598 |
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+ | 0.6282 | 6650 | 0.0 | - |
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+ | 0.6330 | 6700 | 0.0 | 0.179 |
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+ | 0.6377 | 6750 | 0.0 | - |
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+ | 0.6424 | 6800 | 0.0 | 0.1719 |
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+ | 0.6471 | 6850 | 0.0001 | - |
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+ | 0.6519 | 6900 | 0.0 | 0.1812 |
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+ | 0.6566 | 6950 | 0.0 | - |
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+ | 0.6613 | 7000 | 0.0 | 0.1648 |
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+ | 0.6660 | 7050 | 0.0 | - |
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+ | 0.6708 | 7100 | 0.0 | 0.1717 |
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+ | 0.6755 | 7150 | 0.0 | - |
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+ | 0.6802 | 7200 | 0.0 | 0.1793 |
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+ | 0.6849 | 7250 | 0.0 | - |
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+ | 0.6897 | 7300 | 0.0 | 0.1766 |
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+ | 0.6944 | 7350 | 0.0 | - |
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+ | 0.6991 | 7400 | 0.0 | 0.177 |
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+ | 0.7038 | 7450 | 0.0 | - |
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+ | 0.7085 | 7500 | 0.0 | 0.1749 |
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+ | 0.7133 | 7550 | 0.0 | - |
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+ | 0.7180 | 7600 | 0.0 | 0.1814 |
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+ | 0.7227 | 7650 | 0.0 | - |
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+ | 0.7274 | 7700 | 0.0 | 0.1742 |
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+ | 0.7322 | 7750 | 0.0 | - |
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+ | 0.7369 | 7800 | 0.0 | 0.179 |
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+ | 0.7416 | 7850 | 0.0 | - |
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+ | 0.7463 | 7900 | 0.0 | 0.1767 |
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+ | 0.7511 | 7950 | 0.0 | - |
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+ | 0.7558 | 8000 | 0.0 | 0.1809 |
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+ | 0.7605 | 8050 | 0.0 | - |
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+ | 0.7652 | 8100 | 0.0 | 0.1767 |
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+ | 0.7700 | 8150 | 0.0 | - |
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+ | 0.7747 | 8200 | 0.0 | 0.1698 |
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+ | 0.7794 | 8250 | 0.0 | - |
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+ | 0.7841 | 8300 | 0.0 | 0.1772 |
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+ | 0.7889 | 8350 | 0.0 | - |
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+ | 0.7936 | 8400 | 0.0 | 0.1722 |
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+ | 0.7983 | 8450 | 0.0 | - |
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+ | 0.8030 | 8500 | 0.0 | 0.1671 |
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+ | 0.8077 | 8550 | 0.0 | - |
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+ | 0.8125 | 8600 | 0.0 | 0.181 |
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+ | 0.8172 | 8650 | 0.0 | - |
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+ | 0.8219 | 8700 | 0.0 | 0.1788 |
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+ | 0.8266 | 8750 | 0.0 | - |
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+ | 0.8314 | 8800 | 0.0 | 0.1784 |
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+ | 0.8361 | 8850 | 0.0 | - |
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+ | 0.8408 | 8900 | 0.0 | 0.1806 |
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+ | 0.8455 | 8950 | 0.0 | - |
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+ | 0.8503 | 9000 | 0.0 | 0.1783 |
313
+ | 0.8550 | 9050 | 0.0 | - |
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+ | 0.8597 | 9100 | 0.0 | 0.1783 |
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+ | 0.8644 | 9150 | 0.0 | - |
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+ | 0.8692 | 9200 | 0.0 | 0.1785 |
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+ | 0.8739 | 9250 | 0.0 | - |
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+ | 0.8786 | 9300 | 0.0 | 0.1772 |
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+ | 0.8833 | 9350 | 0.0 | - |
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+ | 0.8880 | 9400 | 0.0 | 0.1816 |
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+ | 0.8928 | 9450 | 0.0 | - |
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+ | 0.8975 | 9500 | 0.0 | 0.1794 |
323
+ | 0.9022 | 9550 | 0.0 | - |
324
+ | 0.9069 | 9600 | 0.0 | 0.168 |
325
+ | 0.9117 | 9650 | 0.0 | - |
326
+ | 0.9164 | 9700 | 0.0 | 0.1771 |
327
+ | 0.9211 | 9750 | 0.0 | - |
328
+ | 0.9258 | 9800 | 0.0 | 0.1675 |
329
+ | 0.9306 | 9850 | 0.0 | - |
330
+ | 0.9353 | 9900 | 0.0 | 0.1746 |
331
+ | 0.9400 | 9950 | 0.0 | - |
332
+ | 0.9447 | 10000 | 0.0 | 0.1769 |
333
+ | 0.9495 | 10050 | 0.0 | - |
334
+ | 0.9542 | 10100 | 0.0 | 0.177 |
335
+ | 0.9589 | 10150 | 0.0 | - |
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+ | 0.9636 | 10200 | 0.0 | 0.1771 |
337
+ | 0.9684 | 10250 | 0.0 | - |
338
+ | 0.9731 | 10300 | 0.0 | 0.1794 |
339
+ | 0.9778 | 10350 | 0.0 | - |
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+ | 0.9825 | 10400 | 0.0 | 0.177 |
341
+ | 0.9872 | 10450 | 0.0 | - |
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+ | 0.9920 | 10500 | 0.0 | 0.1794 |
343
+ | 0.9967 | 10550 | 0.0 | - |
344
+
345
+ * The bold row denotes the saved checkpoint.
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+ ### Framework Versions
347
+ - Python: 3.10.11
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+ - SetFit: 1.0.3
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+ - Sentence Transformers: 2.7.0
350
+ - Transformers: 4.39.0
351
+ - PyTorch: 2.3.1
352
+ - Datasets: 2.20.0
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+ - Tokenizers: 0.15.2
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+
355
+ ## Citation
356
+
357
+ ### BibTeX
358
+ ```bibtex
359
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
360
+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
364
+ title = {Efficient Few-Shot Learning Without Prompts},
365
+ publisher = {arXiv},
366
+ 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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