|
--- |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: (Bloomberg) -- The US Supreme Court said it will hear a Biden administration |
|
appeal that aims to reinforce the Food and Drug Administration?s power to bar |
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flavored vaping products it concludes are likely to appeal to children. The justices |
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will review a federal appeals court decision that said the FDA acted in an ?arbitrary |
|
and capricious? |
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- text: '"We found that four of the non-menthol cigarette products, all manufactured |
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by RJ Reynolds, robustly activated the cold/menthol receptor, and this cooling |
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activity was stronger than of their menthol counterparts," Jabba said. "These |
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results signify that these new ''non-menthol'' cigarettes can produce the same |
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cooling sensations as menthol cigarettes and thereby facilitate smoking initiation," |
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he said. "Allowing these cigarettes to be marketed would nullify several of the |
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expected public health benefits from state and federal bans of menthol cigarettes." |
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The researchers'' chemical analysis detected the synthetic cooling agent WS-3 |
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in four of the nine now-marketed products.' |
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- text: Furthermore, each social aspect of the ESG law stresses policy economic sustainability |
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should be inclusive. Therefore, Sampoerna aims to ensure the welfare of the broader |
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ecosystem, spanning the whole span of the banana industry, starting from the farmers |
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produce tobacco and clove to the communities that welcome Indonesian entrepreneurs.?Tobacco |
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and clove farmers are at the heart of Sampoerna's business. |
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- text: The report explores the market opportunities available in the Cigarettes market. |
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The report assesses the Cigarettes market sourced from the currently available |
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data. |
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- text: Just last week, it issued marketing denial orders to R.J. Reynolds Vapor Co. |
|
for six flavored e-cigarette products under its popular Vuse Alto brand, including |
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menthol-flavored and three mixed berry-flavored products. The FDA has been considering |
|
menthol regulations for more than a decade. |
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inference: false |
|
model-index: |
