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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: Suasana:Tempatnya ramai sekali dan ngantei banget. Suasana di dalam resto |
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sangat panas dan padat. Makanannya enak enak. |
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- text: bener2 pedes puolll:Rasanya sgt gak cocok dilidah gue orang bekasi.. ayamnya |
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ayam kampung sih tp kecil bgt (beli yg dada).. terus tempe bacem sgt padet dan |
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tahunya enak sih.. untuk sambel pedes bgt bener2 pedes puolll, tp rasanya gasukaa. |
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- text: gang:Suasana di dalam resto sangat panas dan padat. Makanannya enak enak. |
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Dan restonya ada di beberapa tempat dalam satu gang. |
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- text: tempe:Menu makanannya khas Sunda ada ayam, pepes ikan, babat, tahu, tempe, |
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sayur-sayur. Tidak banyak variasinya tapi kualitas rasanya oke. Saat itu pesen |
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ayam bakar, jukut goreng, tempe sama pepes tahu. Ini semuanya enak (menurut pendapat |
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pribadi). |
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- text: 'babat:Kemaren kebetulan makan babat sama nyobain cumi, buat tekstur babatnya |
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itu engga alot sama sekali dan tidak amis, sedangkan buat cumi utuh lumayan gede |
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juga tekstur kenyel kenyelnya dapet dan mateng juga sampe ke dalem. ' |
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pipeline_tag: text-classification |
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inference: false |
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model-index: |
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- name: SetFit Aspect Model |
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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: 0.80625 |
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name: Accuracy |
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--- |
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# SetFit Aspect Model |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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:** [Unknown](https://huggingface.co/unknown) --> |
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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:** [pahri/setfit-indo-resto-RM-ibu-imas-aspect](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-aspect) |
