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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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- absa |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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metrics: |
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- accuracy |
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widget: |
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- text: timur:unggul di atas tetangga di jalan 6 timur, taj mahal juga sangat sebanding, |
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dalam kualitas makanan, dengan baluchi yang terlalu dipuji (dan kurang layak). |
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- text: makanan:saya sangat merekomendasikan cafe st bart's untuk makanan mereka, |
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suasana dan layanan yang luar biasa melayani |
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- text: terong parmesan:parmesan terung juga enak, dan teman saya yang besar di manhattan |
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metakan bahwa tidak ada orang yang pantas mendapatkan ziti panggang yang lebih |
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enak dengan saus daging terong parmesan |
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- text: tuna lelehan:kami memesan tuna lelehan - itu datang dengan keluar keju yang |
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ha membuat sandwich tuna daging tuna |
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- text: manhattan metakan:parmesan terung juga enak, dan teman saya yang besar di |
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manhattan metakan bahwa tidak ada orang yang pantas mendapatkan ziti panggang |
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yang lebih enak dengan saus daging ziti panggang dengan saus daging |
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pipeline_tag: text-classification |
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inference: false |
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base_model: firqaaa/indo-sentence-bert-base |
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model-index: |
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- name: SetFit Aspect Model with firqaaa/indo-sentence-bert-base |
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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: 0.9087072065030483 |
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name: Accuracy |
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--- |
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# SetFit Aspect Model with firqaaa/indo-sentence-bert-base |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) 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. In particular, this model is in charge of filtering aspect span candidates. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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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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This model was trained within the context of a larger system for ABSA, which looks like so: |
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1. Use a spaCy model to select possible aspect span candidates. |
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2. **Use this SetFit model to filter these possible aspect span candidates.** |
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3. Use a SetFit model to classify the filtered aspect span candidates. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) |
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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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- **spaCy Model:** id_core_news_trf |
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- **SetFitABSA Aspect Model:** [firqaaa/indo-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect) |
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- **SetFitABSA Polarity Model:** [firqaaa/indo-setfit-absa-bert-base-restaurants-polarity](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-polarity) |
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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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### Model Sources |
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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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### Model Labels |
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| Label | Examples | |
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|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| aspect | <ul><li>'reservasi:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'nyonya rumah:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang nyonya rumah'</li><li>'busboy:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang.'</li></ul> | |
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| no aspect | <ul><li>'restoran:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'keharusan:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'sebelah kanan:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang nyonya rumah'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.9087 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import AbsaModel |
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# Download from the 🤗 Hub |
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model = AbsaModel.from_pretrained( |
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"firqaaa/setfit-indo-absa-restaurants-aspect", |
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"firqaaa/setfit-indo-absa-restaurants-polarity", |
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) |
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# Run inference |
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preds = model("The food was great, but the venue is just way too busy.") |
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``` |
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<!-- |
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### Downstream Use |
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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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### Out-of-Scope Use |
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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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## Bias, Risks and Limitations |
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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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### Recommendations |
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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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## Training Details |
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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 | 19.7819 | 59 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 2939 | |
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| aspect | 1468 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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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: True |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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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 | 0.3135 | - | |
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| 0.0001 | 50 | 0.3401 | - | |
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| 0.0001 | 100 | 0.3212 | - | |
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| 0.0002 | 150 | 0.3641 | - | |
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| 0.0003 | 200 | 0.3317 | - | |
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| 0.0004 | 250 | 0.2809 | - | |
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| 0.0004 | 300 | 0.2446 | - | |
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| 0.0005 | 350 | 0.284 | - | |