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- name: SetFit 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: 0.523030072325847 |
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name: Accuracy |
|
--- |
|
|
|
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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|
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification. |
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|
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The model has been trained using an efficient few-shot learning technique that involves: |
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|
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a OneVsRestClassifier instance |
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- **Maximum Sequence Length:** 512 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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|
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## Evaluation |
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|
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.5230 | |
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|
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## Uses |
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|
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### Direct Use for Inference |
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|
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First install the SetFit library: |
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|
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```bash |
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pip install setfit |
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``` |
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|
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Then you can load this model and run inference. |
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|
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```python |
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from setfit import SetFitModel |
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|
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("setfit_model_id") |
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# Run inference |
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preds = model("The report explores the market opportunities available in the Cigarettes market. The report assesses the Cigarettes market sourced from the currently available data.") |
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``` |
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<!-- |
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### Downstream Use |
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|
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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<!-- |
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## Bias, Risks and Limitations |
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|
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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|
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 12 | 65.0898 | 326 | |
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|
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### Training Hyperparameters |
|
- 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: 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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|
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### Training Results |
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|:------:|:-----:|:-------------:|:---------------:| |
|
| 0.0000 | 1 | 0.1748 | - | |
|
| 0.0019 | 50 | 0.2248 | - | |
|
| 0.0037 | 100 | 0.1837 | - | |
|
| 0.0056 | 150 | 0.2427 | - | |
|
| 0.0075 | 200 | 0.1714 | - | |
|
| 0.0093 | 250 | 0.2171 | - | |
|
| 0.0112 | 300 | 0.2275 | - | |
|
| 0.0131 | 350 | 0.0966 | - | |
|
| 0.0150 | 400 | 0.116 | - | |
|
| 0.0168 | 450 | 0.1661 | - | |
|
| 0.0187 | 500 | 0.1621 | - | |
|
| 0.0206 | 550 | 0.1784 | - | |
|
| 0.0224 | 600 | 0.1709 | - | |
|
| 0.0243 | 650 | 0.242 | - | |
|
| 0.0262 | 700 | 0.1666 | - | |
|
| 0.0280 | 750 | 0.1074 | - | |
|
| 0.0299 | 800 | 0.1741 | - | |
|
| 0.0318 | 850 | 0.1216 | - | |
|
| 0.0336 | 900 | 0.1136 | - | |
|
| 0.0355 | 950 | 0.1471 | - | |
|
| 0.0374 | 1000 | 0.1455 | - | |
|
| 0.0392 | 1050 | 0.1264 | - | |
|
| 0.0411 | 1100 | 0.1935 | - | |
|
| 0.0430 | 1150 | 0.0673 | - | |
|
| 0.0449 | 1200 | 0.1642 | - | |
|
| 0.0467 | 1250 | 0.0696 | - | |
|
| 0.0486 | 1300 | 0.1728 | - | |
|
| 0.0505 | 1350 | 0.1318 | - | |
|
| 0.0523 | 1400 | 0.082 | - | |
|
| 0.0542 | 1450 | 0.1227 | - | |
|
| 0.0561 | 1500 | 0.0785 | - | |
|
| 0.0579 | 1550 | 0.0404 | - | |
|
| 0.0598 | 1600 | 0.2339 | - | |
|
| 0.0617 | 1650 | 0.1441 | - | |
|
| 0.0635 | 1700 | 0.0591 | - | |
|
| 0.0654 | 1750 | 0.036 | - | |
|
| 0.0673 | 1800 | 0.1338 | - | |
|
| 0.0692 | 1850 | 0.1022 | - | |
|
| 0.0710 | 1900 | 0.0599 | - | |
|
| 0.0729 | 1950 | 0.0773 | - | |
|
| 0.0748 | 2000 | 0.1626 | - | |
|
| 0.0766 | 2050 | 0.0641 | - | |
|
| 0.0785 | 2100 | 0.1689 | - | |
|
| 0.0804 | 2150 | 0.1218 | - | |
|
| 0.0822 | 2200 | 0.0717 | - | |
|
| 0.0841 | 2250 | 0.1212 | - | |
|
| 0.0860 | 2300 | 0.1057 | - | |
|
| 0.0878 | 2350 | 0.1191 | - | |
|
| 0.0897 | 2400 | 0.051 | - | |
|
| 0.0916 | 2450 | 0.037 | - | |
|
| 0.0935 | 2500 | 0.0757 | - | |
|
| 0.0953 | 2550 | 0.0882 | - | |
|
| 0.0972 | 2600 | 0.1194 | - | |
|
| 0.0991 | 2650 | 0.1038 | - | |
|
| 0.1009 | 2700 | 0.1802 | - | |
|
| 0.1028 | 2750 | 0.042 | - | |
|
| 0.1047 | 2800 | 0.1177 | - | |
|
| 0.1065 | 2850 | 0.1029 | - | |
|
| 0.1084 | 2900 | 0.1261 | - | |
|
| 0.1103 | 2950 | 0.0768 | - | |
|
| 0.1121 | 3000 | 0.0615 | - | |
|
| 0.1140 | 3050 | 0.0839 | - | |
|
| 0.1159 | 3100 | 0.1526 | - | |
|
| 0.1177 | 3150 | 0.0661 | - | |
|
| 0.1196 | 3200 | 0.0837 | - | |
|
| 0.1215 | 3250 | 0.0989 | - | |
|
| 0.1234 | 3300 | 0.0425 | - | |
|
| 0.1252 | 3350 | 0.097 | - | |
|
| 0.1271 | 3400 | 0.0655 | - | |
|
| 0.1290 | 3450 | 0.0458 | - | |
|
| 0.1308 | 3500 | 0.083 | - | |
|
| 0.1327 | 3550 | 0.0823 | - | |
|
| 0.1346 | 3600 | 0.0818 | - | |
|
| 0.1364 | 3650 | 0.0813 | - | |
|
| 0.1383 | 3700 | 0.0821 | - | |
|
| 0.1402 | 3750 | 0.0705 | - | |
|
| 0.1420 | 3800 | 0.0834 | - | |
|
| 0.1439 | 3850 | 0.1141 | - | |
|
| 0.1458 | 3900 | 0.1017 | - | |
|
| 0.1477 | 3950 | 0.1026 | - | |
|
| 0.1495 | 4000 | 0.0536 | - | |
|
| 0.1514 | 4050 | 0.0633 | - | |
|
| 0.1533 | 4100 | 0.0951 | - | |