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- **SetFitABSA Polarity Model:** [pahri/setfit-indo-resto-RM-ibu-imas-polarity](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-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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| no aspect | <ul><li>'ambel leuncanya:ambel leuncanya enak terus pedesss'</li><li>'Warung Sunda:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'</li><li>'makanannya:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'</li></ul> | |
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| aspect | <ul><li>'ayam bakar:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'</li><li>'Ayam bakar:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'</li><li>'sambel terasi merah:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'</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** | 0.8063 | |
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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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"pahri/setfit-indo-resto-RM-ibu-imas-aspect", |
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"pahri/setfit-indo-resto-RM-ibu-imas-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 | 4 | 37.7180 | 93 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 371 | |
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| aspect | 51 | |
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### Training Hyperparameters |
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- batch_size: (6, 6) |
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- num_epochs: (1, 16) |
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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: False |
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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 | 0.4225 | - | |
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| 0.0021 | 50 | 0.2528 | - | |
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| 0.0043 | 100 | 0.3611 | - | |
|
| 0.0064 | 150 | 0.2989 | - | |
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| 0.0085 | 200 | 0.2907 | - | |
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| 0.0107 | 250 | 0.1609 | - | |
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| 0.0128 | 300 | 0.3534 | - | |
|
| 0.0149 | 350 | 0.1294 | - | |
|
| 0.0171 | 400 | 0.2797 | - | |
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| 0.0192 | 450 | 0.3119 | - | |
|
| 0.0213 | 500 | 0.004 | - | |
|
| 0.0235 | 550 | 0.1057 | - | |
|
| 0.0256 | 600 | 0.1049 | - | |
|
| 0.0277 | 650 | 0.1601 | - | |
|
| 0.0299 | 700 | 0.151 | - | |
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| 0.0320 | 750 | 0.1034 | - | |
|
| 0.0341 | 800 | 0.2356 | - | |
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| 0.0363 | 850 | 0.1335 | - | |
|
| 0.0384 | 900 | 0.0559 | - | |
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| 0.0405 | 950 | 0.0028 | - | |
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| 0.0427 | 1000 | 0.1307 | - | |
|
| 0.0448 | 1050 | 0.0049 | - | |
|
| 0.0469 | 1100 | 0.1348 | - | |
