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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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widget: |
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- text: A traumatised dog that was found buried up to its head in dirt in France is |
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now in safe hands. This is such a... http://t.co/AGQo1479xM |
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- text: 'Hibernating pbx irrespective of pitch fatality careerism pan: crbZFZ' |
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- text: Stuart Broad Takes Eight Before Joe Root Runs Riot Against Aussies |
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- text: Maj Muzzamil Pilot Offr of MI-17 crashed near Mansehra today. http://t.co/kL4R1ccWct |
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- text: '@AdriaSimon_: Hailstorm day 2.... #round2 #yyc #yycstorm http://t.co/FqQI8GVLQ4' |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/all-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: 0.8172066549912435 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-mpnet-base-v2 |
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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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-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. |
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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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-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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- **Maximum Sequence Length:** 384 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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| 0 | <ul><li>"Was '80s New #Wave a #Casualty of #AIDS?: Tweet And Since they\x89Ûªd grown up watching David\x89Û_ http://t.co/qBecjli7cx"</li><li>"@CharlesDagnall He's getting 50 here I think. Salt. Wounds. Rub. In."</li><li>'Navy sidelines 3 newest subs http://t.co/gpVZV0249Y'</li></ul> | |
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| 1 | <ul><li>'The Latest: More Homes Razed by Northern California Wildfire - ABC News http://t.co/bKsYymvIsg #GN'</li><li>'@Durban_Knight Rescuers are searching for hundreds of migrants in the Mediterranean after a boat carr... http://t.co/cWCVBuBs01 @Nosy_Be'</li><li>'NEMA Ekiti distributed relief materials to affected victims of Rain/Windstorm disaster at Ode-Ekiti in Gbonyin LGA.'</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** | 0.8172 | |
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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("pEpOo/catastrophy5") |
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# Run inference |
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preds = model("Stuart Broad Takes Eight Before Joe Root Runs Riot Against Aussies") |
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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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### 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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## 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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### 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 | 1 | 14.9796 | 54 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 1732 | |
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| 1 | 1313 | |
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### Training Hyperparameters |
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- 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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- num_iterations: 20 |
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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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| 0.0001 | 1 | 0.3383 | - | |
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| 0.0066 | 50 | 0.352 | - | |
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| 0.0131 | 100 | 0.3529 | - | |
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| 0.0197 | 150 | 0.2286 | - | |
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| 0.0263 | 200 | 0.2654 | - | |
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| 0.0328 | 250 | 0.2892 | - | |
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| 0.0394 | 300 | 0.1808 | - | |
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| 0.0460 | 350 | 0.2056 | - | |
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| 0.0525 | 400 | 0.0863 | - | |
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| 0.0591 | 450 | 0.2034 | - | |
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| 0.0657 | 500 | 0.1339 | - | |
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| 0.0722 | 550 | 0.1022 | - | |
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| 0.0788 | 600 | 0.1083 | - | |
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| 0.0854 | 650 | 0.1035 | - | |
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| 0.0919 | 700 | 0.1201 | - | |
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| 0.0985 | 750 | 0.0626 | - | |
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| 0.1051 | 800 | 0.1257 | - | |
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| 0.1117 | 850 | 0.1543 | - | |
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| 0.1182 | 900 | 0.0367 | - | |
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| 0.1248 | 950 | 0.1749 | - | |
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| 0.1314 | 1000 | 0.0553 | - | |
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| 0.1379 | 1050 | 0.0836 | - | |
