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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: EPS:Why do I invest in $TSLA? Do I have blind faith? No. I closely watch their |
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EPS, their P/E, their products, their forecast. This is the only investment I |
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KNOW. And I know this is a great investment. I don’t say this to convince anyone. |
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These are my thoughts about my investment. |
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- text: EPS:$TSLA at 57x Street 2023 EPS (45x my 2023 EPS) seems an absurd valuation |
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for 50%+ volume/EPS growth fueled by the dual tailwinds of soaring EV adoption |
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and TSLA capacity. Investors seem overly worried Elon will sell more TSLA shares |
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even though he says “no further sales planned.” https://t.co/80siAfL847 |
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- text: 'TSLA:Cars ... for delivery ? Most likely so. $TSLA #GigaBerlin https://t.co/XL6auHEYjZ' |
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- text: companies:Mainstream media has done an amazing job at brainwashing people. |
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Today at work, we were asked what companies we believe in & I said @Tesla |
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because they make the safest cars & EVERYONE disagreed with me because they |
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heard“they catch on fire & the batteries cost 20k to replace” |
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- text: 'cash flow:The market won’t be able to hold Tesla stock down longer, once |
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all factories are ramping and in full production. |
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There’s a certain point where the # of cars being produced, revenue & profit |
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& cash flow generated makes the valuation of Tesla look ridiculous. |
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$TSLA #Tesla' |
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pipeline_tag: text-classification |
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inference: false |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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model-index: |
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- name: SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2 |
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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: 0.9798115746971736 |
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name: Accuracy |
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--- |
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# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2 |
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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 [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co./sentence-transformers/paraphrase-mpnet-base-v2) 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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co./sentence-transformers/paraphrase-mpnet-base-v2) |
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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:** en_core_web_lg |