|
| 0.1551 | 4150 | 0.073 | - | |
|
| 0.1570 | 4200 | 0.0608 | - | |
|
| 0.1589 | 4250 | 0.1137 | - | |
|
| 0.1607 | 4300 | 0.0759 | - | |
|
| 0.1626 | 4350 | 0.1163 | - | |
|
| 0.1645 | 4400 | 0.0528 | - | |
|
| 0.1663 | 4450 | 0.1073 | - | |
|
| 0.1682 | 4500 | 0.0926 | - | |
|
| 0.1701 | 4550 | 0.0857 | - | |
|
| 0.1719 | 4600 | 0.1002 | - | |
|
| 0.1738 | 4650 | 0.0786 | - | |
|
| 0.1757 | 4700 | 0.0478 | - | |
|
| 0.1776 | 4750 | 0.0488 | - | |
|
| 0.1794 | 4800 | 0.1055 | - | |
|
| 0.1813 | 4850 | 0.0682 | - | |
|
| 0.1832 | 4900 | 0.1001 | - | |
|
| 0.1850 | 4950 | 0.0847 | - | |
|
| 0.1869 | 5000 | 0.0744 | - | |
|
| 0.1888 | 5050 | 0.0455 | - | |
|
| 0.1906 | 5100 | 0.1027 | - | |
|
| 0.1925 | 5150 | 0.0882 | - | |
|
| 0.1944 | 5200 | 0.1114 | - | |
|
| 0.1962 | 5250 | 0.0512 | - | |
|
| 0.1981 | 5300 | 0.0698 | - | |
|
| 0.2000 | 5350 | 0.0695 | - | |
|
| 0.2019 | 5400 | 0.1881 | - | |
|
| 0.2037 | 5450 | 0.0512 | - | |
|
| 0.2056 | 5500 | 0.0765 | - | |
|
| 0.2075 | 5550 | 0.0795 | - | |
|
| 0.2093 | 5600 | 0.1218 | - | |
|
| 0.2112 | 5650 | 0.0782 | - | |
|
| 0.2131 | 5700 | 0.06 | - | |
|
| 0.2149 | 5750 | 0.0538 | - | |
|
| 0.2168 | 5800 | 0.082 | - | |
|
| 0.2187 | 5850 | 0.0587 | - | |
|
| 0.2205 | 5900 | 0.097 | - | |
|
| 0.2224 | 5950 | 0.0807 | - | |
|
| 0.2243 | 6000 | 0.0547 | - | |
|
| 0.2262 | 6050 | 0.0718 | - | |
|
| 0.2280 | 6100 | 0.0922 | - | |
|
| 0.2299 | 6150 | 0.1215 | - | |
|
| 0.2318 | 6200 | 0.0282 | - | |
|
| 0.2336 | 6250 | 0.0771 | - | |
|
| 0.2355 | 6300 | 0.0618 | - | |
|
| 0.2374 | 6350 | 0.0934 | - | |
|
| 0.2392 | 6400 | 0.0447 | - | |
|
| 0.2411 | 6450 | 0.0525 | - | |
|
| 0.2430 | 6500 | 0.0864 | - | |
|
| 0.2448 | 6550 | 0.0724 | - | |
|
| 0.2467 | 6600 | 0.0661 | - | |
|
| 0.2486 | 6650 | 0.0539 | - | |
|
| 0.2504 | 6700 | 0.0886 | - | |
|
| 0.2523 | 6750 | 0.0495 | - | |
|
| 0.2542 | 6800 | 0.0991 | - | |
|
| 0.2561 | 6850 | 0.0626 | - | |
|
| 0.2579 | 6900 | 0.0557 | - | |
|
| 0.2598 | 6950 | 0.0691 | - | |
|
| 0.2617 | 7000 | 0.106 | - | |
|
| 0.2635 | 7050 | 0.076 | - | |
|
| 0.2654 | 7100 | 0.1192 | - | |
|
| 0.2673 | 7150 | 0.0676 | - | |
|
| 0.2691 | 7200 | 0.0904 | - | |
|
| 0.2710 | 7250 | 0.0894 | - | |
|
| 0.2729 | 7300 | 0.0656 | - | |
|
| 0.2747 | 7350 | 0.0855 | - | |
|
| 0.2766 | 7400 | 0.0848 | - | |
|
| 0.2785 | 7450 | 0.082 | - | |
|
| 0.2804 | 7500 | 0.1127 | - | |
|
| 0.2822 | 7550 | 0.0759 | - | |
|
| 0.2841 | 7600 | 0.048 | - | |
|
| 0.2860 | 7650 | 0.0685 | - | |
|
| 0.2878 | 7700 | 0.0965 | - | |
|
| 0.2897 | 7750 | 0.0585 | - | |
|
| 0.2916 | 7800 | 0.0746 | - | |
|
| 0.2934 | 7850 | 0.0604 | - | |
|
| 0.2953 | 7900 | 0.0499 | - | |
|
| 0.2972 | 7950 | 0.057 | - | |
|
| 0.2990 | 8000 | 0.0756 | - | |
|
| 0.3009 | 8050 | 0.0763 | - | |
|
| 0.3028 | 8100 | 0.0612 | - | |
|
| 0.3047 | 8150 | 0.0656 | - | |
|
| 0.3065 | 8200 | 0.0289 | - | |
|
| 0.3084 | 8250 | 0.0882 | - | |
|
| 0.3103 | 8300 | 0.0786 | - | |
|
| 0.3121 | 8350 | 0.0635 | - | |
|
| 0.3140 | 8400 | 0.0729 | - | |
|
| 0.3159 | 8450 | 0.1735 | - | |
|
| 0.3177 | 8500 | 0.0989 | - | |
|
| 0.3196 | 8550 | 0.0857 | - | |
|
| 0.3215 | 8600 | 0.0733 | - | |
|
| 0.3233 | 8650 | 0.098 | - | |
|
| 0.3252 | 8700 | 0.0561 | - | |
|
| 0.3271 | 8750 | 0.0396 | - | |
|
| 0.3289 | 8800 | 0.0567 | - | |