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| 0.1445 | 1100 | 0.0161 | - | |
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| 0.1511 | 1150 | 0.1149 | - | |
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| 0.1576 | 1200 | 0.1144 | - | |
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| 0.1642 | 1250 | 0.0028 | - | |
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| 0.1708 | 1300 | 0.0037 | - | |
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| 0.1773 | 1350 | 0.1769 | - | |
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| 0.1839 | 1400 | 0.0172 | - | |
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| 0.1905 | 1450 | 0.0397 | - | |
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| 0.1970 | 1500 | 0.0645 | - | |
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| 0.2036 | 1550 | 0.0659 | - | |
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| 0.2102 | 1600 | 0.0014 | - | |
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| 0.2167 | 1650 | 0.0016 | - | |
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| 0.2233 | 1700 | 0.0729 | - | |
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| 0.2299 | 1750 | 0.0072 | - | |
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| 0.2364 | 1800 | 0.0175 | - | |
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| 0.2430 | 1850 | 0.0278 | - | |
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| 0.2496 | 1900 | 0.0537 | - | |
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| 0.2561 | 1950 | 0.0038 | - | |
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| 0.2627 | 2000 | 0.087 | - | |
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| 0.2693 | 2050 | 0.0459 | - | |
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| 0.2758 | 2100 | 0.0169 | - | |
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| 0.2824 | 2150 | 0.0112 | - | |
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| 0.2890 | 2200 | 0.001 | - | |
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| 0.2955 | 2250 | 0.0204 | - | |
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| 0.3021 | 2300 | 0.0796 | - | |
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| 0.3087 | 2350 | 0.0592 | - | |
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| 0.3153 | 2400 | 0.0003 | - | |
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| 0.3218 | 2450 | 0.0033 | - | |
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| 0.3284 | 2500 | 0.0309 | - | |
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| 0.3350 | 2550 | 0.0065 | - | |
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| 0.3415 | 2600 | 0.002 | - | |
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| 0.3481 | 2650 | 0.0076 | - | |
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| 0.3547 | 2700 | 0.0008 | - | |
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| 0.3612 | 2750 | 0.0023 | - | |
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| 0.3678 | 2800 | 0.0028 | - | |
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| 0.3744 | 2850 | 0.0171 | - | |
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| 0.3809 | 2900 | 0.0011 | - | |
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| 0.3875 | 2950 | 0.0015 | - | |
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| 0.3941 | 3000 | 0.0468 | - | |
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| 0.4006 | 3050 | 0.0075 | - | |
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| 0.4072 | 3100 | 0.0009 | - | |
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| 0.4138 | 3150 | 0.0334 | - | |
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| 0.4203 | 3200 | 0.0002 | - | |
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| 0.4269 | 3250 | 0.0001 | - | |
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| 0.4335 | 3300 | 0.0002 | - | |
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| 0.4400 | 3350 | 0.0001 | - | |
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| 0.4466 | 3400 | 0.021 | - | |
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| 0.4532 | 3450 | 0.0043 | - | |
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| 0.4597 | 3500 | 0.0084 | - | |
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| 0.4663 | 3550 | 0.0009 | - | |
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| 0.4729 | 3600 | 0.0033 | - | |
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| 0.4794 | 3650 | 0.0035 | - | |
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| 0.4860 | 3700 | 0.0004 | - | |
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| 0.4926 | 3750 | 0.0297 | - | |
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| 0.4991 | 3800 | 0.0004 | - | |
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| 0.5057 | 3850 | 0.0011 | - | |
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| 0.5123 | 3900 | 0.0238 | - | |
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| 0.5188 | 3950 | 0.0248 | - | |
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| 0.5254 | 4000 | 0.0293 | - | |
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| 0.5320 | 4050 | 0.0365 | - | |
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| 0.5386 | 4100 | 0.0261 | - | |
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| 0.5451 | 4150 | 0.0469 | - | |
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| 0.5517 | 4200 | 0.0098 | - | |
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| 0.5583 | 4250 | 0.0002 | - | |
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| 0.5648 | 4300 | 0.0236 | - | |
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| 0.5714 | 4350 | 0.0001 | - | |
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| 0.5780 | 4400 | 0.0001 | - | |