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- **SetFitABSA Aspect Model:** [NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect](https://huggingface.co./NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect) |
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- **SetFitABSA Polarity Model:** [NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-polarity](https://huggingface.co./NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-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>'Tesla:Tesla could deliver 500K+ vehicles in Q4, increasing annual deliveries by 50%. Due to headwinds in 2022, now the manufacturer is ramping up production even harder to get as many EVs on the road as possible\n\n #Tesla $TSLA \nhttps://t.co/b2NCtZqDYn'</li><li>'vehicles:Tesla could deliver 500K+ vehicles in Q4, increasing annual deliveries by 50%. Due to headwinds in 2022, now the manufacturer is ramping up production even harder to get as many EVs on the road as possible\n\n #Tesla $TSLA \nhttps://t.co/b2NCtZqDYn'</li><li>'Q4:Tesla could deliver 500K+ vehicles in Q4, increasing annual deliveries by 50%. Due to headwinds in 2022, now the manufacturer is ramping up production even harder to get as many EVs on the road as possible\n\n #Tesla $TSLA \nhttps://t.co/b2NCtZqDYn'</li></ul> | |
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| aspect | <ul><li>"profit:I'm pretty sure, all an EV tax incentive will do, is raise the price of Teslas, at least for the next few years.\n\ni.e. just more profit for $TSLA\nAs if demand wasn't abundant enough already."</li><li>"price:I'm pretty sure, all an EV tax incentive will do, is raise the price of Teslas, at least for the next few years.\n\ni.e. just more profit for $TSLA\nAs if demand wasn't abundant enough already."</li><li>'car:John Hennessey gets a $TSLA Plaid. \nA retired OEM executive describes Tesla as a $30k car with $70k in batteries. \nThe perfect description of a Tesla https://t.co/m5J5m3AuMJ'</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** | 0.9798 | |
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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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"NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect", |
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"NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-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 | 11 | 41.4789 | 57 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 560 | |
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| aspect | 33 | |
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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: 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: False |
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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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| 0.0001 | 1 | 0.2511 | - | |
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| 0.0025 | 50 | 0.2558 | - | |
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| 0.0051 | 100 | 0.2147 | - | |
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| 0.0076 | 150 | 0.2265 | - | |
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| 0.0101 | 200 | 0.2474 | - | |
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| 0.0127 | 250 | 0.2286 | - | |
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| 0.0152 | 300 | 0.1717 | - | |
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| 0.0178 | 350 | 0.0737 | - | |
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| 0.0203 | 400 | 0.0231 | - | |
