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--- |
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base_model: sentence-transformers/all-MiniLM-L6-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: What are the key situations that require the preparation of a mission order? |
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- text: How can audio data be used to improve speaker identification using neural |
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networks? |
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- text: How can organizations balance the need for data privacy with the benefits |
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of involving interns in data-related projects? |
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- text: What is the purpose of the message posted by the CR? |
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- text: What are the consequences of adopting a 'if not broken, don't fix' attitude |
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towards data monitoring? |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/all-MiniLM-L6-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: 0.3076923076923077 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-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:** 256 tokens |
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- **Number of Classes:** 4 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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| very_semantic | <ul><li>'What are the key considerations when proposing names for a project or initiative?'</li><li>'What are the key aspects of team life and events in a company?'</li><li>'What is being asked for or sought in this conversation?'</li></ul> | |
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| lexical | <ul><li>'Who is responsible for reviewing and signing documents related to conference submissions?'</li><li>'How do data architecture and management systems enable digital transformation and address its associated challenges?'</li><li>'How do keys or access credentials get shared or transferred among team members in a workplace?'</li></ul> | |
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| very_lexical | <ul><li>'What are some of the key challenges associated with handling and storing large amounts of genomic data?'</li><li>"What is the focus of Eurobiomed's partnership with Digital113?"</li><li>'What are the key considerations for generating well-formatted JSON instances that conform to a given schema?'</li></ul> | |
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| semantic | <ul><li>'How can visualizations be used to enhance documentation and collaboration in software development?'</li><li>'What are the key considerations when choosing a distance metric for a vector database?'</li><li>'How can AI be leveraged to support HR departments in detecting and addressing gender bias?'</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** | 0.3077 | |
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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("yaniseuranova/setfit-rag-hybrid-search-query-router-test") |
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# Run inference |
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preds = model("What is the purpose of the message posted by the CR?") |
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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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*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 | 7 | 14.1913 | 24 | |
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| Label | Training Sample Count | |
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|:--------------|:----------------------| |
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| lexical | 41 | |
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| semantic | 24 | |
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| very_lexical | 17 | |
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| very_semantic | 33 | |
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### Training Hyperparameters |
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- batch_size: (4, 4) |
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- num_epochs: (2, 2) |
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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: 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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| 0.0004 | 1 | 0.4883 | - | |
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| 0.0209 | 50 | 0.3738 | - | |
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| 0.0417 | 100 | 0.2192 | - | |
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| 0.0626 | 150 | 0.1503 | - | |
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| 0.0834 | 200 | 0.1514 | - | |
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| 0.1043 | 250 | 0.1829 | - | |
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| 0.1251 | 300 | 0.4191 | - | |
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| 0.1460 | 350 | 0.2136 | - | |
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| 0.1668 | 400 | 0.1847 | - | |
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| 0.1877 | 450 | 0.1681 | - | |
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| 0.2085 | 500 | 0.222 | - | |
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| 0.2294 | 550 | 0.0397 | - | |
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| 0.2502 | 600 | 0.2626 | - | |
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| 0.2711 | 650 | 0.1343 | - | |
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| 0.2919 | 700 | 0.1769 | - | |
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| 0.3128 | 750 | 0.1704 | - | |
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| 0.3336 | 800 | 0.401 | - | |
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| 0.3545 | 850 | 0.1405 | - | |
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| 0.3753 | 900 | 0.1892 | - | |
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| 0.3962 | 950 | 0.1444 | - | |
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| 0.4170 | 1000 | 0.2337 | - | |
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| 0.4379 | 1050 | 0.1848 | - | |
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| 0.4587 | 1100 | 0.0601 | - | |
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| 0.4796 | 1150 | 0.2467 | - | |
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| 0.5004 | 1200 | 0.1829 | - | |
