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--- |
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library_name: setfit |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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datasets: |
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- ethos |
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metrics: |
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- accuracy |
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widget: |
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- text: "This is what happens when you'\x80\x99re a bitch and a cunt. Looks like\ |
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\ that guy called it correctly. Only a bitch and a cunt would think she could\ |
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\ lay hands on another with no repercussions. Maybe after that seizure she won'\x80\ |
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\x99t be as much of a bitchy cunt." |
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- text: What if we send every men to mars to start a new life there! They are useless |
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here (Of course milk them first to store their juices) |
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- text: Honestly, go fuck yourself! bitch! |
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- text: Hindus take my ass please |
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- text: Im going to choke you with your cross necklace idiotic religious pig |
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pipeline_tag: text-classification |
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inference: false |
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base_model: lighteternal/stsb-xlm-r-greek-transfer |
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model-index: |
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- name: SetFit with lighteternal/stsb-xlm-r-greek-transfer |
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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: ethos |
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type: ethos |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.208 |
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name: Accuracy |
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--- |
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# SetFit with lighteternal/stsb-xlm-r-greek-transfer |
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This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [ethos](https://huggingface.co/datasets/ethos) dataset that can be used for Text Classification. This SetFit model uses [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) as the Sentence Transformer embedding model. A ClassifierChain instance is used for classification. |
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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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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) |
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- **Classification head:** a ClassifierChain instance |
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- **Maximum Sequence Length:** 400 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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- **Training Dataset:** [ethos](https://huggingface.co/datasets/ethos) |