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| 0.0228 | 450 | 0.0069 | - | |
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| 0.0254 | 500 | 0.0032 | - | |
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| 0.0279 | 550 | 0.002 | - | |
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| 0.0304 | 600 | 0.0008 | - | |
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| 0.0330 | 650 | 0.0023 | - | |
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| 0.0355 | 700 | 0.002 | - | |
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| 0.0381 | 750 | 0.0008 | - | |
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| 0.0406 | 800 | 0.0019 | - | |
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| 0.0431 | 850 | 0.0003 | - | |
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| 0.0457 | 900 | 0.0004 | - | |
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| 0.0482 | 950 | 0.0005 | - | |
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| 0.0507 | 1000 | 0.0003 | - | |
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| 0.0533 | 1050 | 0.0006 | - | |
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| 0.0558 | 1100 | 0.0071 | - | |
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| 0.0584 | 1150 | 0.0001 | - | |
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| 0.0609 | 1200 | 0.0001 | - | |
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| 0.0634 | 1250 | 0.0001 | - | |
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| 0.0660 | 1300 | 0.0001 | - | |
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| 0.0685 | 1350 | 0.0004 | - | |
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| 0.0710 | 1400 | 0.0001 | - | |
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| 0.0736 | 1450 | 0.0002 | - | |
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| 0.0761 | 1500 | 0.0002 | - | |
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| 0.0787 | 1550 | 0.0002 | - | |
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| 0.0812 | 1600 | 0.0001 | - | |
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| 0.0837 | 1650 | 0.0001 | - | |
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| 0.0863 | 1700 | 0.0007 | - | |
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| 0.0888 | 1750 | 0.0001 | - | |
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| 0.0913 | 1800 | 0.0002 | - | |
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| 0.0939 | 1850 | 0.0011 | - | |
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| 0.0964 | 1900 | 0.0007 | - | |
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| 0.0990 | 1950 | 0.001 | - | |
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| 0.1015 | 2000 | 0.0003 | - | |
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| 0.1040 | 2050 | 0.0004 | - | |
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| 0.1066 | 2100 | 0.0006 | - | |
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| 0.1091 | 2150 | 0.0004 | - | |
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| 0.1116 | 2200 | 0.0 | - | |
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| 0.1142 | 2250 | 0.0 | - | |
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| 0.1167 | 2300 | 0.0001 | - | |
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| 0.1193 | 2350 | 0.0017 | - | |
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| 0.1218 | 2400 | 0.0007 | - | |
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| 0.1243 | 2450 | 0.0023 | - | |
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| 0.1269 | 2500 | 0.0 | - | |
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| 0.1294 | 2550 | 0.0 | - | |
