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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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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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widget: |
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- text: so i am currently stuck in an automatic revolving door . |
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- text: ah my favorite pastime , watching logan and crying |
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- text: i have a new instagram account ! go give theollyjackson a follow |
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- text: guess they are not rich enough to get their precious cars in a garage . |
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- text: last day in my twenties |
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pipeline_tag: text-classification |
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inference: true |
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base_model: BAAI/bge-small-en-v1.5 |
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model-index: |
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- name: SetFit with BAAI/bge-small-en-v1.5 |
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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: 0.6617812852311161 |
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name: Accuracy |
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- type: f1 |
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value: 0.3951612903225807 |
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name: F1 |
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- type: precision |
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value: 0.2890855457227139 |
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name: Precision |
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- type: recall |
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value: 0.6242038216560509 |
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name: Recall |
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--- |
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# SetFit with BAAI/bge-small-en-v1.5 |
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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 [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
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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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| NON_SARCASTIC | <ul><li>'so the newer devices have the ios screenshot i m still on ios but my ipad mini 1 st gen shows the ios screenshot . odd .'</li><li>'why do amazon need a test authorisation when i add a new payment card , as well as the authorisation they get when i actually use it ?'</li><li>'waterboarding sounds like a lot of fun until you find out what it is'</li></ul> | |
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| SARCASTIC | <ul><li>"have you been reading long ? you are not very good at it . it has nothing to do with who i like , especially since i am not a fan of corbyn anyway . it ' s that in one case someone was literally slapped in the face , and in the other someone wore a milkshake . battery > being annoying"</li><li>'wish one of the many people dressed as killers were actually one n killed me'</li><li>'is it even christmas if there isn t a fight with neighbours and a broken wrist ?'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | F1 | Precision | Recall | |
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|:--------|:---------|:-------|:----------|:-------| |
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| **all** | 0.6618 | 0.3952 | 0.2891 | 0.6242 | |
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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("w11wo/bge-small-en-v1.5-isarcasm") |
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# Run inference |
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preds = model("last day in my twenties") |
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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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### 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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## 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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 2 | 19.8489 | 63 | |
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| Label | Training Sample Count | |
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|:--------------|:----------------------| |
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| NON_SARCASTIC | 609 | |
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| SARCASTIC | 609 | |
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### Training Hyperparameters |
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- batch_size: (256, 16) |
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- num_epochs: (3, 8) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 5e-06) |
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- head_learning_rate: 0.002 |
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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: True |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0003 | 1 | 0.2571 | - | |
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| 0.0172 | 50 | 0.251 | - | |
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| 0.0344 | 100 | 0.2556 | - | |
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| 0.0517 | 150 | 0.2513 | - | |
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| 0.0689 | 200 | 0.2531 | - | |
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| 0.0861 | 250 | 0.2518 | - | |
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| 0.1033 | 300 | 0.2553 | - | |
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| 0.1206 | 350 | 0.2501 | - | |
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| 0.1378 | 400 | 0.2546 | - | |
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| 0.1550 | 450 | 0.2506 | - | |
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| 0.1722 | 500 | 0.2317 | - | |
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| 0.1895 | 550 | 0.093 | - | |
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| 0.2067 | 600 | 0.0139 | - | |
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| 0.2239 | 650 | 0.0166 | - | |
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| 0.2411 | 700 | 0.0053 | - | |
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| 0.2584 | 750 | 0.0013 | - | |
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| 0.2756 | 800 | 0.0121 | - | |
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| 0.2928 | 850 | 0.0096 | - | |
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| 0.3100 | 900 | 0.0043 | - | |