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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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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.208 | |
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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 SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("st-karlos-efood/setfit-multilabel-example-classifier-chain") |
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# Run inference |
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preds = model("Hindus take my ass please") |
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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 | 3 | 9.9307 | 61 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (10, 10) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 10 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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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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| 0.0006 | 1 | 0.2027 | - | |
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| 0.0305 | 50 | 0.2092 | - | |
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| 0.0609 | 100 | 0.1605 | - | |
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| 0.0914 | 150 | 0.1726 | - | |
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| 0.1219 | 200 | 0.1322 | - | |
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| 0.1523 | 250 | 0.1252 | - | |
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| 0.1828 | 300 | 0.1404 | - | |
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| 0.2133 | 350 | 0.0927 | - | |
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| 0.2438 | 400 | 0.1039 | - | |
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| 0.2742 | 450 | 0.0904 | - | |
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| 0.3047 | 500 | 0.1194 | - | |
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| 0.3352 | 550 | 0.1024 | - | |
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| 0.3656 | 600 | 0.151 | - | |
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| 0.3961 | 650 | 0.0842 | - | |
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| 0.4266 | 700 | 0.1158 | - | |
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| 0.4570 | 750 | 0.214 | - | |
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| 0.4875 | 800 | 0.1167 | - | |
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| 0.5180 | 850 | 0.1174 | - | |
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| 0.5484 | 900 | 0.1567 | - | |
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| 0.5789 | 950 | 0.0726 | - | |
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| 0.6094 | 1000 | 0.0741 | - | |
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| 0.6399 | 1050 | 0.0841 | - | |
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| 0.6703 | 1100 | 0.0606 | - | |
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| 0.7008 | 1150 | 0.1005 | - | |
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| 0.7313 | 1200 | 0.1236 | - | |
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| 0.7617 | 1250 | 0.141 | - | |
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| 0.7922 | 1300 | 0.1611 | - | |
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| 0.8227 | 1350 | 0.1068 | - | |
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| 0.8531 | 1400 | 0.0542 | - | |
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| 0.8836 | 1450 | 0.1635 | - | |