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| 0.0006 | 400 | 0.3257 | - | |
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| 0.0007 | 450 | 0.2996 | - | |
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| 0.0007 | 500 | 0.209 | 0.295 | |
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| 0.0008 | 550 | 0.2121 | - | |
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| 0.0009 | 600 | 0.2204 | - | |
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| 0.0010 | 650 | 0.3023 | - | |
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| 0.0010 | 700 | 0.3253 | - | |
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| 0.0011 | 750 | 0.233 | - | |
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| 0.0012 | 800 | 0.3131 | - | |
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| 0.0013 | 850 | 0.2873 | - | |
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| 0.0013 | 900 | 0.2028 | - | |
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| 0.0014 | 950 | 0.2608 | - | |
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| 0.0015 | 1000 | 0.2842 | 0.2696 | |
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| 0.0016 | 1050 | 0.2297 | - | |
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| 0.0016 | 1100 | 0.266 | - | |
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| 0.0017 | 1150 | 0.2771 | - | |
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| 0.0018 | 1200 | 0.2347 | - | |
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| 0.0019 | 1250 | 0.2539 | - | |
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| 0.0019 | 1300 | 0.3409 | - | |
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| 0.0020 | 1350 | 0.2925 | - | |
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| 0.0021 | 1400 | 0.2608 | - | |
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| 0.0021 | 1450 | 0.2792 | - | |
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| 0.0022 | 1500 | 0.261 | 0.2636 | |
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| 0.0023 | 1550 | 0.2596 | - | |
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| 0.0024 | 1600 | 0.2563 | - | |
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| 0.0024 | 1650 | 0.2329 | - | |
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| 0.0025 | 1700 | 0.2954 | - | |
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| 0.0026 | 1750 | 0.3329 | - | |
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| 0.0027 | 1800 | 0.2138 | - | |
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| 0.0027 | 1850 | 0.2591 | - | |
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| 0.0028 | 1900 | 0.268 | - | |
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| 0.0029 | 1950 | 0.2144 | - | |
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| 0.0030 | 2000 | 0.2361 | 0.2586 | |
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| 0.0030 | 2050 | 0.2322 | - | |
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| 0.0031 | 2100 | 0.2646 | - | |
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| 0.0032 | 2150 | 0.2018 | - | |
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| 0.0033 | 2200 | 0.2579 | - | |
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| 0.0033 | 2250 | 0.2501 | - | |
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| 0.0034 | 2300 | 0.2657 | - | |
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| 0.0035 | 2350 | 0.2272 | - | |
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| 0.0036 | 2400 | 0.2383 | - | |
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| 0.0036 | 2450 | 0.2615 | - | |
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| 0.0037 | 2500 | 0.2818 | 0.2554 | |
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| 0.0038 | 2550 | 0.2616 | - | |
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| 0.0039 | 2600 | 0.2225 | - | |
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| 0.0039 | 2650 | 0.2749 | - | |
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| 0.0040 | 2700 | 0.2572 | - | |
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| 0.0041 | 2750 | 0.2729 | - | |
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| 0.0041 | 2800 | 0.2559 | - | |
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| 0.0042 | 2850 | 0.2363 | - | |
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| 0.0043 | 2900 | 0.2518 | - | |
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| 0.0044 | 2950 | 0.1948 | - | |
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| 0.0044 | 3000 | 0.2842 | 0.2538 | |
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| 0.0045 | 3050 | 0.2243 | - | |
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| 0.0046 | 3100 | 0.2186 | - | |
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| 0.0047 | 3150 | 0.2829 | - | |
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| 0.0047 | 3200 | 0.2101 | - | |
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| 0.0048 | 3250 | 0.2156 | - | |
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| 0.0049 | 3300 | 0.2539 | - | |
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| 0.0050 | 3350 | 0.3005 | - | |
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| 0.0050 | 3400 | 0.2699 | - | |
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| 0.0051 | 3450 | 0.2431 | - | |
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| 0.0052 | 3500 | 0.2931 | 0.2515 | |
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| 0.0053 | 3550 | 0.2032 | - | |
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| 0.0053 | 3600 | 0.2451 | - | |
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| 0.0054 | 3650 | 0.2419 | - | |
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| 0.0055 | 3700 | 0.2267 | - | |
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| 0.0056 | 3750 | 0.2945 | - | |
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| 0.0056 | 3800 | 0.2689 | - | |
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| 0.0057 | 3850 | 0.2596 | - | |
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| 0.0058 | 3900 | 0.2978 | - | |
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| 0.0059 | 3950 | 0.2876 | - | |
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| 0.0059 | 4000 | 0.2484 | 0.2482 | |
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| 0.0060 | 4050 | 0.2698 | - | |
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| 0.0061 | 4100 | 0.2155 | - | |
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| 0.0061 | 4150 | 0.2474 | - | |
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| 0.0062 | 4200 | 0.2683 | - | |
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| 0.0063 | 4250 | 0.2979 | - | |
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| 0.0064 | 4300 | 0.2866 | - | |
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| 0.0064 | 4350 | 0.2604 | - | |
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| 0.0065 | 4400 | 0.1989 | - | |
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| 0.0066 | 4450 | 0.2708 | - | |
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| 0.0067 | 4500 | 0.2705 | 0.2407 | |
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| 0.0067 | 4550 | 0.2144 | - | |
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| 0.0068 | 4600 | 0.2503 | - | |
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| 0.0069 | 4650 | 0.2193 | - | |
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| 0.0070 | 4700 | 0.1796 | - | |
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| 0.0070 | 4750 | 0.2384 | - | |
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| 0.0071 | 4800 | 0.1933 | - | |
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| 0.0072 | 4850 | 0.2248 | - | |
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| 0.0073 | 4900 | 0.22 | - | |
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| 0.0073 | 4950 | 0.2052 | - | |
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| 0.0074 | 5000 | 0.2314 | 0.224 | |
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| 0.0075 | 5050 | 0.2279 | - | |
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| 0.0076 | 5100 | 0.2198 | - | |
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| 0.0076 | 5150 | 0.2332 | - | |