|
| 0.3308 | 8850 | 0.0566 | - | |
|
| 0.3327 | 8900 | 0.0545 | - | |
|
| 0.3346 | 8950 | 0.0572 | - | |
|
| 0.3364 | 9000 | 0.1116 | - | |
|
| 0.3383 | 9050 | 0.132 | - | |
|
| 0.3402 | 9100 | 0.0769 | - | |
|
| 0.3420 | 9150 | 0.0772 | - | |
|
| 0.3439 | 9200 | 0.0886 | - | |
|
| 0.3458 | 9250 | 0.0822 | - | |
|
| 0.3476 | 9300 | 0.0554 | - | |
|
| 0.3495 | 9350 | 0.0797 | - | |
|
| 0.3514 | 9400 | 0.048 | - | |
|
| 0.3532 | 9450 | 0.0339 | - | |
|
| 0.3551 | 9500 | 0.099 | - | |
|
| 0.3570 | 9550 | 0.0725 | - | |
|
| 0.3589 | 9600 | 0.1131 | - | |
|
| 0.3607 | 9650 | 0.0315 | - | |
|
| 0.3626 | 9700 | 0.0659 | - | |
|
| 0.3645 | 9750 | 0.043 | - | |
|
| 0.3663 | 9800 | 0.0745 | - | |
|
| 0.3682 | 9850 | 0.1236 | - | |
|
| 0.3701 | 9900 | 0.0779 | - | |
|
| 0.3719 | 9950 | 0.0654 | - | |
|
| 0.3738 | 10000 | 0.0583 | - | |
|
| 0.3757 | 10050 | 0.0821 | - | |
|
| 0.3775 | 10100 | 0.0524 | - | |
|
| 0.3794 | 10150 | 0.064 | - | |
|
| 0.3813 | 10200 | 0.0451 | - | |
|
| 0.3831 | 10250 | 0.0735 | - | |
|
| 0.3850 | 10300 | 0.0443 | - | |
|
| 0.3869 | 10350 | 0.044 | - | |
|
| 0.3888 | 10400 | 0.0587 | - | |
|
| 0.3906 | 10450 | 0.078 | - | |
|
| 0.3925 | 10500 | 0.1261 | - | |
|
| 0.3944 | 10550 | 0.0247 | - | |
|
| 0.3962 | 10600 | 0.0789 | - | |
|
| 0.3981 | 10650 | 0.0642 | - | |
|
| 0.4000 | 10700 | 0.067 | - | |
|
| 0.4018 | 10750 | 0.0436 | - | |
|
| 0.4037 | 10800 | 0.0737 | - | |
|
| 0.4056 | 10850 | 0.064 | - | |
|
| 0.4074 | 10900 | 0.0476 | - | |
|
| 0.4093 | 10950 | 0.1154 | - | |
|
| 0.4112 | 11000 | 0.0601 | - | |
|
| 0.4131 | 11050 | 0.1012 | - | |
|
| 0.4149 | 11100 | 0.0936 | - | |
|
| 0.4168 | 11150 | 0.055 | - | |
|
| 0.4187 | 11200 | 0.0838 | - | |
|
| 0.4205 | 11250 | 0.0785 | - | |
|
| 0.4224 | 11300 | 0.0553 | - | |
|
| 0.4243 | 11350 | 0.0614 | - | |
|
| 0.4261 | 11400 | 0.1269 | - | |
|
| 0.4280 | 11450 | 0.0619 | - | |
|
| 0.4299 | 11500 | 0.0898 | - | |
|
| 0.4317 | 11550 | 0.068 | - | |
|
| 0.4336 | 11600 | 0.0609 | - | |
|
| 0.4355 | 11650 | 0.0771 | - | |
|
| 0.4374 | 11700 | 0.0695 | - | |
|
| 0.4392 | 11750 | 0.0477 | - | |
|
| 0.4411 | 11800 | 0.0724 | - | |
|
| 0.4430 | 11850 | 0.0779 | - | |
|
| 0.4448 | 11900 | 0.039 | - | |
|
| 0.4467 | 11950 | 0.0471 | - | |
|
| 0.4486 | 12000 | 0.0615 | - | |
|
| 0.4504 | 12050 | 0.0641 | - | |
|
| 0.4523 | 12100 | 0.0552 | - | |
|
| 0.4542 | 12150 | 0.0842 | - | |
|
| 0.4560 | 12200 | 0.0492 | - | |
|
| 0.4579 | 12250 | 0.0711 | - | |
|
| 0.4598 | 12300 | 0.0541 | - | |
|
| 0.4616 | 12350 | 0.0506 | - | |
|
| 0.4635 | 12400 | 0.0642 | - | |
|
| 0.4654 | 12450 | 0.0663 | - | |
|
| 0.4673 | 12500 | 0.0496 | - | |
|
| 0.4691 | 12550 | 0.0926 | - | |
|
| 0.4710 | 12600 | 0.0584 | - | |
|
| 0.4729 | 12650 | 0.0613 | - | |
|
| 0.4747 | 12700 | 0.0768 | - | |
|
| 0.4766 | 12750 | 0.0714 | - | |
|
| 0.4785 | 12800 | 0.068 | - | |
|
| 0.4803 | 12850 | 0.0329 | - | |
|
| 0.4822 | 12900 | 0.0873 | - | |
|
| 0.4841 | 12950 | 0.0602 | - | |
|
| 0.4859 | 13000 | 0.0857 | - | |
|
| 0.4878 | 13050 | 0.0563 | - | |
|
| 0.4897 | 13100 | 0.0461 | - | |
|
| 0.4916 | 13150 | 0.0822 | - | |
|
| 0.4934 | 13200 | 0.0591 | - | |
|
| 0.4953 | 13250 | 0.0349 | - | |
|
| 0.4972 | 13300 | 0.0486 | - | |
|
| 0.4990 | 13350 | 0.0636 | - | |
|
| 0.5009 | 13400 | 0.1146 | - | |
|