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| 0.1319 | 2600 | 0.0007 | - | |
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| 0.1345 | 2650 | 0.0 | - | |
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| 0.1370 | 2700 | 0.0004 | - | |
|
| 0.1396 | 2750 | 0.0001 | - | |
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| 0.1421 | 2800 | 0.0002 | - | |
|
| 0.1446 | 2850 | 0.0019 | - | |
|
| 0.1472 | 2900 | 0.0002 | - | |
|
| 0.1497 | 2950 | 0.0001 | - | |
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| 0.1522 | 3000 | 0.0 | - | |
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| 0.1548 | 3050 | 0.0001 | - | |
|
| 0.1573 | 3100 | 0.0 | - | |
|
| 0.1598 | 3150 | 0.0001 | - | |
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| 0.1624 | 3200 | 0.0007 | - | |
|
| 0.1649 | 3250 | 0.0 | - | |
|
| 0.1675 | 3300 | 0.0002 | - | |
|
| 0.1700 | 3350 | 0.0004 | - | |
|
| 0.1725 | 3400 | 0.0 | - | |
|
| 0.1751 | 3450 | 0.0 | - | |
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| 0.1776 | 3500 | 0.0 | - | |
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| 0.1801 | 3550 | 0.0 | - | |
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| 0.1827 | 3600 | 0.0001 | - | |
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| 0.1852 | 3650 | 0.0 | - | |
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| 0.1878 | 3700 | 0.0001 | - | |
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| 0.1903 | 3750 | 0.0 | - | |
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| 0.1928 | 3800 | 0.0 | - | |
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| 0.1954 | 3850 | 0.0 | - | |
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| 0.1979 | 3900 | 0.0 | - | |
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| 0.2004 | 3950 | 0.0 | - | |
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| 0.2030 | 4000 | 0.0 | - | |
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| 0.2055 | 4050 | 0.0019 | - | |
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| 0.2081 | 4100 | 0.0 | - | |
|
| 0.2106 | 4150 | 0.0001 | - | |
|
| 0.2131 | 4200 | 0.0 | - | |
|
| 0.2157 | 4250 | 0.0 | - | |
|
| 0.2182 | 4300 | 0.0 | - | |
|
| 0.2207 | 4350 | 0.0 | - | |
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| 0.2233 | 4400 | 0.0005 | - | |
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| 0.2258 | 4450 | 0.0 | - | |
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| 0.2284 | 4500 | 0.0 | - | |
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| 0.2309 | 4550 | 0.0 | - | |
|
| 0.2334 | 4600 | 0.0 | - | |
|
| 0.2360 | 4650 | 0.0 | - | |
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| 0.2385 | 4700 | 0.0009 | - | |
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| 0.2410 | 4750 | 0.0 | - | |
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| 0.2436 | 4800 | 0.0 | - | |
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| 0.2461 | 4850 | 0.0 | - | |
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| 0.2487 | 4900 | 0.0002 | - | |
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| 0.2512 | 4950 | 0.0 | - | |
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| 0.2537 | 5000 | 0.0011 | - | |
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| 0.2563 | 5050 | 0.0 | - | |
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| 0.2588 | 5100 | 0.0 | - | |
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| 0.2613 | 5150 | 0.0 | - | |
|
| 0.2639 | 5200 | 0.0 | - | |
|
| 0.2664 | 5250 | 0.0 | - | |
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| 0.2690 | 5300 | 0.0 | - | |
