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--- |
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base_model: BAAI/bge-m3 |
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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: How does technology impact our daily lives and what benefits can it bring |
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to various activities? |
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- text: How do organizations effectively deploy and manage machine learning algorithms |
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to drive business value? |
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- text: What are the key considerations for organizing and managing computer lab resources |
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and tracking their status? |
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- text: How can batch processing improve the efficiency of data lake operations? |
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- text: What is the purpose of setting up a CUPS on a server? |
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inference: true |
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model-index: |
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- name: SetFit with BAAI/bge-m3 |
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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: 0.8947368421052632 |
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name: Accuracy |
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--- |
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# SetFit with BAAI/bge-m3 |
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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-m3](https://huggingface.co./BAAI/bge-m3) 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:** [BAAI/bge-m3](https://huggingface.co./BAAI/bge-m3) |
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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:** 8192 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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| lexical | <ul><li>"How does Happeo's search AI work to provide answers to user queries?"</li><li>'What are the primary areas of focus in the domain of Data Science and Analysis?'</li><li>'How can one organize a running event in Belgium?'</li></ul> | |
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| semantic | <ul><li>'What changes can be made to a channel header?'</li><li>'How can hardware capabilities impact the accuracy of motion and object detections?'</li><li>'Who is responsible for managing guarantees and prolongations?'</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** | 0.8947 | |
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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") |
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# Run inference |
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preds = model("What is the purpose of setting up a CUPS on a server?") |
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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 | 4 | 13.7407 | 28 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| lexical | 44 | |
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| semantic | 118 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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- num_epochs: (3, 3) |
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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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| 0.0005 | 1 | 0.257 | - | |
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| 0.0250 | 50 | 0.1944 | - | |
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| 0.0499 | 100 | 0.2383 | - | |
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| 0.0749 | 150 | 0.1279 | - | |
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| 0.0999 | 200 | 0.0033 | - | |
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| 0.1248 | 250 | 0.0021 | - | |
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| 0.1498 | 300 | 0.0012 | - | |
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| 0.1747 | 350 | 0.0008 | - | |
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| 0.1997 | 400 | 0.0004 | - | |
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| 0.2247 | 450 | 0.0006 | - | |
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| 0.2496 | 500 | 0.0005 | - | |
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| 0.2746 | 550 | 0.0003 | - | |
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| 0.2996 | 600 | 0.0003 | - | |
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| 0.3245 | 650 | 0.0003 | - | |
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| 0.3495 | 700 | 0.0004 | - | |
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| 0.3744 | 750 | 0.0005 | - | |
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| 0.3994 | 800 | 0.0003 | - | |
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| 0.4244 | 850 | 0.0002 | - | |
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| 0.4493 | 900 | 0.0002 | - | |
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| 0.4743 | 950 | 0.0002 | - | |
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| 0.4993 | 1000 | 0.0001 | - | |
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| 0.5242 | 1050 | 0.0001 | - | |
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| 0.5492 | 1100 | 0.0001 | - | |
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| 0.5741 | 1150 | 0.0002 | - | |
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| 0.5991 | 1200 | 0.0001 | - | |
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| 0.6241 | 1250 | 0.0003 | - | |
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| 0.6490 | 1300 | 0.0002 | - | |
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| 0.6740 | 1350 | 0.0001 | - | |
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| 0.6990 | 1400 | 0.0003 | - | |
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| 0.7239 | 1450 | 0.0001 | - | |
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| 0.7489 | 1500 | 0.0002 | - | |
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| 0.7738 | 1550 | 0.0001 | - | |
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| 0.7988 | 1600 | 0.0002 | - | |
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| 0.8238 | 1650 | 0.0002 | - | |
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| 0.8487 | 1700 | 0.0002 | - | |
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| 0.8737 | 1750 | 0.0002 | - | |
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| 0.8987 | 1800 | 0.0003 | - | |
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| 0.9236 | 1850 | 0.0001 | - | |
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| 0.9486 | 1900 | 0.0001 | - | |
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| 0.9735 | 1950 | 0.0001 | - | |
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| 0.9985 | 2000 | 0.0001 | - | |
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| **1.0** | **2003** | **-** | **0.1735** | |