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| 0.0491 | 1150 | 0.0392 | - | |
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| 0.0512 | 1200 | 0.054 | - | |
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| 0.0533 | 1250 | 0.0016 | - | |
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| 0.0555 | 1300 | 0.0012 | - | |
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| 0.0576 | 1350 | 0.0414 | - | |
|
| 0.0597 | 1400 | 0.1087 | - | |
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| 0.0618 | 1450 | 0.0464 | - | |
|
| 0.0640 | 1500 | 0.0095 | - | |
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| 0.0661 | 1550 | 0.0011 | - | |
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| 0.0682 | 1600 | 0.0002 | - | |
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| 0.0704 | 1650 | 0.1047 | - | |
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| 0.0725 | 1700 | 0.001 | - | |
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| 0.0746 | 1750 | 0.0965 | - | |
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| 0.0768 | 1800 | 0.0002 | - | |
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| 0.0789 | 1850 | 0.1436 | - | |
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| 0.0810 | 1900 | 0.0011 | - | |
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| 0.0832 | 1950 | 0.001 | - | |
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| 0.0853 | 2000 | 0.1765 | - | |
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| 0.0874 | 2050 | 0.1401 | - | |
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| 0.0896 | 2100 | 0.0199 | - | |
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| 0.0917 | 2150 | 0.0 | - | |
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| 0.0938 | 2200 | 0.0023 | - | |
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| 0.0960 | 2250 | 0.0034 | - | |
|
| 0.0981 | 2300 | 0.0001 | - | |
|
| 0.1002 | 2350 | 0.0948 | - | |
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| 0.1024 | 2400 | 0.1634 | - | |
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| 0.1045 | 2450 | 0.0 | - | |
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| 0.1066 | 2500 | 0.0005 | - | |
|
| 0.1088 | 2550 | 0.0695 | - | |
|
| 0.1109 | 2600 | 0.0 | - | |
|
| 0.1130 | 2650 | 0.0067 | - | |
|
| 0.1152 | 2700 | 0.0025 | - | |
|
| 0.1173 | 2750 | 0.0013 | - | |
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| 0.1194 | 2800 | 0.1426 | - | |
|
| 0.1216 | 2850 | 0.0001 | - | |
|
| 0.1237 | 2900 | 0.0 | - | |
|
| 0.1258 | 2950 | 0.0 | - | |
|
| 0.1280 | 3000 | 0.0001 | - | |
|
| 0.1301 | 3050 | 0.0001 | - | |
|
| 0.1322 | 3100 | 0.0122 | - | |
|
| 0.1344 | 3150 | 0.0 | - | |
|
| 0.1365 | 3200 | 0.0001 | - | |
|
| 0.1386 | 3250 | 0.0041 | - | |
|
| 0.1408 | 3300 | 0.2549 | - | |
|
| 0.1429 | 3350 | 0.0062 | - | |
|
| 0.1450 | 3400 | 0.0154 | - | |
|
| 0.1472 | 3450 | 0.1776 | - | |
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| 0.1493 | 3500 | 0.0039 | - | |
|
| 0.1514 | 3550 | 0.0183 | - | |