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| 0.2715 | 5350 | 0.0026 | - | |
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| 0.2740 | 5400 | 0.0 | - | |
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| 0.2766 | 5450 | 0.0021 | - | |
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| 0.2791 | 5500 | 0.0 | - | |
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| 0.2816 | 5550 | 0.0001 | - | |
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| 0.2842 | 5600 | 0.0 | - | |
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| 0.2867 | 5650 | 0.0001 | - | |
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| 0.2893 | 5700 | 0.0 | - | |
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| 0.2918 | 5750 | 0.0 | - | |
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| 0.2943 | 5800 | 0.0 | - | |
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| 0.2969 | 5850 | 0.0 | - | |
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| 0.2994 | 5900 | 0.0 | - | |
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| 0.3019 | 5950 | 0.0 | - | |
|
| 0.3045 | 6000 | 0.0 | - | |
|
| 0.3070 | 6050 | 0.0 | - | |
|
| 0.3096 | 6100 | 0.0 | - | |
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| 0.3121 | 6150 | 0.0003 | - | |
|
| 0.3146 | 6200 | 0.0 | - | |
|
| 0.3172 | 6250 | 0.0 | - | |
|
| 0.3197 | 6300 | 0.0 | - | |
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| 0.3222 | 6350 | 0.0001 | - | |
|
| 0.3248 | 6400 | 0.0009 | - | |
|
| 0.3273 | 6450 | 0.0 | - | |
|
| 0.3298 | 6500 | 0.0 | - | |
|
| 0.3324 | 6550 | 0.0 | - | |
|
| 0.3349 | 6600 | 0.0 | - | |
|
| 0.3375 | 6650 | 0.0 | - | |
|
| 0.3400 | 6700 | 0.0 | - | |
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| 0.3425 | 6750 | 0.0 | - | |
|
| 0.3451 | 6800 | 0.0 | - | |
|
| 0.3476 | 6850 | 0.0 | - | |
|
| 0.3501 | 6900 | 0.0 | - | |
|
| 0.3527 | 6950 | 0.0 | - | |
|
| 0.3552 | 7000 | 0.0 | - | |
|
| 0.3578 | 7050 | 0.0 | - | |
|
| 0.3603 | 7100 | 0.0536 | - | |
|
| 0.3628 | 7150 | 0.0 | - | |
|
| 0.3654 | 7200 | 0.0 | - | |
|
| 0.3679 | 7250 | 0.0 | - | |
|
| 0.3704 | 7300 | 0.0 | - | |
|
| 0.3730 | 7350 | 0.0 | - | |
|
| 0.3755 | 7400 | 0.0 | - | |
|
| 0.3781 | 7450 | 0.0 | - | |
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| 0.3806 | 7500 | 0.0 | - | |
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| 0.3831 | 7550 | 0.0 | - | |
|
| 0.3857 | 7600 | 0.0 | - | |
|
| 0.3882 | 7650 | 0.0 | - | |
|
| 0.3907 | 7700 | 0.0 | - | |
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| 0.3933 | 7750 | 0.0019 | - | |
|
| 0.3958 | 7800 | 0.0 | - | |
|
| 0.3984 | 7850 | 0.0 | - | |
|
| 0.4009 | 7900 | 0.0548 | - | |
|
| 0.4034 | 7950 | 0.0 | - | |
|
| 0.4060 | 8000 | 0.0053 | - | |
|
| 0.4085 | 8050 | 0.0 | - | |
|
| 0.4110 | 8100 | 0.0 | - | |
|
| 0.4136 | 8150 | 0.0 | - | |
|
| 0.4161 | 8200 | 0.0 | - | |
|
| 0.4187 | 8250 | 0.0624 | - | |
|
| 0.4212 | 8300 | 0.0622 | - | |
|
| 0.4237 | 8350 | 0.0618 | - | |
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| 0.4263 | 8400 | 0.0001 | - | |
|
| 0.4288 | 8450 | 0.0 | - | |
|
| 0.4313 | 8500 | 0.0001 | - | |
|
| 0.4339 | 8550 | 0.0 | - | |
|
| 0.4364 | 8600 | 0.0 | - | |
|
| 0.4390 | 8650 | 0.0 | - | |
|
| 0.4415 | 8700 | 0.0012 | - | |
|
| 0.4440 | 8750 | 0.0001 | - | |
|
| 0.4466 | 8800 | 0.0005 | - | |
|
| 0.4491 | 8850 | 0.0 | - | |
|
| 0.4516 | 8900 | 0.0 | - | |
|
| 0.4542 | 8950 | 0.0 | - | |
|
| 0.4567 | 9000 | 0.0 | - | |
|
| 0.4593 | 9050 | 0.0 | - | |
|
| 0.4618 | 9100 | 0.0 | - | |
|
| 0.4643 | 9150 | 0.0 | - | |
|
| 0.4669 | 9200 | 0.0 | - | |
|
| 0.4694 | 9250 | 0.0408 | - | |
|
| 0.4719 | 9300 | 0.0498 | - | |
|
| 0.4745 | 9350 | 0.0 | - | |