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| 0.9141 | 1500 | 0.106 | - | |
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| 0.9445 | 1550 | 0.0817 | - | |
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| 0.9750 | 1600 | 0.1157 | - | |
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| 1.0055 | 1650 | 0.1031 | - | |
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| 1.0360 | 1700 | 0.0969 | - | |
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| 1.0664 | 1750 | 0.0742 | - | |
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| 1.0969 | 1800 | 0.0697 | - | |
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| 1.1274 | 1850 | 0.1072 | - | |
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| 1.1578 | 1900 | 0.0593 | - | |
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| 1.1883 | 1950 | 0.1102 | - | |
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| 1.2188 | 2000 | 0.1586 | - | |
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| 1.2492 | 2050 | 0.1523 | - | |
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| 1.2797 | 2100 | 0.0921 | - | |
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| 1.3102 | 2150 | 0.0634 | - | |
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| 1.3406 | 2200 | 0.073 | - | |
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| 1.3711 | 2250 | 0.1131 | - | |
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| 1.4016 | 2300 | 0.0493 | - | |
|
| 1.4321 | 2350 | 0.106 | - | |
|
| 1.4625 | 2400 | 0.0585 | - | |
|
| 1.4930 | 2450 | 0.1058 | - | |
|
| 1.5235 | 2500 | 0.0892 | - | |
|
| 1.5539 | 2550 | 0.0649 | - | |
|
| 1.5844 | 2600 | 0.0481 | - | |
|
| 1.6149 | 2650 | 0.1359 | - | |
|
| 1.6453 | 2700 | 0.0734 | - | |
|
| 1.6758 | 2750 | 0.0762 | - | |
|
| 1.7063 | 2800 | 0.1082 | - | |
|
| 1.7367 | 2850 | 0.1274 | - | |
|
| 1.7672 | 2900 | 0.0724 | - | |
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| 1.7977 | 2950 | 0.0842 | - | |
|
| 1.8282 | 3000 | 0.1558 | - | |
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| 1.8586 | 3050 | 0.071 | - | |
|
| 1.8891 | 3100 | 0.1716 | - | |
|
| 1.9196 | 3150 | 0.1078 | - | |
|
| 1.9500 | 3200 | 0.1037 | - | |
|
| 1.9805 | 3250 | 0.0773 | - | |
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| 2.0110 | 3300 | 0.0706 | - | |
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| 2.0414 | 3350 | 0.1577 | - | |
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| 2.0719 | 3400 | 0.0825 | - | |
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| 2.1024 | 3450 | 0.1227 | - | |
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| 2.1328 | 3500 | 0.1069 | - | |
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| 2.1633 | 3550 | 0.1037 | - | |
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| 2.1938 | 3600 | 0.0595 | - | |
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| 2.2243 | 3650 | 0.0569 | - | |
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| 2.2547 | 3700 | 0.0967 | - | |
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| 2.2852 | 3750 | 0.0632 | - | |
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| 2.3157 | 3800 | 0.1014 | - | |
|
| 2.3461 | 3850 | 0.0868 | - | |
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| 2.3766 | 3900 | 0.0986 | - | |
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| 2.4071 | 3950 | 0.0585 | - | |
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| 2.4375 | 4000 | 0.063 | - | |
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| 2.4680 | 4050 | 0.1124 | - | |