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| 0.1536 | 3600 | 0.0045 | - | |
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| 0.1557 | 3650 | 0.1108 | - | |
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| 0.1578 | 3700 | 0.0002 | - | |
|
| 0.1600 | 3750 | 0.01 | - | |
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| 0.1621 | 3800 | 0.0002 | - | |
|
| 0.1642 | 3850 | 0.0001 | - | |
|
| 0.1664 | 3900 | 0.1612 | - | |
|
| 0.1685 | 3950 | 0.0107 | - | |
|
| 0.1706 | 4000 | 0.0548 | - | |
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| 0.1728 | 4050 | 0.0001 | - | |
|
| 0.1749 | 4100 | 0.0162 | - | |
|
| 0.1770 | 4150 | 0.1294 | - | |
|
| 0.1792 | 4200 | 0.0 | - | |
|
| 0.1813 | 4250 | 0.0032 | - | |
|
| 0.1834 | 4300 | 0.0051 | - | |
|
| 0.1855 | 4350 | 0.0 | - | |
|
| 0.1877 | 4400 | 0.0151 | - | |
|
| 0.1898 | 4450 | 0.0097 | - | |
|
| 0.1919 | 4500 | 0.0002 | - | |
|
| 0.1941 | 4550 | 0.0045 | - | |
|
| 0.1962 | 4600 | 0.0001 | - | |
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| 0.1983 | 4650 | 0.0001 | - | |
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| 0.2005 | 4700 | 0.0227 | - | |
|
| 0.2026 | 4750 | 0.0018 | - | |
|
| 0.2047 | 4800 | 0.0 | - | |
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| 0.2069 | 4850 | 0.0001 | - | |
|
| 0.2090 | 4900 | 0.0 | - | |
|
| 0.2111 | 4950 | 0.0 | - | |
|
| 0.2133 | 5000 | 0.0 | - | |
|
| 0.2154 | 5050 | 0.0002 | - | |
|
| 0.2175 | 5100 | 0.0002 | - | |
|
| 0.2197 | 5150 | 0.0038 | - | |
|
| 0.2218 | 5200 | 0.0 | - | |
|
| 0.2239 | 5250 | 0.0 | - | |
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| 0.2261 | 5300 | 0.0 | - | |
|
| 0.2282 | 5350 | 0.0028 | - | |
|
| 0.2303 | 5400 | 0.0 | - | |
|
| 0.2325 | 5450 | 0.1146 | - | |
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| 0.2346 | 5500 | 0.0 | - | |
|
| 0.2367 | 5550 | 0.0073 | - | |
|
| 0.2389 | 5600 | 0.0467 | - | |
|
| 0.2410 | 5650 | 0.0092 | - | |
|
| 0.2431 | 5700 | 0.0196 | - | |
|
| 0.2453 | 5750 | 0.0002 | - | |
|
| 0.2474 | 5800 | 0.0043 | - | |
|
| 0.2495 | 5850 | 0.0378 | - | |
|
| 0.2517 | 5900 | 0.0049 | - | |
|
| 0.2538 | 5950 | 0.0054 | - | |
|
| 0.2559 | 6000 | 0.1757 | - | |
|
| 0.2581 | 6050 | 0.0 | - | |
|
| 0.2602 | 6100 | 0.0001 | - | |
|
| 0.2623 | 6150 | 0.1327 | - | |
|
| 0.2645 | 6200 | 0.0 | - | |
|
| 0.2666 | 6250 | 0.0 | - | |
|
| 0.2687 | 6300 | 0.0 | - | |
|
| 0.2709 | 6350 | 0.0134 | - | |
|
| 0.2730 | 6400 | 0.0001 | - | |
|
| 0.2751 | 6450 | 0.0112 | - | |
|
| 0.2773 | 6500 | 0.0864 | - | |
|
| 0.2794 | 6550 | 0.0 | - | |
|
| 0.2815 | 6600 | 0.0094 | - | |
|
| 0.2837 | 6650 | 0.1358 | - | |
|
| 0.2858 | 6700 | 0.0155 | - | |
|
| 0.2879 | 6750 | 0.0025 | - | |
|
| 0.2901 | 6800 | 0.0002 | - | |
|
| 0.2922 | 6850 | 0.0001 | - | |
|
| 0.2943 | 6900 | 0.2809 | - | |
|
| 0.2965 | 6950 | 0.0 | - | |
|
| 0.2986 | 7000 | 0.0242 | - | |
|
| 0.3007 | 7050 | 0.0015 | - | |
|
| 0.3028 | 7100 | 0.0 | - | |
|
| 0.3050 | 7150 | 0.1064 | - | |
|
| 0.3071 | 7200 | 0.1636 | - | |
|