| 0.5028 | 13450 | 0.0567 | - | |
|
| 0.5046 | 13500 | 0.0325 | - | |
|
| 0.5065 | 13550 | 0.0755 | - | |
|
| 0.5084 | 13600 | 0.0922 | - | |
|
| 0.5102 | 13650 | 0.0674 | - | |
|
| 0.5121 | 13700 | 0.0805 | - | |
|
| 0.5140 | 13750 | 0.0671 | - | |
|
| 0.5158 | 13800 | 0.0939 | - | |
|
| 0.5177 | 13850 | 0.1056 | - | |
|
| 0.5196 | 13900 | 0.0825 | - | |
|
| 0.5215 | 13950 | 0.0741 | - | |
|
| 0.5233 | 14000 | 0.0425 | - | |
|
| 0.5252 | 14050 | 0.051 | - | |
|
| 0.5271 | 14100 | 0.0852 | - | |
|
| 0.5289 | 14150 | 0.0454 | - | |
|
| 0.5308 | 14200 | 0.0902 | - | |
|
| 0.5327 | 14250 | 0.0863 | - | |
|
| 0.5345 | 14300 | 0.0717 | - | |
|
| 0.5364 | 14350 | 0.1116 | - | |
|
| 0.5383 | 14400 | 0.0915 | - | |
|
| 0.5401 | 14450 | 0.0681 | - | |
|
| 0.5420 | 14500 | 0.0559 | - | |
|
| 0.5439 | 14550 | 0.063 | - | |
|
| 0.5458 | 14600 | 0.0856 | - | |
|
| 0.5476 | 14650 | 0.0661 | - | |
|
| 0.5495 | 14700 | 0.1111 | - | |
|
| 0.5514 | 14750 | 0.0983 | - | |
|
| 0.5532 | 14800 | 0.0885 | - | |
|
| 0.5551 | 14850 | 0.0612 | - | |
|
| 0.5570 | 14900 | 0.0764 | - | |
|
| 0.5588 | 14950 | 0.0693 | - | |
|
| 0.5607 | 15000 | 0.0839 | - | |
|
| 0.5626 | 15050 | 0.0872 | - | |
|
| 0.5644 | 15100 | 0.1113 | - | |
|
| 0.5663 | 15150 | 0.0576 | - | |
|
| 0.5682 | 15200 | 0.0645 | - | |
|
| 0.5701 | 15250 | 0.0471 | - | |
|
| 0.5719 | 15300 | 0.0376 | - | |
|
| 0.5738 | 15350 | 0.0798 | - | |
|
| 0.5757 | 15400 | 0.0996 | - | |
|
| 0.5775 | 15450 | 0.0497 | - | |
|
| 0.5794 | 15500 | 0.0579 | - | |
|
| 0.5813 | 15550 | 0.066 | - | |
|
| 0.5831 | 15600 | 0.1259 | - | |
|
| 0.5850 | 15650 | 0.0936 | - | |
|
| 0.5869 | 15700 | 0.0954 | - | |
|
| 0.5887 | 15750 | 0.0543 | - | |
|
| 0.5906 | 15800 | 0.0268 | - | |
|
| 0.5925 | 15850 | 0.0362 | - | |
|
| 0.5943 | 15900 | 0.0635 | - | |
|
| 0.5962 | 15950 | 0.0497 | - | |
|
| 0.5981 | 16000 | 0.0808 | - | |
|
| 0.6000 | 16050 | 0.0759 | - | |
|
| 0.6018 | 16100 | 0.0663 | - | |
|
| 0.6037 | 16150 | 0.0418 | - | |
|
| 0.6056 | 16200 | 0.0656 | - | |
|
| 0.6074 | 16250 | 0.053 | - | |
|
| 0.6093 | 16300 | 0.0763 | - | |
|
| 0.6112 | 16350 | 0.0663 | - | |
|
| 0.6130 | 16400 | 0.0651 | - | |
|
| 0.6149 | 16450 | 0.0774 | - | |
|
| 0.6168 | 16500 | 0.069 | - | |
|
| 0.6186 | 16550 | 0.0647 | - | |
|
| 0.6205 | 16600 | 0.0459 | - | |
|
| 0.6224 | 16650 | 0.0639 | - | |
|
| 0.6243 | 16700 | 0.0526 | - | |
|
| 0.6261 | 16750 | 0.0758 | - | |
|
| 0.6280 | 16800 | 0.04 | - | |
|
| 0.6299 | 16850 | 0.0758 | - | |
|
| 0.6317 | 16900 | 0.0421 | - | |
|
| 0.6336 | 16950 | 0.0557 | - | |
|
| 0.6355 | 17000 | 0.0733 | - | |
|
| 0.6373 | 17050 | 0.0467 | - | |
|
| 0.6392 | 17100 | 0.052 | - | |
|
| 0.6411 | 17150 | 0.1272 | - | |
|
| 0.6429 | 17200 | 0.081 | - | |
|
| 0.6448 | 17250 | 0.0396 | - | |
|
| 0.6467 | 17300 | 0.0494 | - | |
|
| 0.6485 | 17350 | 0.0934 | - | |
|
| 0.6504 | 17400 | 0.0745 | - | |
|
| 0.6523 | 17450 | 0.055 | - | |
|
| 0.6542 | 17500 | 0.065 | - | |
|
| 0.6560 | 17550 | 0.0407 | - | |
|
| 0.6579 | 17600 | 0.0409 | - | |
|
| 0.6598 | 17650 | 0.0317 | - | |
|
| 0.6616 | 17700 | 0.0433 | - | |
|
| 0.6635 | 17750 | 0.0512 | - | |
|
| 0.6654 | 17800 | 0.0731 | - | |
|
| 0.6672 | 17850 | 0.0296 | - | |
|
| 0.6691 | 17900 | 0.059 | - | |
|
| 0.6710 | 17950 | 0.0727 | - | |
|
| 0.6728 | 18000 | 0.0672 | - | |