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| 2.4985 | 4100 | 0.0444 | - | |
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| 2.5289 | 4150 | 0.1547 | - | |
|
| 2.5594 | 4200 | 0.1087 | - | |
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| 2.5899 | 4250 | 0.0946 | - | |
|
| 2.6204 | 4300 | 0.0261 | - | |
|
| 2.6508 | 4350 | 0.0414 | - | |
|
| 2.6813 | 4400 | 0.0715 | - | |
|
| 2.7118 | 4450 | 0.0831 | - | |
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| 2.7422 | 4500 | 0.0779 | - | |
|
| 2.7727 | 4550 | 0.1049 | - | |
|
| 2.8032 | 4600 | 0.1224 | - | |
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| 2.8336 | 4650 | 0.0926 | - | |
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| 2.8641 | 4700 | 0.0745 | - | |
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| 2.8946 | 4750 | 0.0642 | - | |
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| 2.9250 | 4800 | 0.0536 | - | |
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| 2.9555 | 4850 | 0.1296 | - | |
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| 2.9860 | 4900 | 0.0596 | - | |
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| 3.0165 | 4950 | 0.0361 | - | |
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| 3.0469 | 5000 | 0.0592 | - | |
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| 3.0774 | 5050 | 0.0656 | - | |
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| 3.1079 | 5100 | 0.0584 | - | |
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| 3.1383 | 5150 | 0.0729 | - | |
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| 3.1688 | 5200 | 0.1037 | - | |
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| 3.1993 | 5250 | 0.0685 | - | |
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| 3.2297 | 5300 | 0.0511 | - | |
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| 3.2602 | 5350 | 0.0427 | - | |
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| 3.2907 | 5400 | 0.1067 | - | |
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| 3.3211 | 5450 | 0.0807 | - | |
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| 3.3516 | 5500 | 0.0815 | - | |
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| 3.3821 | 5550 | 0.1016 | - | |
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| 3.4126 | 5600 | 0.1034 | - | |
|
| 3.4430 | 5650 | 0.1257 | - | |
|
| 3.4735 | 5700 | 0.0877 | - | |
|
| 3.5040 | 5750 | 0.0808 | - | |
|
| 3.5344 | 5800 | 0.0926 | - | |
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| 3.5649 | 5850 | 0.0967 | - | |
|
| 3.5954 | 5900 | 0.0401 | - | |
|
| 3.6258 | 5950 | 0.0547 | - | |
|
| 3.6563 | 6000 | 0.0872 | - | |
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| 3.6868 | 6050 | 0.0808 | - | |
|
| 3.7172 | 6100 | 0.1125 | - | |
|
| 3.7477 | 6150 | 0.1431 | - | |
|
| 3.7782 | 6200 | 0.1039 | - | |
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| 3.8087 | 6250 | 0.061 | - | |
|
| 3.8391 | 6300 | 0.1022 | - | |
|
| 3.8696 | 6350 | 0.0394 | - | |
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| 3.9001 | 6400 | 0.0892 | - | |
|
| 3.9305 | 6450 | 0.0535 | - | |
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| 3.9610 | 6500 | 0.0793 | - | |
|
| 3.9915 | 6550 | 0.0462 | - | |
|
| 4.0219 | 6600 | 0.0686 | - | |
|
| 4.0524 | 6650 | 0.0506 | - | |
|
| 4.0829 | 6700 | 0.1012 | - | |
|
| 4.1133 | 6750 | 0.0852 | - | |
|
| 4.1438 | 6800 | 0.0729 | - | |
|
| 4.1743 | 6850 | 0.1007 | - | |
|
| 4.2048 | 6900 | 0.0431 | - | |
|
| 4.2352 | 6950 | 0.0683 | - | |
|
| 4.2657 | 7000 | 0.0712 | - | |