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| 0.3272 | 950 | 0.0014 | - | |
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| 0.3445 | 1000 | 0.0009 | - | |
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| 0.3617 | 1050 | 0.0117 | - | |
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| 0.3789 | 1100 | 0.0144 | - | |
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| 0.3961 | 1150 | 0.0084 | - | |
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| 0.4134 | 1200 | 0.0006 | - | |
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| 0.4306 | 1250 | 0.0005 | - | |
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| 0.4478 | 1300 | 0.0081 | - | |
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| 0.4650 | 1350 | 0.0144 | - | |
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| 0.4823 | 1400 | 0.0045 | - | |
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| 0.4995 | 1450 | 0.0042 | - | |
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| 0.5167 | 1500 | 0.0005 | - | |
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| 0.5339 | 1550 | 0.003 | - | |
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| 0.5512 | 1600 | 0.0004 | - | |
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| 0.5684 | 1650 | 0.0005 | - | |
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| 0.5856 | 1700 | 0.0004 | - | |
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| 0.6028 | 1750 | 0.0004 | - | |
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| 0.6200 | 1800 | 0.0026 | - | |
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| 0.6373 | 1850 | 0.0004 | - | |
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| 0.6545 | 1900 | 0.0004 | - | |
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| 0.6717 | 1950 | 0.0003 | - | |
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| 0.6889 | 2000 | 0.0014 | - | |
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| 0.7062 | 2050 | 0.0004 | - | |
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| 0.7234 | 2100 | 0.0003 | - | |
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| 0.7406 | 2150 | 0.0003 | - | |
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| 0.7578 | 2200 | 0.0004 | - | |
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| 0.7751 | 2250 | 0.0003 | - | |
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| 0.7923 | 2300 | 0.0003 | - | |
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| 0.8095 | 2350 | 0.0003 | - | |
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| 0.8267 | 2400 | 0.0003 | - | |
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| 0.8440 | 2450 | 0.0003 | - | |
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| 0.8612 | 2500 | 0.0003 | - | |
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| 0.8784 | 2550 | 0.0003 | - | |
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| 0.8956 | 2600 | 0.0003 | - | |
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| 0.9128 | 2650 | 0.0003 | - | |
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| 0.9301 | 2700 | 0.0003 | - | |
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| 0.9473 | 2750 | 0.0004 | - | |
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| 0.9645 | 2800 | 0.0003 | - | |
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| 0.9817 | 2850 | 0.0003 | - | |
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| 0.9990 | 2900 | 0.0036 | - | |
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| 1.0162 | 2950 | 0.0003 | - | |
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| 1.0334 | 3000 | 0.0003 | - | |
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| 1.0506 | 3050 | 0.0002 | - | |
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| 1.0679 | 3100 | 0.0002 | - | |
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| 1.0851 | 3150 | 0.0002 | - | |
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| 1.1023 | 3200 | 0.0002 | - | |
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| 1.1195 | 3250 | 0.0002 | - | |
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| 1.1368 | 3300 | 0.0003 | - | |
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| 1.1540 | 3350 | 0.0004 | - | |
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| 1.1712 | 3400 | 0.0002 | - | |
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| 1.1884 | 3450 | 0.0002 | - | |
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| 1.2056 | 3500 | 0.0002 | - | |
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| 1.2229 | 3550 | 0.0002 | - | |
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| 1.2401 | 3600 | 0.0002 | - | |
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| 1.2573 | 3650 | 0.0009 | - | |
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| 1.2745 | 3700 | 0.0002 | - | |
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| 1.2918 | 3750 | 0.0002 | - | |
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| 1.3090 | 3800 | 0.0002 | - | |
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| 1.3262 | 3850 | 0.0002 | - | |
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| 1.3434 | 3900 | 0.0002 | - | |
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| 1.3607 | 3950 | 0.0002 | - | |
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| 1.3779 | 4000 | 0.0002 | - | |
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| 1.3951 | 4050 | 0.0002 | - | |
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| 1.4123 | 4100 | 0.0002 | - | |
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| 1.4296 | 4150 | 0.0002 | - | |
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| 1.4468 | 4200 | 0.0003 | - | |
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| 1.4640 | 4250 | 0.0002 | - | |
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| 1.4812 | 4300 | 0.0002 | - | |
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| 1.4984 | 4350 | 0.0002 | - | |
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| 1.5157 | 4400 | 0.0002 | - | |
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| 1.5329 | 4450 | 0.0002 | - | |
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| 1.5501 | 4500 | 0.0002 | - | |
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| 1.5673 | 4550 | 0.0002 | - | |
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| 1.5846 | 4600 | 0.0002 | - | |
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| 1.6018 | 4650 | 0.0002 | - | |
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| 1.6190 | 4700 | 0.0002 | - | |
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| 1.6362 | 4750 | 0.0002 | - | |
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| 1.6535 | 4800 | 0.0002 | - | |
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| 1.6707 | 4850 | 0.0002 | - | |
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| 1.6879 | 4900 | 0.0002 | - | |