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| 0.0077 | 5200 | 0.1666 | - | |
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| 0.0078 | 5250 | 0.1949 | - | |
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| 0.0079 | 5300 | 0.1802 | - | |
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| 0.0079 | 5350 | 0.2496 | - | |
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| 0.0080 | 5400 | 0.2399 | - | |
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| 0.0081 | 5450 | 0.2042 | - | |
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| 0.0082 | 5500 | 0.1859 | 0.2077 | |
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| 0.0082 | 5550 | 0.2216 | - | |
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| 0.0083 | 5600 | 0.1227 | - | |
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| 0.0084 | 5650 | 0.2351 | - | |
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| 0.0084 | 5700 | 0.2735 | - | |
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| 0.0085 | 5750 | 0.1008 | - | |
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| 0.0086 | 5800 | 0.1568 | - | |
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| 0.0087 | 5850 | 0.1211 | - | |
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| 0.0087 | 5900 | 0.0903 | - | |
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| 0.0088 | 5950 | 0.1473 | - | |
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| 0.0089 | 6000 | 0.1167 | 0.1877 | |
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| 0.0090 | 6050 | 0.206 | - | |
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| 0.0090 | 6100 | 0.2392 | - | |
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| 0.0091 | 6150 | 0.116 | - | |
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| 0.0092 | 6200 | 0.1493 | - | |
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| 0.0093 | 6250 | 0.1373 | - | |
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| 0.0093 | 6300 | 0.1163 | - | |
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| 0.0094 | 6350 | 0.0669 | - | |
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| 0.0095 | 6400 | 0.0756 | - | |
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| 0.0096 | 6450 | 0.0788 | - | |
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| 0.0096 | 6500 | 0.1816 | 0.1838 | |
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| 0.0097 | 6550 | 0.1288 | - | |
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| 0.0098 | 6600 | 0.0946 | - | |
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| 0.0099 | 6650 | 0.1374 | - | |
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| 0.0099 | 6700 | 0.2167 | - | |
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| 0.0100 | 6750 | 0.0759 | - | |
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| 0.0101 | 6800 | 0.1543 | - | |
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| 0.0102 | 6850 | 0.0573 | - | |
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| 0.0102 | 6900 | 0.1169 | - | |
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| 0.0103 | 6950 | 0.0294 | - | |
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| **0.0104** | **7000** | **0.1241** | **0.1769** | |
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| 0.0104 | 7050 | 0.0803 | - | |
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| 0.0105 | 7100 | 0.0139 | - | |
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| 0.0106 | 7150 | 0.01 | - | |
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| 0.0107 | 7200 | 0.0502 | - | |
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| 0.0107 | 7250 | 0.0647 | - | |
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| 0.0108 | 7300 | 0.0117 | - | |
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| 0.0109 | 7350 | 0.0894 | - | |
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| 0.0110 | 7400 | 0.0101 | - | |
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| 0.0110 | 7450 | 0.0066 | - | |
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| 0.0111 | 7500 | 0.0347 | 0.1899 | |
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| 0.0112 | 7550 | 0.0893 | - | |
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| 0.0113 | 7600 | 0.0127 | - | |
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| 0.0113 | 7650 | 0.1285 | - | |
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| 0.0114 | 7700 | 0.0049 | - | |
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| 0.0115 | 7750 | 0.0571 | - | |
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| 0.0116 | 7800 | 0.0068 | - | |
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| 0.0116 | 7850 | 0.0586 | - | |
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| 0.0117 | 7900 | 0.0788 | - | |
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| 0.0118 | 7950 | 0.0655 | - | |
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| 0.0119 | 8000 | 0.0052 | 0.1807 | |
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| 0.0119 | 8050 | 0.0849 | - | |
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| 0.0120 | 8100 | 0.0133 | - | |
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| 0.0121 | 8150 | 0.0445 | - | |
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| 0.0122 | 8200 | 0.0118 | - | |
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| 0.0122 | 8250 | 0.0118 | - | |
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| 0.0123 | 8300 | 0.063 | - | |
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| 0.0124 | 8350 | 0.0751 | - | |
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| 0.0124 | 8400 | 0.058 | - | |
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| 0.0125 | 8450 | 0.002 | - | |
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| 0.0126 | 8500 | 0.0058 | 0.1804 | |
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| 0.0127 | 8550 | 0.0675 | - | |
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| 0.0127 | 8600 | 0.0067 | - | |
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| 0.0128 | 8650 | 0.0087 | - | |
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| 0.0129 | 8700 | 0.0028 | - | |
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| 0.0130 | 8750 | 0.0626 | - | |
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| 0.0130 | 8800 | 0.0563 | - | |
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| 0.0131 | 8850 | 0.0012 | - | |
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| 0.0132 | 8900 | 0.0067 | - | |
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| 0.0133 | 8950 | 0.0011 | - | |
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| 0.0133 | 9000 | 0.0105 | 0.189 | |
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| 0.0134 | 9050 | 0.101 | - | |
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| 0.0135 | 9100 | 0.1162 | - | |
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| 0.0136 | 9150 | 0.0593 | - | |
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| 0.0136 | 9200 | 0.0004 | - | |
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| 0.0137 | 9250 | 0.0012 | - | |
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| 0.0138 | 9300 | 0.0022 | - | |
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| 0.0139 | 9350 | 0.0033 | - | |
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| 0.0139 | 9400 | 0.0025 | - | |
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| 0.0140 | 9450 | 0.0578 | - | |
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| 0.0141 | 9500 | 0.0012 | 0.1967 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.13 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.2.2 |
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- spaCy: 3.7.4 |
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- Transformers: 4.36.2 |
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- PyTorch: 2.1.2+cu121 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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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}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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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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