|
| 4.2962 | 7050 | 0.0732 | - | |
|
| 4.3266 | 7100 | 0.0374 | - | |
|
| 4.3571 | 7150 | 0.1015 | - | |
|
| 4.3876 | 7200 | 0.15 | - | |
|
| 4.4180 | 7250 | 0.0852 | - | |
|
| 4.4485 | 7300 | 0.0714 | - | |
|
| 4.4790 | 7350 | 0.0587 | - | |
|
| 4.5094 | 7400 | 0.1335 | - | |
|
| 4.5399 | 7450 | 0.1123 | - | |
|
| 4.5704 | 7500 | 0.0538 | - | |
|
| 4.6009 | 7550 | 0.0989 | - | |
|
| 4.6313 | 7600 | 0.0878 | - | |
|
| 4.6618 | 7650 | 0.0963 | - | |
|
| 4.6923 | 7700 | 0.0991 | - | |
|
| 4.7227 | 7750 | 0.0776 | - | |
|
| 4.7532 | 7800 | 0.0663 | - | |
|
| 4.7837 | 7850 | 0.0696 | - | |
|
| 4.8141 | 7900 | 0.0704 | - | |
|
| 4.8446 | 7950 | 0.0626 | - | |
|
| 4.8751 | 8000 | 0.0657 | - | |
|
| 4.9055 | 8050 | 0.0567 | - | |
|
| 4.9360 | 8100 | 0.0619 | - | |
|
| 4.9665 | 8150 | 0.0792 | - | |
|
| 4.9970 | 8200 | 0.0671 | - | |
|
| 5.0274 | 8250 | 0.1068 | - | |
|
| 5.0579 | 8300 | 0.1111 | - | |
|
| 5.0884 | 8350 | 0.0968 | - | |
|
| 5.1188 | 8400 | 0.0577 | - | |
|
| 5.1493 | 8450 | 0.0934 | - | |
|
| 5.1798 | 8500 | 0.0854 | - | |
|
| 5.2102 | 8550 | 0.0587 | - | |
|
| 5.2407 | 8600 | 0.048 | - | |
|
| 5.2712 | 8650 | 0.0829 | - | |
|
| 5.3016 | 8700 | 0.0985 | - | |
|
| 5.3321 | 8750 | 0.107 | - | |
|
| 5.3626 | 8800 | 0.0662 | - | |
|
| 5.3931 | 8850 | 0.0799 | - | |
|
| 5.4235 | 8900 | 0.0948 | - | |
|
| 5.4540 | 8950 | 0.087 | - | |
|
| 5.4845 | 9000 | 0.0429 | - | |
|
| 5.5149 | 9050 | 0.0699 | - | |
|
| 5.5454 | 9100 | 0.0911 | - | |
|
| 5.5759 | 9150 | 0.1268 | - | |
|
| 5.6063 | 9200 | 0.1042 | - | |
|
| 5.6368 | 9250 | 0.0642 | - | |
|
| 5.6673 | 9300 | 0.0736 | - | |
|
| 5.6977 | 9350 | 0.0329 | - | |
|
| 5.7282 | 9400 | 0.126 | - | |
|
| 5.7587 | 9450 | 0.0991 | - | |
|
| 5.7892 | 9500 | 0.1038 | - | |
|
| 5.8196 | 9550 | 0.0842 | - | |
|
| 5.8501 | 9600 | 0.0623 | - | |
|
| 5.8806 | 9650 | 0.0642 | - | |
|
| 5.9110 | 9700 | 0.0902 | - | |
|
| 5.9415 | 9750 | 0.0994 | - | |
|
| 5.9720 | 9800 | 0.0685 | - | |
|
| 6.0024 | 9850 | 0.0573 | - | |
|
| 6.0329 | 9900 | 0.0537 | - | |
|
| 6.0634 | 9950 | 0.0478 | - | |
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| 6.0938 | 10000 | 0.0513 | - | |
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| 6.1243 | 10050 | 0.0529 | - | |
|
| 6.1548 | 10100 | 0.095 | - | |
|
| 6.1853 | 10150 | 0.0578 | - | |
|
| 6.2157 | 10200 | 0.0918 | - | |
|
| 6.2462 | 10250 | 0.0594 | - | |
|
| 6.2767 | 10300 | 0.1015 | - | |
|
| 6.3071 | 10350 | 0.036 | - | |
|
| 6.3376 | 10400 | 0.0524 | - | |
|
| 6.3681 | 10450 | 0.0927 | - | |
|
| 6.3985 | 10500 | 0.0934 | - | |
|
| 6.4290 | 10550 | 0.0788 | - | |
|
| 6.4595 | 10600 | 0.0842 | - | |
|
| 6.4899 | 10650 | 0.0703 | - | |
|
| 6.5204 | 10700 | 0.0684 | - | |
|
| 6.5509 | 10750 | 0.0759 | - | |
|
| 6.5814 | 10800 | 0.0271 | - | |
|
| 6.6118 | 10850 | 0.0391 | - | |
|
| 6.6423 | 10900 | 0.0895 | - | |
|
| 6.6728 | 10950 | 0.054 | - | |
|
| 6.7032 | 11000 | 0.0987 | - | |
|
| 6.7337 | 11050 | 0.0577 | - | |
|
| 6.7642 | 11100 | 0.0822 | - | |
|
| 6.7946 | 11150 | 0.0986 | - | |
|
| 6.8251 | 11200 | 0.0423 | - | |
|
| 6.8556 | 11250 | 0.0672 | - | |
|
| 6.8860 | 11300 | 0.0747 | - | |
|
| 6.9165 | 11350 | 0.0873 | - | |
|
| 6.9470 | 11400 | 0.106 | - | |
|
| 6.9775 | 11450 | 0.0975 | - | |
|
| 7.0079 | 11500 | 0.0957 | - | |
|
| 7.0384 | 11550 | 0.0487 | - | |
|
| 7.0689 | 11600 | 0.0698 | - | |
|
| 7.0993 | 11650 | 0.0317 | - | |
|
| 7.1298 | 11700 | 0.0732 | - | |
|
| 7.1603 | 11750 | 0.1114 | - | |
|
| 7.1907 | 11800 | 0.0689 | - | |
|
| 7.2212 | 11850 | 0.1211 | - | |
|