|
| 0.4770 | 9400 | 0.0 | - | |
|
| 0.4795 | 9450 | 0.0017 | - | |
|
| 0.4821 | 9500 | 0.0 | - | |
|
| 0.4846 | 9550 | 0.0 | - | |
|
| 0.4872 | 9600 | 0.0 | - | |
|
| 0.4897 | 9650 | 0.0 | - | |
|
| 0.4922 | 9700 | 0.0 | - | |
|
| 0.4948 | 9750 | 0.0 | - | |
|
| 0.4973 | 9800 | 0.0589 | - | |
|
| 0.4998 | 9850 | 0.0 | - | |
|
| 0.5024 | 9900 | 0.0 | - | |
|
| 0.5049 | 9950 | 0.0015 | - | |
|
| 0.5075 | 10000 | 0.0 | - | |
|
| 0.5100 | 10050 | 0.0 | - | |
|
| 0.5125 | 10100 | 0.0 | - | |
|
| 0.5151 | 10150 | 0.0 | - | |
|
| 0.5176 | 10200 | 0.0 | - | |
|
| 0.5201 | 10250 | 0.0 | - | |
|
| 0.5227 | 10300 | 0.0013 | - | |
|
| 0.5252 | 10350 | 0.0023 | - | |
|
| 0.5278 | 10400 | 0.0 | - | |
|
| 0.5303 | 10450 | 0.0 | - | |
|
| 0.5328 | 10500 | 0.0 | - | |
|
| 0.5354 | 10550 | 0.0003 | - | |
|
| 0.5379 | 10600 | 0.0 | - | |
|
| 0.5404 | 10650 | 0.0 | - | |
|
| 0.5430 | 10700 | 0.0002 | - | |
|
| 0.5455 | 10750 | 0.0 | - | |
|
| 0.5481 | 10800 | 0.0 | - | |
|
| 0.5506 | 10850 | 0.0005 | - | |
|
| 0.5531 | 10900 | 0.0 | - | |
|
| 0.5557 | 10950 | 0.0 | - | |
|
| 0.5582 | 11000 | 0.0 | - | |
|
| 0.5607 | 11050 | 0.0 | - | |
|
| 0.5633 | 11100 | 0.0 | - | |
|
| 0.5658 | 11150 | 0.0 | - | |
|
| 0.5684 | 11200 | 0.0 | - | |
|
| 0.5709 | 11250 | 0.0 | - | |
|
| 0.5734 | 11300 | 0.0 | - | |
|
| 0.5760 | 11350 | 0.0008 | - | |
|
| 0.5785 | 11400 | 0.0 | - | |
|
| 0.5810 | 11450 | 0.0024 | - | |
|
| 0.5836 | 11500 | 0.0 | - | |
|
| 0.5861 | 11550 | 0.0 | - | |
|
| 0.5887 | 11600 | 0.0 | - | |
|
| 0.5912 | 11650 | 0.0 | - | |
|
| 0.5937 | 11700 | 0.001 | - | |
|
| 0.5963 | 11750 | 0.0 | - | |
|
| 0.5988 | 11800 | 0.0 | - | |
|
| 0.6013 | 11850 | 0.0 | - | |
|
| 0.6039 | 11900 | 0.0527 | - | |
|
| 0.6064 | 11950 | 0.0021 | - | |
|
| 0.6090 | 12000 | 0.0 | - | |
|
| 0.6115 | 12050 | 0.0 | - | |
|
| 0.6140 | 12100 | 0.0 | - | |
|
| 0.6166 | 12150 | 0.0 | - | |
|
| 0.6191 | 12200 | 0.0 | - | |
|
| 0.6216 | 12250 | 0.0 | - | |
|
| 0.6242 | 12300 | 0.0 | - | |
|
| 0.6267 | 12350 | 0.0006 | - | |
|
| 0.6292 | 12400 | 0.0 | - | |
|
| 0.6318 | 12450 | 0.0 | - | |
|
| 0.6343 | 12500 | 0.001 | - | |
|
| 0.6369 | 12550 | 0.0017 | - | |
|
| 0.6394 | 12600 | 0.0 | - | |
|
| 0.6419 | 12650 | 0.0 | - | |
|
| 0.6445 | 12700 | 0.0 | - | |
|
| 0.6470 | 12750 | 0.0012 | - | |
|
| 0.6495 | 12800 | 0.0 | - | |
|
| 0.6521 | 12850 | 0.0 | - | |
|
| 0.6546 | 12900 | 0.0 | - | |
|
| 0.6572 | 12950 | 0.0434 | - | |
|
| 0.6597 | 13000 | 0.0 | - | |
|
| 0.6622 | 13050 | 0.0 | - | |
|
| 0.6648 | 13100 | 0.0003 | - | |
|
| 0.6673 | 13150 | 0.0 | - | |
|
| 0.6698 | 13200 | 0.0 | - | |
|
| 0.6724 | 13250 | 0.0003 | - | |
|
| 0.6749 | 13300 | 0.0 | - | |
|
| 0.6775 | 13350 | 0.0 | - | |
|
| 0.6800 | 13400 | 0.0005 | - | |
|
| 0.6825 | 13450 | 0.0 | - | |
|
| 0.6851 | 13500 | 0.0011 | - | |
|
| 0.6876 | 13550 | 0.0475 | - | |
|
| 0.6901 | 13600 | 0.0 | - | |
|
| 0.6927 | 13650 | 0.0007 | - | |
|
| 0.6952 | 13700 | 0.0 | - | |
|
| 0.6978 | 13750 | 0.0 | - | |
|
| 0.7003 | 13800 | 0.0 | - | |
|
| 0.7028 | 13850 | 0.0 | - | |
|
| 0.7054 | 13900 | 0.0 | - | |
|
| 0.7079 | 13950 | 0.0015 | - | |
|
| 0.7104 | 14000 | 0.0034 | - | |
|
| 0.7130 | 14050 | 0.0009 | - | |
|
| 0.7155 | 14100 | 0.0 | - | |
|
| 0.7181 | 14150 | 0.0009 | - | |
|
| 0.7206 | 14200 | 0.0 | - | |
|
| 0.7231 | 14250 | 0.0003 | - | |
|
| 0.7257 | 14300 | 0.0004 | - | |
|
| 0.7282 | 14350 | 0.0 | - | |
|
| 0.7307 | 14400 | 0.0003 | - | |
|
| 0.7333 | 14450 | 0.0 | - | |
|
| 0.7358 | 14500 | 0.0 | - | |
|
| 0.7384 | 14550 | 0.0 | - | |
|
| 0.7409 | 14600 | 0.0 | - | |
|
| 0.7434 | 14650 | 0.0 | - | |
|
| 0.7460 | 14700 | 0.0018 | - | |
|