| 0.3092 | 7250 | 0.267 | - | |
|
| 0.3114 | 7300 | 0.1656 | - | |
|
| 0.3135 | 7350 | 0.0943 | - | |
|
| 0.3156 | 7400 | 0.189 | - | |
|
| 0.3178 | 7450 | 0.0055 | - | |
|
| 0.3199 | 7500 | 0.1286 | - | |
|
| 0.3220 | 7550 | 0.1062 | - | |
|
| 0.3242 | 7600 | 0.1275 | - | |
|
| 0.3263 | 7650 | 0.0101 | - | |
|
| 0.3284 | 7700 | 0.0162 | - | |
|
| 0.3306 | 7750 | 0.0001 | - | |
|
| 0.3327 | 7800 | 0.0001 | - | |
|
| 0.3348 | 7850 | 0.0003 | - | |
|
| 0.3370 | 7900 | 0.0 | - | |
|
| 0.3391 | 7950 | 0.135 | - | |
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| 0.3412 | 8000 | 0.0 | - | |
|
| 0.3434 | 8050 | 0.0125 | - | |
|
| 0.3455 | 8100 | 0.0004 | - | |
|
| 0.3476 | 8150 | 0.0 | - | |
|
| 0.3498 | 8200 | 0.2229 | - | |
|
| 0.3519 | 8250 | 0.0 | - | |
|
| 0.3540 | 8300 | 0.0051 | - | |
|
| 0.3562 | 8350 | 0.0 | - | |
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| 0.3583 | 8400 | 0.0001 | - | |
|
| 0.3604 | 8450 | 0.0 | - | |
|
| 0.3626 | 8500 | 0.1261 | - | |
|
| 0.3647 | 8550 | 0.0054 | - | |
|
| 0.3668 | 8600 | 0.1636 | - | |
|
| 0.3690 | 8650 | 0.0036 | - | |
|
| 0.3711 | 8700 | 0.0 | - | |
|
| 0.3732 | 8750 | 0.0027 | - | |
|
| 0.3754 | 8800 | 0.0 | - | |
|
| 0.3775 | 8850 | 0.1422 | - | |
|
| 0.3796 | 8900 | 0.1314 | - | |
|
| 0.3818 | 8950 | 0.003 | - | |
|
| 0.3839 | 9000 | 0.0 | - | |
|
| 0.3860 | 9050 | 0.0092 | - | |
|
| 0.3882 | 9100 | 0.0129 | - | |
|
| 0.3903 | 9150 | 0.0 | - | |
|
| 0.3924 | 9200 | 0.0 | - | |
|
| 0.3946 | 9250 | 0.1659 | - | |
|
| 0.3967 | 9300 | 0.0 | - | |
|
| 0.3988 | 9350 | 0.0 | - | |
|
| 0.4010 | 9400 | 0.0085 | - | |
|
| 0.4031 | 9450 | 0.0 | - | |
|
| 0.4052 | 9500 | 0.0 | - | |
|
| 0.4074 | 9550 | 0.0 | - | |
|
| 0.4095 | 9600 | 0.0112 | - | |
|
| 0.4116 | 9650 | 0.0 | - | |
|
| 0.4138 | 9700 | 0.0154 | - | |
|
| 0.4159 | 9750 | 0.0011 | - | |
|
| 0.4180 | 9800 | 0.0077 | - | |
|
| 0.4202 | 9850 | 0.0064 | - | |
|
| 0.4223 | 9900 | 0.0 | - | |
|
| 0.4244 | 9950 | 0.0 | - | |
|
| 0.4265 | 10000 | 0.0121 | - | |
|
| 0.4287 | 10050 | 0.0 | - | |
|
| 0.4308 | 10100 | 0.0 | - | |
|
| 0.4329 | 10150 | 0.0076 | - | |
|
| 0.4351 | 10200 | 0.0039 | - | |
|
| 0.4372 | 10250 | 0.2153 | - | |
|
| 0.4393 | 10300 | 0.0 | - | |
|
| 0.4415 | 10350 | 0.1218 | - | |
|
| 0.4436 | 10400 | 0.0077 | - | |
|
| 0.4457 | 10450 | 0.1311 | - | |
|
| 0.4479 | 10500 | 0.0 | - | |
|
| 0.4500 | 10550 | 0.0 | - | |
|
| 0.4521 | 10600 | 0.0 | - | |
|
| 0.4543 | 10650 | 0.0041 | - | |
|
| 0.4564 | 10700 | 0.0073 | - | |
|
| 0.4585 | 10750 | 0.0051 | - | |
|
| 0.4607 | 10800 | 0.0 | - | |
|
| 0.4628 | 10850 | 0.0 | - | |
|
| 0.4649 | 10900 | 0.0 | - | |
|
| 0.4671 | 10950 | 0.0001 | - | |
|
| 0.4692 | 11000 | 0.0 | - | |
|
| 0.4713 | 11050 | 0.1696 | - | |
|
| 0.4735 | 11100 | 0.0 | - | |
|
| 0.4756 | 11150 | 0.1243 | - | |
|
| 0.4777 | 11200 | 0.0 | - | |
|
| 0.4799 | 11250 | 0.0 | - | |
|
| 0.4820 | 11300 | 0.0003 | - | |
|
| 0.4841 | 11350 | 0.0707 | - | |
|
| 0.4863 | 11400 | 0.166 | - | |
|
| 0.4884 | 11450 | 0.4964 | - | |
|