| 7.2517 | 11900 | 0.0753 | - | |
|
| 7.2821 | 11950 | 0.062 | - | |
|
| 7.3126 | 12000 | 0.075 | - | |
|
| 7.3431 | 12050 | 0.0494 | - | |
|
| 7.3736 | 12100 | 0.0724 | - | |
|
| 7.4040 | 12150 | 0.0605 | - | |
|
| 7.4345 | 12200 | 0.0508 | - | |
|
| 7.4650 | 12250 | 0.0828 | - | |
|
| 7.4954 | 12300 | 0.0512 | - | |
|
| 7.5259 | 12350 | 0.1291 | - | |
|
| 7.5564 | 12400 | 0.0459 | - | |
|
| 7.5868 | 12450 | 0.0869 | - | |
|
| 7.6173 | 12500 | 0.0379 | - | |
|
| 7.6478 | 12550 | 0.1878 | - | |
|
| 7.6782 | 12600 | 0.0824 | - | |
|
| 7.7087 | 12650 | 0.0945 | - | |
|
| 7.7392 | 12700 | 0.0763 | - | |
|
| 7.7697 | 12750 | 0.0602 | - | |
|
| 7.8001 | 12800 | 0.0342 | - | |
|
| 7.8306 | 12850 | 0.0746 | - | |
|
| 7.8611 | 12900 | 0.065 | - | |
|
| 7.8915 | 12950 | 0.0749 | - | |
|
| 7.9220 | 13000 | 0.0618 | - | |
|
| 7.9525 | 13050 | 0.0567 | - | |
|
| 7.9829 | 13100 | 0.069 | - | |
|
| 8.0134 | 13150 | 0.0487 | - | |
|
| 8.0439 | 13200 | 0.0578 | - | |
|
| 8.0743 | 13250 | 0.0876 | - | |
|
| 8.1048 | 13300 | 0.0942 | - | |
|
| 8.1353 | 13350 | 0.0774 | - | |
|
| 8.1658 | 13400 | 0.0557 | - | |
|
| 8.1962 | 13450 | 0.0872 | - | |
|
| 8.2267 | 13500 | 0.0652 | - | |
|
| 8.2572 | 13550 | 0.088 | - | |
|
| 8.2876 | 13600 | 0.05 | - | |
|
| 8.3181 | 13650 | 0.0572 | - | |
|
| 8.3486 | 13700 | 0.053 | - | |
|
| 8.3790 | 13750 | 0.0745 | - | |
|
| 8.4095 | 13800 | 0.1119 | - | |
|
| 8.4400 | 13850 | 0.0909 | - | |
|
| 8.4704 | 13900 | 0.0374 | - | |
|
| 8.5009 | 13950 | 0.0515 | - | |
|
| 8.5314 | 14000 | 0.0827 | - | |
|
| 8.5619 | 14050 | 0.0925 | - | |
|
| 8.5923 | 14100 | 0.0793 | - | |
|
| 8.6228 | 14150 | 0.1123 | - | |
|
| 8.6533 | 14200 | 0.0387 | - | |
|
| 8.6837 | 14250 | 0.0898 | - | |
|
| 8.7142 | 14300 | 0.0627 | - | |
|
| 8.7447 | 14350 | 0.0863 | - | |
|
| 8.7751 | 14400 | 0.1257 | - | |
|
| 8.8056 | 14450 | 0.0553 | - | |
|
| 8.8361 | 14500 | 0.0664 | - | |
|
| 8.8665 | 14550 | 0.0641 | - | |
|
| 8.8970 | 14600 | 0.0577 | - | |
|
| 8.9275 | 14650 | 0.0672 | - | |
|
| 8.9580 | 14700 | 0.0776 | - | |
|
| 8.9884 | 14750 | 0.0951 | - | |
|
| 9.0189 | 14800 | 0.0721 | - | |
|
| 9.0494 | 14850 | 0.0609 | - | |
|
| 9.0798 | 14900 | 0.0821 | - | |
|
| 9.1103 | 14950 | 0.0477 | - | |
|
| 9.1408 | 15000 | 0.0974 | - | |
|
| 9.1712 | 15050 | 0.0534 | - | |
|
| 9.2017 | 15100 | 0.0673 | - | |
|
| 9.2322 | 15150 | 0.0549 | - | |
|
| 9.2626 | 15200 | 0.0833 | - | |
|
| 9.2931 | 15250 | 0.0957 | - | |
|
| 9.3236 | 15300 | 0.0601 | - | |
|
| 9.3541 | 15350 | 0.0702 | - | |
|
| 9.3845 | 15400 | 0.0852 | - | |
|
| 9.4150 | 15450 | 0.0576 | - | |
|
| 9.4455 | 15500 | 0.1006 | - | |
|
| 9.4759 | 15550 | 0.0697 | - | |
|
| 9.5064 | 15600 | 0.0778 | - | |
|
| 9.5369 | 15650 | 0.0778 | - | |
|
| 9.5673 | 15700 | 0.0844 | - | |
|
| 9.5978 | 15750 | 0.0724 | - | |
|
| 9.6283 | 15800 | 0.0988 | - | |
|
| 9.6587 | 15850 | 0.0699 | - | |
|
| 9.6892 | 15900 | 0.0772 | - | |
|
| 9.7197 | 15950 | 0.0757 | - | |
|
| 9.7502 | 16000 | 0.0671 | - | |
|
| 9.7806 | 16050 | 0.1057 | - | |
|
| 9.8111 | 16100 | 0.075 | - | |
|
| 9.8416 | 16150 | 0.0475 | - | |
|
| 9.8720 | 16200 | 0.0572 | - | |
|
| 9.9025 | 16250 | 0.1176 | - | |
|
| 9.9330 | 16300 | 0.0552 | - | |
|
| 9.9634 | 16350 | 0.1032 | - | |
|
| 9.9939 | 16400 | 0.0935 | - | |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 2.2.2 |
|
- Transformers: 4.35.2 |
|
- PyTorch: 2.1.0+cu121 |
|
- Datasets: 2.16.1 |
|
- Tokenizers: 0.15.0 |
|
|
|
## 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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