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| 0.5845 | 4450 | 0.0001 | - | |
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| 0.5911 | 4500 | 0.0138 | - | |
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| 0.5977 | 4550 | 0.0116 | - | |
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| 0.6042 | 4600 | 0.0003 | - | |
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| 0.6108 | 4650 | 0.0003 | - | |
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| 0.6174 | 4700 | 0.0001 | - | |
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| 0.6239 | 4750 | 0.0 | - | |
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| 0.6305 | 4800 | 0.0246 | - | |
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| 0.6371 | 4850 | 0.0001 | - | |
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| 0.6436 | 4900 | 0.0543 | - | |
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| 0.6502 | 4950 | 0.0001 | - | |
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| 0.6568 | 5000 | 0.0093 | - | |
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| 0.6633 | 5050 | 0.0001 | - | |
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| 0.6699 | 5100 | 0.0 | - | |
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| 0.6765 | 5150 | 0.0002 | - | |
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| 0.6830 | 5200 | 0.0001 | - | |
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| 0.6896 | 5250 | 0.0372 | - | |
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| 0.6962 | 5300 | 0.0 | - | |
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| 0.7027 | 5350 | 0.0001 | - | |
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| 0.7093 | 5400 | 0.0001 | - | |
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| 0.7159 | 5450 | 0.0003 | - | |
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| 0.7224 | 5500 | 0.0004 | - | |
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| 0.7290 | 5550 | 0.0001 | - | |
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| 0.7356 | 5600 | 0.0 | - | |
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| 0.7422 | 5650 | 0.0 | - | |
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| 0.7487 | 5700 | 0.0001 | - | |
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| 0.7553 | 5750 | 0.0001 | - | |
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| 0.7619 | 5800 | 0.0 | - | |
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| 0.7684 | 5850 | 0.0 | - | |
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| 0.7750 | 5900 | 0.0 | - | |
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| 0.7816 | 5950 | 0.0 | - | |
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| 0.7881 | 6000 | 0.0 | - | |
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| 0.7947 | 6050 | 0.0 | - | |
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| 0.8013 | 6100 | 0.0 | - | |
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| 0.8078 | 6150 | 0.0001 | - | |
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| 0.8144 | 6200 | 0.0001 | - | |
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| 0.8210 | 6250 | 0.0 | - | |
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| 0.8275 | 6300 | 0.0 | - | |
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| 0.8341 | 6350 | 0.0 | - | |
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| 0.8407 | 6400 | 0.0002 | - | |
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| 0.8472 | 6450 | 0.0 | - | |
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| 0.8538 | 6500 | 0.0001 | - | |
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| 0.8604 | 6550 | 0.0 | - | |
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| 0.8669 | 6600 | 0.0001 | - | |
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| 0.8735 | 6650 | 0.0001 | - | |
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| 0.8801 | 6700 | 0.0 | - | |
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| 0.8866 | 6750 | 0.0 | - | |
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| 0.8932 | 6800 | 0.0373 | - | |
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| 0.8998 | 6850 | 0.0 | - | |
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| 0.9063 | 6900 | 0.0 | - | |
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| 0.9129 | 6950 | 0.0272 | - | |
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| 0.9195 | 7000 | 0.0 | - | |
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| 0.9260 | 7050 | 0.0 | - | |
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| 0.9326 | 7100 | 0.0001 | - | |
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| 0.9392 | 7150 | 0.0 | - | |
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| 0.9458 | 7200 | 0.0002 | - | |
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| 0.9523 | 7250 | 0.0001 | - | |
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| 0.9589 | 7300 | 0.0 | - | |
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| 0.9655 | 7350 | 0.0 | - | |
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| 0.9720 | 7400 | 0.0 | - | |
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| 0.9786 | 7450 | 0.0001 | - | |
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| 0.9852 | 7500 | 0.0 | - | |
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| 0.9917 | 7550 | 0.0 | - | |
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| 0.9983 | 7600 | 0.0 | - | |
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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: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.15.0 |
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- Tokenizers: 0.15.0 |
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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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