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| 1.7051 | 4950 | 0.0002 | - | |
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| 1.7224 | 5000 | 0.0003 | - | |
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| 1.7396 | 5050 | 0.0002 | - | |
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| 1.7568 | 5100 | 0.0002 | - | |
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| 1.7740 | 5150 | 0.0002 | - | |
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| 1.7913 | 5200 | 0.0002 | - | |
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| 1.8085 | 5250 | 0.0002 | - | |
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| 1.8257 | 5300 | 0.0038 | - | |
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| 1.8429 | 5350 | 0.0002 | - | |
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| 1.8601 | 5400 | 0.0002 | - | |
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| 1.8774 | 5450 | 0.0002 | - | |
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| 1.8946 | 5500 | 0.0002 | - | |
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| 1.9118 | 5550 | 0.0002 | - | |
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| 1.9290 | 5600 | 0.0005 | - | |
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| 1.9463 | 5650 | 0.0002 | - | |
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| 1.9635 | 5700 | 0.0002 | - | |
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| 1.9807 | 5750 | 0.0002 | - | |
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| 1.9979 | 5800 | 0.0002 | - | |
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| 2.0152 | 5850 | 0.0001 | - | |
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| 2.0324 | 5900 | 0.0002 | - | |
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| 2.0496 | 5950 | 0.0002 | - | |
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| 2.0668 | 6000 | 0.0002 | - | |
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| 2.0841 | 6050 | 0.0002 | - | |
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| 2.1013 | 6100 | 0.0002 | - | |
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| 2.1185 | 6150 | 0.0002 | - | |
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| 2.1357 | 6200 | 0.0001 | - | |
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| 2.1529 | 6250 | 0.0002 | - | |
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| 2.1702 | 6300 | 0.0002 | - | |
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| 2.1874 | 6350 | 0.0001 | - | |
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| 2.2046 | 6400 | 0.0001 | - | |
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| 2.2218 | 6450 | 0.0001 | - | |
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| 2.2391 | 6500 | 0.0001 | - | |
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| 2.2563 | 6550 | 0.0001 | - | |
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| 2.2735 | 6600 | 0.0001 | - | |
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| 2.2907 | 6650 | 0.0001 | - | |
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| 2.3080 | 6700 | 0.0001 | - | |
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| 2.3252 | 6750 | 0.0001 | - | |
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| 2.3424 | 6800 | 0.0001 | - | |
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| 2.3596 | 6850 | 0.0001 | - | |
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| 2.3769 | 6900 | 0.0001 | - | |
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| 2.3941 | 6950 | 0.0001 | - | |
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| 2.4113 | 7000 | 0.0001 | - | |
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| 2.4285 | 7050 | 0.0001 | - | |
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| 2.4457 | 7100 | 0.0001 | - | |
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| 2.4630 | 7150 | 0.0001 | - | |
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| 2.4802 | 7200 | 0.0001 | - | |
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| 2.4974 | 7250 | 0.0001 | - | |
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| 2.5146 | 7300 | 0.0001 | - | |
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| 2.5319 | 7350 | 0.0001 | - | |
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| 2.5491 | 7400 | 0.0001 | - | |
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| 2.5663 | 7450 | 0.0001 | - | |
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| 2.5835 | 7500 | 0.0001 | - | |
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| 2.6008 | 7550 | 0.0001 | - | |
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| 2.6180 | 7600 | 0.0001 | - | |
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| 2.6352 | 7650 | 0.0001 | - | |
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| 2.6524 | 7700 | 0.0001 | - | |
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| 2.6697 | 7750 | 0.0001 | - | |
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| 2.6869 | 7800 | 0.0001 | - | |
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| 2.7041 | 7850 | 0.0001 | - | |
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| 2.7213 | 7900 | 0.0001 | - | |
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| 2.7385 | 7950 | 0.0001 | - | |
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| 2.7558 | 8000 | 0.0001 | - | |
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| 2.7730 | 8050 | 0.0001 | - | |
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| 2.7902 | 8100 | 0.0001 | - | |
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| 2.8074 | 8150 | 0.0001 | - | |
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| 2.8247 | 8200 | 0.0001 | - | |
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| 2.8419 | 8250 | 0.0001 | - | |
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| 2.8591 | 8300 | 0.0001 | - | |
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| 2.8763 | 8350 | 0.0001 | - | |
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| 2.8936 | 8400 | 0.0001 | - | |
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| 2.9108 | 8450 | 0.0001 | - | |
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| 2.9280 | 8500 | 0.0001 | - | |
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| 2.9452 | 8550 | 0.0001 | - | |
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| 2.9625 | 8600 | 0.0001 | - | |
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| 2.9797 | 8650 | 0.0001 | - | |
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| 2.9969 | 8700 | 0.0001 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.1 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.32.0 |
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- PyTorch: 2.1.1+cu121 |
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- Datasets: 2.14.5 |
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- Tokenizers: 0.13.3 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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