| 0.4905 | 11500 | 0.0023 | - | |
|
| 0.4927 | 11550 | 0.0 | - | |
|
| 0.4948 | 11600 | 0.0 | - | |
|
| 0.4969 | 11650 | 0.173 | - | |
|
| 0.4991 | 11700 | 0.0 | - | |
|
| 0.5012 | 11750 | 0.0004 | - | |
|
| 0.5033 | 11800 | 0.0 | - | |
|
| 0.5055 | 11850 | 0.125 | - | |
|
| 0.5076 | 11900 | 0.0042 | - | |
|
| 0.5097 | 11950 | 0.012 | - | |
|
| 0.5119 | 12000 | 0.0046 | - | |
|
| 0.5140 | 12050 | 0.0001 | - | |
|
| 0.5161 | 12100 | 0.0062 | - | |
|
| 0.5183 | 12150 | 0.0 | - | |
|
| 0.5204 | 12200 | 0.017 | - | |
|
| 0.5225 | 12250 | 0.2668 | - | |
|
| 0.5247 | 12300 | 0.0986 | - | |
|
| 0.5268 | 12350 | 0.0071 | - | |
|
| 0.5289 | 12400 | 0.0055 | - | |
|
| 0.5311 | 12450 | 0.006 | - | |
|
| 0.5332 | 12500 | 0.0057 | - | |
|
| 0.5353 | 12550 | 0.0044 | - | |
|
| 0.5375 | 12600 | 0.0039 | - | |
|
| 0.5396 | 12650 | 0.1685 | - | |
|
| 0.5417 | 12700 | 0.125 | - | |
|
| 0.5438 | 12750 | 0.0026 | - | |
|
| 0.5460 | 12800 | 0.0 | - | |
|
| 0.5481 | 12850 | 0.0 | - | |
|
| 0.5502 | 12900 | 0.1024 | - | |
|
| 0.5524 | 12950 | 0.0 | - | |
|
| 0.5545 | 13000 | 0.0 | - | |
|
| 0.5566 | 13050 | 0.0083 | - | |
|
| 0.5588 | 13100 | 0.0 | - | |
|
| 0.5609 | 13150 | 0.0001 | - | |
|
| 0.5630 | 13200 | 0.0 | - | |
|
| 0.5652 | 13250 | 0.095 | - | |
|
| 0.5673 | 13300 | 0.0001 | - | |
|
| 0.5694 | 13350 | 0.0026 | - | |
|
| 0.5716 | 13400 | 0.0 | - | |
|
| 0.5737 | 13450 | 0.0041 | - | |
|
| 0.5758 | 13500 | 0.1654 | - | |
|
| 0.5780 | 13550 | 0.0003 | - | |
|
| 0.5801 | 13600 | 0.0056 | - | |
|
| 0.5822 | 13650 | 0.0 | - | |
|
| 0.5844 | 13700 | 0.1012 | - | |
|
| 0.5865 | 13750 | 0.0 | - | |
|
| 0.5886 | 13800 | 0.0001 | - | |
|
| 0.5908 | 13850 | 0.0042 | - | |
|
| 0.5929 | 13900 | 0.0122 | - | |
|
| 0.5950 | 13950 | 0.1047 | - | |
|
| 0.5972 | 14000 | 0.0 | - | |
|
| 0.5993 | 14050 | 0.0121 | - | |
|
| 0.6014 | 14100 | 0.0 | - | |
|
| 0.6036 | 14150 | 0.0 | - | |
|
| 0.6057 | 14200 | 0.0 | - | |
|
| 0.6078 | 14250 | 0.0105 | - | |
|
| 0.6100 | 14300 | 0.0 | - | |
|
| 0.6121 | 14350 | 0.011 | - | |
|
| 0.6142 | 14400 | 0.0329 | - | |
|
| 0.6164 | 14450 | 0.0942 | - | |
|
| 0.6185 | 14500 | 0.0173 | - | |
|
| 0.6206 | 14550 | 0.0 | - | |
|
| 0.6228 | 14600 | 0.1032 | - | |
|
| 0.6249 | 14650 | 0.016 | - | |
|
| 0.6270 | 14700 | 0.0079 | - | |
|
| 0.6292 | 14750 | 0.0 | - | |
|
| 0.6313 | 14800 | 0.1088 | - | |
|
| 0.6334 | 14850 | 0.0091 | - | |
|
| 0.6356 | 14900 | 0.0039 | - | |
|
| 0.6377 | 14950 | 0.0 | - | |
|
| 0.6398 | 15000 | 0.0 | - | |
|
| 0.6420 | 15050 | 0.0 | - | |
|
| 0.6441 | 15100 | 0.1654 | - | |
|
| 0.6462 | 15150 | 0.0 | - | |
|
| 0.6484 | 15200 | 0.0002 | - | |
|
| 0.6505 | 15250 | 0.0 | - | |
|
| 0.6526 | 15300 | 0.1745 | - | |
|
| 0.6548 | 15350 | 0.0 | - | |
|
| 0.6569 | 15400 | 0.156 | - | |
|
| 0.6590 | 15450 | 0.0 | - | |
|
| 0.6611 | 15500 | 0.0 | - | |
|
| 0.6633 | 15550 | 0.1755 | - | |
|
| 0.6654 | 15600 | 0.008 | - | |
|
| 0.6675 | 15650 | 0.0 | - | |
|
| 0.6697 | 15700 | 0.0 | - | |
|
| 0.6718 | 15750 | 0.0041 | - | |
|
| 0.6739 | 15800 | 0.0037 | - | |