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| 1.0235 | 2050 | 0.0001 | - | |
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| 1.0484 | 2100 | 0.0001 | - | |
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| 1.0734 | 2150 | 0.0001 | - | |
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| 1.0984 | 2200 | 0.0 | - | |
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| 1.1233 | 2250 | 0.0001 | - | |
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| 1.1483 | 2300 | 0.0001 | - | |
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| 1.1732 | 2350 | 0.0001 | - | |
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| 1.1982 | 2400 | 0.0002 | - | |
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| 1.2232 | 2450 | 0.0001 | - | |
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| 1.2481 | 2500 | 0.0 | - | |
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| 1.2731 | 2550 | 0.0001 | - | |
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| 1.2981 | 2600 | 0.0001 | - | |
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| 1.3230 | 2650 | 0.0 | - | |
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| 1.3480 | 2700 | 0.0001 | - | |
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| 1.3729 | 2750 | 0.0001 | - | |
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| 1.3979 | 2800 | 0.0001 | - | |
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| 1.4229 | 2850 | 0.0 | - | |
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| 1.4478 | 2900 | 0.0001 | - | |
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| 1.4728 | 2950 | 0.0001 | - | |
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| 1.4978 | 3000 | 0.0001 | - | |
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| 1.5227 | 3050 | 0.0001 | - | |
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| 1.5477 | 3100 | 0.0 | - | |
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| 1.5726 | 3150 | 0.0 | - | |
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| 1.5976 | 3200 | 0.0001 | - | |
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| 1.6226 | 3250 | 0.0001 | - | |
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| 1.6475 | 3300 | 0.0001 | - | |
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| 1.6725 | 3350 | 0.0001 | - | |
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| 1.6975 | 3400 | 0.0001 | - | |
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| 1.7224 | 3450 | 0.0 | - | |
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| 1.7474 | 3500 | 0.0002 | - | |
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| 1.7723 | 3550 | 0.0001 | - | |
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| 1.7973 | 3600 | 0.0 | - | |
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| 1.8223 | 3650 | 0.0 | - | |
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| 1.8472 | 3700 | 0.0001 | - | |
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| 1.8722 | 3750 | 0.0 | - | |
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| 1.8972 | 3800 | 0.0001 | - | |
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| 1.9221 | 3850 | 0.0 | - | |
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| 1.9471 | 3900 | 0.0 | - | |
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| 1.9720 | 3950 | 0.0001 | - | |
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| 1.9970 | 4000 | 0.0 | - | |
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| 2.0 | 4006 | - | 0.2593 | |
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| 2.0220 | 4050 | 0.0001 | - | |
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| 2.0469 | 4100 | 0.0001 | - | |
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| 2.0719 | 4150 | 0.0 | - | |
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| 2.0969 | 4200 | 0.0001 | - | |
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| 2.1218 | 4250 | 0.0 | - | |
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| 2.1468 | 4300 | 0.0001 | - | |
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| 2.1717 | 4350 | 0.0001 | - | |
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| 2.1967 | 4400 | 0.0001 | - | |
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| 2.2217 | 4450 | 0.0001 | - | |
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| 2.2466 | 4500 | 0.0001 | - | |
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| 2.2716 | 4550 | 0.0 | - | |
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| 2.2966 | 4600 | 0.0 | - | |
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| 2.3215 | 4650 | 0.0 | - | |
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| 2.3465 | 4700 | 0.0001 | - | |
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| 2.3714 | 4750 | 0.0001 | - | |
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| 2.3964 | 4800 | 0.0002 | - | |
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| 2.4214 | 4850 | 0.0001 | - | |
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| 2.4463 | 4900 | 0.0001 | - | |
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| 2.4713 | 4950 | 0.0 | - | |
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| 2.4963 | 5000 | 0.0001 | - | |
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| 2.5212 | 5050 | 0.0001 | - | |
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| 2.5462 | 5100 | 0.0 | - | |
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| 2.5711 | 5150 | 0.0001 | - | |
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| 2.5961 | 5200 | 0.0 | - | |
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| 2.6211 | 5250 | 0.0 | - | |
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| 2.6460 | 5300 | 0.0 | - | |
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| 2.6710 | 5350 | 0.0 | - | |
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| 2.6960 | 5400 | 0.0 | - | |
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| 2.7209 | 5450 | 0.0 | - | |
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| 2.7459 | 5500 | 0.0 | - | |
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| 2.7708 | 5550 | 0.0 | - | |
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| 2.7958 | 5600 | 0.0001 | - | |
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| 2.8208 | 5650 | 0.0 | - | |
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| 2.8457 | 5700 | 0.0 | - | |
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| 2.8707 | 5750 | 0.0 | - | |
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| 2.8957 | 5800 | 0.0 | - | |
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| 2.9206 | 5850 | 0.0 | - | |
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| 2.9456 | 5900 | 0.0001 | - | |
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| 2.9705 | 5950 | 0.0 | - | |
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| 2.9955 | 6000 | 0.0 | - | |
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| 3.0 | 6009 | - | 0.2738 | |
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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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