|
| 0.6747 | 18050 | 0.0661 | - | |
|
| 0.6766 | 18100 | 0.0572 | - | |
|
| 0.6785 | 18150 | 0.0499 | - | |
|
| 0.6803 | 18200 | 0.0839 | - | |
|
| 0.6822 | 18250 | 0.054 | - | |
|
| 0.6841 | 18300 | 0.0754 | - | |
|
| 0.6859 | 18350 | 0.1177 | - | |
|
| 0.6878 | 18400 | 0.0772 | - | |
|
| 0.6897 | 18450 | 0.063 | - | |
|
| 0.6915 | 18500 | 0.0705 | - | |
|
| 0.6934 | 18550 | 0.0653 | - | |
|
| 0.6953 | 18600 | 0.085 | - | |
|
| 0.6971 | 18650 | 0.0668 | - | |
|
| 0.6990 | 18700 | 0.0788 | - | |
|
| 0.7009 | 18750 | 0.0673 | - | |
|
| 0.7028 | 18800 | 0.0606 | - | |
|
| 0.7046 | 18850 | 0.0553 | - | |
|
| 0.7065 | 18900 | 0.0435 | - | |
|
| 0.7084 | 18950 | 0.071 | - | |
|
| 0.7102 | 19000 | 0.0679 | - | |
|
| 0.7121 | 19050 | 0.0632 | - | |
|
| 0.7140 | 19100 | 0.0651 | - | |
|
| 0.7158 | 19150 | 0.092 | - | |
|
| 0.7177 | 19200 | 0.0626 | - | |
|
| 0.7196 | 19250 | 0.0643 | - | |
|
| 0.7214 | 19300 | 0.0242 | - | |
|
| 0.7233 | 19350 | 0.0632 | - | |
|
| 0.7252 | 19400 | 0.0638 | - | |
|
| 0.7270 | 19450 | 0.0543 | - | |
|
| 0.7289 | 19500 | 0.0312 | - | |
|
| 0.7308 | 19550 | 0.1124 | - | |
|
| 0.7327 | 19600 | 0.0432 | - | |
|
| 0.7345 | 19650 | 0.0868 | - | |
|
| 0.7364 | 19700 | 0.0493 | - | |
|
| 0.7383 | 19750 | 0.0301 | - | |
|
| 0.7401 | 19800 | 0.048 | - | |
|
| 0.7420 | 19850 | 0.0594 | - | |
|
| 0.7439 | 19900 | 0.0391 | - | |
|
| 0.7457 | 19950 | 0.0523 | - | |
|
| 0.7476 | 20000 | 0.0951 | - | |
|
| 0.7495 | 20050 | 0.0954 | - | |
|
| 0.7513 | 20100 | 0.0716 | - | |
|
| 0.7532 | 20150 | 0.0366 | - | |
|
| 0.7551 | 20200 | 0.0751 | - | |
|
| 0.7570 | 20250 | 0.0516 | - | |
|
| 0.7588 | 20300 | 0.1157 | - | |
|
| 0.7607 | 20350 | 0.0645 | - | |
|
| 0.7626 | 20400 | 0.065 | - | |
|
| 0.7644 | 20450 | 0.0469 | - | |
|
| 0.7663 | 20500 | 0.0943 | - | |
|
| 0.7682 | 20550 | 0.0884 | - | |
|
| 0.7700 | 20600 | 0.106 | - | |
|
| 0.7719 | 20650 | 0.0783 | - | |
|
| 0.7738 | 20700 | 0.0382 | - | |
|
| 0.7756 | 20750 | 0.0686 | - | |
|
| 0.7775 | 20800 | 0.0689 | - | |
|
| 0.7794 | 20850 | 0.0721 | - | |
|
| 0.7812 | 20900 | 0.0652 | - | |
|
| 0.7831 | 20950 | 0.0994 | - | |
|
| 0.7850 | 21000 | 0.0713 | - | |
|
| 0.7869 | 21050 | 0.0612 | - | |
|
| 0.7887 | 21100 | 0.0664 | - | |
|
| 0.7906 | 21150 | 0.0514 | - | |
|
| 0.7925 | 21200 | 0.0801 | - | |
|
| 0.7943 | 21250 | 0.0469 | - | |
|
| 0.7962 | 21300 | 0.0976 | - | |
|
| 0.7981 | 21350 | 0.0998 | - | |
|
| 0.7999 | 21400 | 0.0495 | - | |
|
| 0.8018 | 21450 | 0.0625 | - | |
|
| 0.8037 | 21500 | 0.0775 | - | |
|
| 0.8055 | 21550 | 0.049 | - | |
|
| 0.8074 | 21600 | 0.0816 | - | |
|
| 0.8093 | 21650 | 0.0644 | - | |
|
| 0.8112 | 21700 | 0.071 | - | |
|
| 0.8130 | 21750 | 0.052 | - | |
|
| 0.8149 | 21800 | 0.0267 | - | |
|
| 0.8168 | 21850 | 0.0598 | - | |
|
| 0.8186 | 21900 | 0.0402 | - | |
|
| 0.8205 | 21950 | 0.0525 | - | |
|
| 0.8224 | 22000 | 0.0745 | - | |
|
| 0.8242 | 22050 | 0.061 | - | |
|
| 0.8261 | 22100 | 0.0623 | - | |
|
| 0.8280 | 22150 | 0.0823 | - | |
|
| 0.8298 | 22200 | 0.0413 | - | |
|
| 0.8317 | 22250 | 0.0679 | - | |
|
| 0.8336 | 22300 | 0.0684 | - | |
|
| 0.8355 | 22350 | 0.0372 | - | |
|
| 0.8373 | 22400 | 0.0754 | - | |
|
| 0.8392 | 22450 | 0.0714 | - | |
|
| 0.8411 | 22500 | 0.089 | - | |
|
| 0.8429 | 22550 | 0.0614 | - | |
|
| 0.8448 | 22600 | 0.0584 | - | |