|
| 0.6761 | 15850 | 0.0 | - | |
|
| 0.6782 | 15900 | 0.0 | - | |
|
| 0.6803 | 15950 | 0.0092 | - | |
|
| 0.6825 | 16000 | 0.0071 | - | |
|
| 0.6846 | 16050 | 0.0053 | - | |
|
| 0.6867 | 16100 | 0.0 | - | |
|
| 0.6889 | 16150 | 0.004 | - | |
|
| 0.6910 | 16200 | 0.0036 | - | |
|
| 0.6931 | 16250 | 0.0 | - | |
|
| 0.6953 | 16300 | 0.0 | - | |
|
| 0.6974 | 16350 | 0.184 | - | |
|
| 0.6995 | 16400 | 0.0 | - | |
|
| 0.7017 | 16450 | 0.0133 | - | |
|
| 0.7038 | 16500 | 0.0 | - | |
|
| 0.7059 | 16550 | 0.174 | - | |
|
| 0.7081 | 16600 | 0.0 | - | |
|
| 0.7102 | 16650 | 0.0233 | - | |
|
| 0.7123 | 16700 | 0.0117 | - | |
|
| 0.7145 | 16750 | 0.0272 | - | |
|
| 0.7166 | 16800 | 0.0095 | - | |
|
| 0.7187 | 16850 | 0.0 | - | |
|
| 0.7209 | 16900 | 0.1656 | - | |
|
| 0.7230 | 16950 | 0.0055 | - | |
|
| 0.7251 | 17000 | 0.0 | - | |
|
| 0.7273 | 17050 | 0.1716 | - | |
|
| 0.7294 | 17100 | 0.0 | - | |
|
| 0.7315 | 17150 | 0.0 | - | |
|
| 0.7337 | 17200 | 0.1035 | - | |
|
| 0.7358 | 17250 | 0.0694 | - | |
|
| 0.7379 | 17300 | 0.1733 | - | |
|
| 0.7401 | 17350 | 0.0092 | - | |
|
| 0.7422 | 17400 | 0.1656 | - | |
|
| 0.7443 | 17450 | 0.0 | - | |
|
| 0.7465 | 17500 | 0.1655 | - | |
|
| 0.7486 | 17550 | 0.0059 | - | |
|
| 0.7507 | 17600 | 0.1116 | - | |
|
| 0.7529 | 17650 | 0.0 | - | |
|
| 0.7550 | 17700 | 0.0068 | - | |
|
| 0.7571 | 17750 | 0.0053 | - | |
|
| 0.7593 | 17800 | 0.0 | - | |
|
| 0.7614 | 17850 | 0.0062 | - | |
|
| 0.7635 | 17900 | 0.0104 | - | |
|
| 0.7657 | 17950 | 0.1727 | - | |
|
| 0.7678 | 18000 | 0.0 | - | |
|
| 0.7699 | 18050 | 0.0 | - | |
|
| 0.7721 | 18100 | 0.0 | - | |
|
| 0.7742 | 18150 | 0.0714 | - | |
|
| 0.7763 | 18200 | 0.0 | - | |
|
| 0.7785 | 18250 | 0.0 | - | |
|
| 0.7806 | 18300 | 0.0002 | - | |
|
| 0.7827 | 18350 | 0.0 | - | |
|
| 0.7848 | 18400 | 0.0 | - | |
|
| 0.7870 | 18450 | 0.0996 | - | |
|
| 0.7891 | 18500 | 0.0 | - | |
|
| 0.7912 | 18550 | 0.0 | - | |
|
| 0.7934 | 18600 | 0.0139 | - | |
|
| 0.7955 | 18650 | 0.0 | - | |
|
| 0.7976 | 18700 | 0.1701 | - | |
|
| 0.7998 | 18750 | 0.0 | - | |
|
| 0.8019 | 18800 | 0.0001 | - | |
|
| 0.8040 | 18850 | 0.0 | - | |
|
| 0.8062 | 18900 | 0.0 | - | |
|
| 0.8083 | 18950 | 0.0 | - | |
|
| 0.8104 | 19000 | 0.0 | - | |
|
| 0.8126 | 19050 | 0.0 | - | |
|
| 0.8147 | 19100 | 0.1093 | - | |
|
| 0.8168 | 19150 | 0.0 | - | |
|
| 0.8190 | 19200 | 0.0 | - | |
|
| 0.8211 | 19250 | 0.0075 | - | |
|
| 0.8232 | 19300 | 0.1079 | - | |
|
| 0.8254 | 19350 | 0.0112 | - | |
|
| 0.8275 | 19400 | 0.1655 | - | |
|
| 0.8296 | 19450 | 0.0152 | - | |
|
| 0.8318 | 19500 | 0.1152 | - | |
|
| 0.8339 | 19550 | 0.0 | - | |
|
| 0.8360 | 19600 | 0.0 | - | |
|
| 0.8382 | 19650 | 0.0079 | - | |
|
| 0.8403 | 19700 | 0.0 | - | |
|
| 0.8424 | 19750 | 0.0 | - | |
|
| 0.8446 | 19800 | 0.0 | - | |
|
| 0.8467 | 19850 | 0.0 | - | |
|
| 0.8488 | 19900 | 0.1161 | - | |
|
| 0.8510 | 19950 | 0.0057 | - | |
|
| 0.8531 | 20000 | 0.0 | - | |
|
| 0.8552 | 20050 | 0.0046 | - | |
|
| 0.8574 | 20100 | 0.0 | - | |
|
| 0.8595 | 20150 | 0.0068 | - | |