| 0.7485 | 14750 | 0.0012 | - | |
|
| 0.7510 | 14800 | 0.0 | - | |
|
| 0.7536 | 14850 | 0.0 | - | |
|
| 0.7561 | 14900 | 0.0013 | - | |
|
| 0.7587 | 14950 | 0.0 | - | |
|
| 0.7612 | 15000 | 0.0 | - | |
|
| 0.7637 | 15050 | 0.0 | - | |
|
| 0.7663 | 15100 | 0.0 | - | |
|
| 0.7688 | 15150 | 0.0 | - | |
|
| 0.7713 | 15200 | 0.0 | - | |
|
| 0.7739 | 15250 | 0.0 | - | |
|
| 0.7764 | 15300 | 0.0 | - | |
|
| 0.7790 | 15350 | 0.0 | - | |
|
| 0.7815 | 15400 | 0.0 | - | |
|
| 0.7840 | 15450 | 0.0 | - | |
|
| 0.7866 | 15500 | 0.0 | - | |
|
| 0.7891 | 15550 | 0.0 | - | |
|
| 0.7916 | 15600 | 0.0004 | - | |
|
| 0.7942 | 15650 | 0.0005 | - | |
|
| 0.7967 | 15700 | 0.0 | - | |
|
| 0.7992 | 15750 | 0.0 | - | |
|
| 0.8018 | 15800 | 0.0 | - | |
|
| 0.8043 | 15850 | 0.0 | - | |
|
| 0.8069 | 15900 | 0.0 | - | |
|
| 0.8094 | 15950 | 0.0555 | - | |
|
| 0.8119 | 16000 | 0.0 | - | |
|
| 0.8145 | 16050 | 0.0 | - | |
|
| 0.8170 | 16100 | 0.0 | - | |
|
| 0.8195 | 16150 | 0.0 | - | |
|
| 0.8221 | 16200 | 0.0 | - | |
|
| 0.8246 | 16250 | 0.0007 | - | |
|
| 0.8272 | 16300 | 0.0 | - | |
|
| 0.8297 | 16350 | 0.0 | - | |
|
| 0.8322 | 16400 | 0.0 | - | |
|
| 0.8348 | 16450 | 0.0003 | - | |
|
| 0.8373 | 16500 | 0.0 | - | |
|
| 0.8398 | 16550 | 0.0012 | - | |
|
| 0.8424 | 16600 | 0.0 | - | |
|
| 0.8449 | 16650 | 0.0 | - | |
|
| 0.8475 | 16700 | 0.0 | - | |
|
| 0.8500 | 16750 | 0.0 | - | |
|
| 0.8525 | 16800 | 0.0 | - | |
|
| 0.8551 | 16850 | 0.0 | - | |
|
| 0.8576 | 16900 | 0.0007 | - | |
|
| 0.8601 | 16950 | 0.0 | - | |
|
| 0.8627 | 17000 | 0.001 | - | |
|
| 0.8652 | 17050 | 0.0 | - | |
|
| 0.8678 | 17100 | 0.0 | - | |
|
| 0.8703 | 17150 | 0.0 | - | |
|
| 0.8728 | 17200 | 0.0 | - | |
|
| 0.8754 | 17250 | 0.0 | - | |
|
| 0.8779 | 17300 | 0.0 | - | |
|
| 0.8804 | 17350 | 0.0 | - | |
|
| 0.8830 | 17400 | 0.0007 | - | |
|
| 0.8855 | 17450 | 0.0 | - | |
|
| 0.8881 | 17500 | 0.0 | - | |
|
| 0.8906 | 17550 | 0.0505 | - | |
|
| 0.8931 | 17600 | 0.0 | - | |
|
| 0.8957 | 17650 | 0.0 | - | |
|
| 0.8982 | 17700 | 0.0008 | - | |
|
| 0.9007 | 17750 | 0.0 | - | |
|
| 0.9033 | 17800 | 0.0003 | - | |
|
| 0.9058 | 17850 | 0.0 | - | |
|
| 0.9084 | 17900 | 0.0 | - | |
|
| 0.9109 | 17950 | 0.0009 | - | |
|
| 0.9134 | 18000 | 0.0 | - | |
|
| 0.9160 | 18050 | 0.0 | - | |
|
| 0.9185 | 18100 | 0.0 | - | |
|
| 0.9210 | 18150 | 0.0 | - | |
|
| 0.9236 | 18200 | 0.0 | - | |
|
| 0.9261 | 18250 | 0.0 | - | |
|
| 0.9287 | 18300 | 0.0 | - | |
|
| 0.9312 | 18350 | 0.0008 | - | |
|
| 0.9337 | 18400 | 0.0 | - | |
|
| 0.9363 | 18450 | 0.0 | - | |
|
| 0.9388 | 18500 | 0.0 | - | |
|
| 0.9413 | 18550 | 0.0 | - | |
|
| 0.9439 | 18600 | 0.0 | - | |
|
| 0.9464 | 18650 | 0.0 | - | |
|
| 0.9489 | 18700 | 0.0 | - | |
|
| 0.9515 | 18750 | 0.0 | - | |
|
| 0.9540 | 18800 | 0.0 | - | |
|
| 0.9566 | 18850 | 0.0 | - | |
|
| 0.9591 | 18900 | 0.0 | - | |
|
| 0.9616 | 18950 | 0.0 | - | |
|
| 0.9642 | 19000 | 0.0 | - | |
|
| 0.9667 | 19050 | 0.0 | - | |
|
| 0.9692 | 19100 | 0.0 | - | |
|
| 0.9718 | 19150 | 0.0 | - | |
|
| 0.9743 | 19200 | 0.0 | - | |
|
| 0.9769 | 19250 | 0.0 | - | |
|
| 0.9794 | 19300 | 0.0005 | - | |
|
| 0.9819 | 19350 | 0.0 | - | |
|
| 0.9845 | 19400 | 0.0 | - | |
|
| 0.9870 | 19450 | 0.0 | - | |
|
| 0.9895 | 19500 | 0.0 | - | |
|
| 0.9921 | 19550 | 0.0011 | - | |
|
| 0.9946 | 19600 | 0.0 | - | |
|
| 0.9972 | 19650 | 0.0 | - | |
|
| 0.9997 | 19700 | 0.0 | - | |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 2.2.2 |
|
- spaCy: 3.6.1 |
|
- Transformers: 4.35.2 |
|
- PyTorch: 2.1.0+cu121 |
|
- Datasets: 2.16.1 |
|
- Tokenizers: 0.15.1 |
|
|
|
## 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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