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| 0.5213 | 1250 | 0.1695 | - | |
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| 0.5421 | 1300 | 0.3892 | - | |
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| 0.5630 | 1350 | 0.1408 | - | |
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| 0.5838 | 1400 | 0.0506 | - | |
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| 0.6047 | 1450 | 0.1835 | - | |
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| 0.6255 | 1500 | 0.3284 | - | |
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| 0.6464 | 1550 | 0.1797 | - | |
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| 0.6672 | 1600 | 0.1118 | - | |
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| 0.6881 | 1650 | 0.1502 | - | |
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| 0.7089 | 1700 | 0.112 | - | |
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| 0.7298 | 1750 | 0.0401 | - | |
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| 0.7506 | 1800 | 0.117 | - | |
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| 0.7715 | 1850 | 0.1287 | - | |
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| 0.7923 | 1900 | 0.0623 | - | |
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| 0.8132 | 1950 | 0.2128 | - | |
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| 0.8340 | 2000 | 0.1542 | - | |
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| 0.8549 | 2050 | 0.1774 | - | |
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| 0.8757 | 2100 | 0.3252 | - | |
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| 0.8966 | 2150 | 0.0152 | - | |
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| 0.9174 | 2200 | 0.0539 | - | |
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| 0.9383 | 2250 | 0.0047 | - | |
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| 0.9591 | 2300 | 0.1232 | - | |
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| 0.9800 | 2350 | 0.3466 | - | |
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| **1.0** | **2398** | **-** | **0.3644** | |
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| 1.0008 | 2400 | 0.0296 | - | |
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| 1.0217 | 2450 | 0.3459 | - | |
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| 1.0425 | 2500 | 0.0867 | - | |
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| 1.0634 | 2550 | 0.1343 | - | |
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| 1.0842 | 2600 | 0.2074 | - | |
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| 1.1051 | 2650 | 0.0052 | - | |
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| 1.1259 | 2700 | 0.0548 | - | |
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| 1.1468 | 2750 | 0.0441 | - | |
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| 1.1676 | 2800 | 0.0821 | - | |
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| 1.1885 | 2850 | 0.0546 | - | |
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| 1.2093 | 2900 | 0.1286 | - | |
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| 1.2302 | 2950 | 0.1222 | - | |
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| 1.2510 | 3000 | 0.0227 | - | |
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| 1.2719 | 3050 | 0.3011 | - | |
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| 1.2927 | 3100 | 0.018 | - | |
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| 1.3136 | 3150 | 0.0581 | - | |
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| 1.3344 | 3200 | 0.0485 | - | |
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| 1.3553 | 3250 | 0.2369 | - | |
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| 1.3761 | 3300 | 0.1681 | - | |
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| 1.3970 | 3350 | 0.1289 | - | |
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| 1.4178 | 3400 | 0.1664 | - | |
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| 1.4387 | 3450 | 0.1467 | - | |
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| 1.4595 | 3500 | 0.1399 | - | |
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| 1.4804 | 3550 | 0.3045 | - | |
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| 1.5013 | 3600 | 0.2155 | - | |
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| 1.5221 | 3650 | 0.061 | - | |
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| 1.5430 | 3700 | 0.0787 | - | |
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| 1.5638 | 3750 | 0.3649 | - | |
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| 1.5847 | 3800 | 0.1202 | - | |
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| 1.6055 | 3850 | 0.1004 | - | |
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| 1.6264 | 3900 | 0.154 | - | |
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| 1.6472 | 3950 | 0.0944 | - | |
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| 1.6681 | 4000 | 0.0004 | - | |
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| 1.6889 | 4050 | 0.1843 | - | |
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| 1.7098 | 4100 | 0.2233 | - | |
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| 1.7306 | 4150 | 0.2203 | - | |
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| 1.7515 | 4200 | 0.0986 | - | |
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| 1.7723 | 4250 | 0.2295 | - | |
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| 1.7932 | 4300 | 0.1763 | - | |
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| 1.8140 | 4350 | 0.3487 | - | |
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| 1.8349 | 4400 | 0.3285 | - | |
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| 1.8557 | 4450 | 0.0152 | - | |
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| 1.8766 | 4500 | 0.1108 | - | |
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| 1.8974 | 4550 | 0.2416 | - | |
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| 1.9183 | 4600 | 0.0476 | - | |
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| 1.9391 | 4650 | 0.2929 | - | |
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| 1.9600 | 4700 | 0.1006 | - | |
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| 1.9808 | 4750 | 0.0925 | - | |
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| 2.0 | 4796 | - | 0.3669 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.6.1 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.1+cu121 |
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- Datasets: 2.18.0 |
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- Tokenizers: 0.15.2 |
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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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