|
| 0.8616 | 20200 | 0.0 | - | |
|
| 0.8638 | 20250 | 0.0 | - | |
|
| 0.8659 | 20300 | 0.0 | - | |
|
| 0.8680 | 20350 | 0.0 | - | |
|
| 0.8702 | 20400 | 0.0141 | - | |
|
| 0.8723 | 20450 | 0.0001 | - | |
|
| 0.8744 | 20500 | 0.0 | - | |
|
| 0.8766 | 20550 | 0.0 | - | |
|
| 0.8787 | 20600 | 0.0171 | - | |
|
| 0.8808 | 20650 | 0.0 | - | |
|
| 0.8830 | 20700 | 0.0 | - | |
|
| 0.8851 | 20750 | 0.0077 | - | |
|
| 0.8872 | 20800 | 0.0 | - | |
|
| 0.8894 | 20850 | 0.0 | - | |
|
| 0.8915 | 20900 | 0.0 | - | |
|
| 0.8936 | 20950 | 0.0 | - | |
|
| 0.8958 | 21000 | 0.0 | - | |
|
| 0.8979 | 21050 | 0.0 | - | |
|
| 0.9000 | 21100 | 0.0 | - | |
|
| 0.9021 | 21150 | 0.0 | - | |
|
| 0.9043 | 21200 | 0.0 | - | |
|
| 0.9064 | 21250 | 0.1048 | - | |
|
| 0.9085 | 21300 | 0.006 | - | |
|
| 0.9107 | 21350 | 0.0 | - | |
|
| 0.9128 | 21400 | 0.0 | - | |
|
| 0.9149 | 21450 | 0.005 | - | |
|
| 0.9171 | 21500 | 0.0 | - | |
|
| 0.9192 | 21550 | 0.0325 | - | |
|
| 0.9213 | 21600 | 0.0136 | - | |
|
| 0.9235 | 21650 | 0.0 | - | |
|
| 0.9256 | 21700 | 0.0062 | - | |
|
| 0.9277 | 21750 | 0.1656 | - | |
|
| 0.9299 | 21800 | 0.1648 | - | |
|
| 0.9320 | 21850 | 0.0 | - | |
|
| 0.9341 | 21900 | 0.0 | - | |
|
| 0.9363 | 21950 | 0.0 | - | |
|
| 0.9384 | 22000 | 0.2844 | - | |
|
| 0.9405 | 22050 | 0.0 | - | |
|
| 0.9427 | 22100 | 0.0 | - | |
|
| 0.9448 | 22150 | 0.0 | - | |
|
| 0.9469 | 22200 | 0.0 | - | |
|
| 0.9491 | 22250 | 0.0 | - | |
|
| 0.9512 | 22300 | 0.2096 | - | |
|
| 0.9533 | 22350 | 0.0073 | - | |
|
| 0.9555 | 22400 | 0.006 | - | |
|
| 0.9576 | 22450 | 0.0 | - | |
|
| 0.9597 | 22500 | 0.0079 | - | |
|
| 0.9619 | 22550 | 0.0071 | - | |
|
| 0.9640 | 22600 | 0.0 | - | |
|
| 0.9661 | 22650 | 0.006 | - | |
|
| 0.9683 | 22700 | 0.1048 | - | |
|
| 0.9704 | 22750 | 0.007 | - | |
|
| 0.9725 | 22800 | 0.0 | - | |
|
| 0.9747 | 22850 | 0.0 | - | |
|
| 0.9768 | 22900 | 0.007 | - | |
|
| 0.9789 | 22950 | 0.0 | - | |
|
| 0.9811 | 23000 | 0.1049 | - | |
|
| 0.9832 | 23050 | 0.0069 | - | |
|
| 0.9853 | 23100 | 0.0 | - | |
|
| 0.9875 | 23150 | 0.0 | - | |
|
| 0.9896 | 23200 | 0.0 | - | |
|
| 0.9917 | 23250 | 0.0 | - | |
|
| 0.9939 | 23300 | 0.007 | - | |
|
| 0.9960 | 23350 | 0.0147 | - | |
|
| 0.9981 | 23400 | 0.0 | - | |
|
|
|
### Framework Versions |
|
- Python: 3.10.13 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 2.7.0 |
|
- spaCy: 3.7.4 |
|
- Transformers: 4.36.2 |
|
- PyTorch: 2.1.2 |
|
- Datasets: 2.18.0 |
|
- Tokenizers: 0.15.2 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
```bibtex |
|
@article{https://doi.org/10.48550/arxiv.2209.11055, |
|
doi = {10.48550/ARXIV.2209.11055}, |
|
url = {https://arxiv.org/abs/2209.11055}, |
|
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
|
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
|
title = {Efficient Few-Shot Learning Without Prompts}, |
|
publisher = {arXiv}, |
|
year = {2022}, |
|
copyright = {Creative Commons Attribution 4.0 International} |
|
} |
|
``` |
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