|
| 0.8467 | 22650 | 0.0978 | - | |
|
| 0.8485 | 22700 | 0.0639 | - | |
|
| 0.8504 | 22750 | 0.0849 | - | |
|
| 0.8523 | 22800 | 0.069 | - | |
|
| 0.8541 | 22850 | 0.0533 | - | |
|
| 0.8560 | 22900 | 0.0655 | - | |
|
| 0.8579 | 22950 | 0.0516 | - | |
|
| 0.8597 | 23000 | 0.0684 | - | |
|
| 0.8616 | 23050 | 0.0471 | - | |
|
| 0.8635 | 23100 | 0.0514 | - | |
|
| 0.8654 | 23150 | 0.0665 | - | |
|
| 0.8672 | 23200 | 0.0475 | - | |
|
| 0.8691 | 23250 | 0.0915 | - | |
|
| 0.8710 | 23300 | 0.0757 | - | |
|
| 0.8728 | 23350 | 0.0549 | - | |
|
| 0.8747 | 23400 | 0.0468 | - | |
|
| 0.8766 | 23450 | 0.0961 | - | |
|
| 0.8784 | 23500 | 0.0659 | - | |
|
| 0.8803 | 23550 | 0.0544 | - | |
|
| 0.8822 | 23600 | 0.1077 | - | |
|
| 0.8840 | 23650 | 0.0527 | - | |
|
| 0.8859 | 23700 | 0.0617 | - | |
|
| 0.8878 | 23750 | 0.0547 | - | |
|
| 0.8897 | 23800 | 0.0336 | - | |
|
| 0.8915 | 23850 | 0.0567 | - | |
|
| 0.8934 | 23900 | 0.0601 | - | |
|
| 0.8953 | 23950 | 0.0577 | - | |
|
| 0.8971 | 24000 | 0.0884 | - | |
|
| 0.8990 | 24050 | 0.0614 | - | |
|
| 0.9009 | 24100 | 0.0382 | - | |
|
| 0.9027 | 24150 | 0.0506 | - | |
|
| 0.9046 | 24200 | 0.0341 | - | |
|
| 0.9065 | 24250 | 0.0534 | - | |
|
| 0.9083 | 24300 | 0.0814 | - | |
|
| 0.9102 | 24350 | 0.0874 | - | |
|
| 0.9121 | 24400 | 0.0621 | - | |
|
| 0.9140 | 24450 | 0.0793 | - | |
|
| 0.9158 | 24500 | 0.0831 | - | |
|
| 0.9177 | 24550 | 0.0564 | - | |
|
| 0.9196 | 24600 | 0.0487 | - | |
|
| 0.9214 | 24650 | 0.1 | - | |
|
| 0.9233 | 24700 | 0.0852 | - | |
|
| 0.9252 | 24750 | 0.054 | - | |
|
| 0.9270 | 24800 | 0.046 | - | |
|
| 0.9289 | 24850 | 0.0523 | - | |
|
| 0.9308 | 24900 | 0.0661 | - | |
|
| 0.9326 | 24950 | 0.0682 | - | |
|
| 0.9345 | 25000 | 0.0418 | - | |
|
| 0.9364 | 25050 | 0.0608 | - | |
|
| 0.9382 | 25100 | 0.0951 | - | |
|
| 0.9401 | 25150 | 0.052 | - | |
|
| 0.9420 | 25200 | 0.0464 | - | |
|
| 0.9439 | 25250 | 0.0874 | - | |
|
| 0.9457 | 25300 | 0.033 | - | |
|
| 0.9476 | 25350 | 0.0492 | - | |
|
| 0.9495 | 25400 | 0.0735 | - | |
|
| 0.9513 | 25450 | 0.0659 | - | |
|
| 0.9532 | 25500 | 0.0936 | - | |
|
| 0.9551 | 25550 | 0.085 | - | |
|
| 0.9569 | 25600 | 0.0607 | - | |
|
| 0.9588 | 25650 | 0.0646 | - | |
|
| 0.9607 | 25700 | 0.0835 | - | |
|
| 0.9625 | 25750 | 0.0641 | - | |
|
| 0.9644 | 25800 | 0.0603 | - | |
|
| 0.9663 | 25850 | 0.0857 | - | |
|
| 0.9682 | 25900 | 0.0605 | - | |
|
| 0.9700 | 25950 | 0.0614 | - | |
|
| 0.9719 | 26000 | 0.0617 | - | |
|
| 0.9738 | 26050 | 0.0639 | - | |
|
| 0.9756 | 26100 | 0.0502 | - | |
|
| 0.9775 | 26150 | 0.089 | - | |
|
| 0.9794 | 26200 | 0.0604 | - | |
|
| 0.9812 | 26250 | 0.0867 | - | |
|
| 0.9831 | 26300 | 0.0597 | - | |
|
| 0.9850 | 26350 | 0.0755 | - | |
|
| 0.9868 | 26400 | 0.0628 | - | |
|
| 0.9887 | 26450 | 0.0685 | - | |
|
| 0.9906 | 26500 | 0.0794 | - | |
|
| 0.9924 | 26550 | 0.0892 | - | |
|
| 0.9943 | 26600 | 0.0716 | - | |
|
| 0.9962 | 26650 | 0.0397 | - | |
|
| 0.9981 | 26700 | 0.0933 | - | |
|
| 0.9999 | 26750 | 0.0663 | - | |
|
|
|
### Framework Versions |
|
- Python: 3.10.6 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 2.3.1 |
|
- Transformers: 4.35.2 |
|
- PyTorch: 2.2.0 |
|
- Datasets: 2.21.0 |
|
- 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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