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--- |
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base_model: sentence-transformers/paraphrase-mpnet-base-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 different types of zari used in the sarees? |
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- text: I need to change the delivery address for my recent order, how can I do that? |
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- text: I need to return an item, what is the return policy for online orders? |
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- text: Are there any sarees with Fekwa Weave technique? |
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- text: What are the different colors in the Air Jordan 1 Retro High OG Volt Gold? |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-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: 0.8666666666666667 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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:** 512 tokens |
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- **Number of Classes:** 6 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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| Out of Scope | <ul><li>'Why is your website so slow?'</li><li>'Can I get a shoutout on your social media?'</li><li>'I like to listen to classical music'</li></ul> | |
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| product faq | <ul><li>'What is the price of the Temple Butidaar Multi Color Border Pure Silk Chiffon Georgette Saree?'</li><li>'Do you have the Air Jordan 1 Low Shadow Brown/Brown Kelp- Sail in size 7?'</li><li>'Is the lakadong turmeric powder available for purchase?'</li></ul> | |
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| order tracking | <ul><li>'What is the expected delivery time for the 10 pack of Cake Boxes to Bhopal?'</li><li>'What is the delivery status for my order placed using email address [email protected]?'</li><li>'I havent received my order'</li></ul> | |
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| product policy | <ul><li>'What is the policy for returning a product that was part of a Cyber Monday sale?'</li><li>'Are there any exceptions to the return policy for items that were purchased with a special occasion promotion?'</li><li>'Are there any restrictions on returning sneakers with added fur or fur trim?'</li></ul> | |
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| product discoverability | <ul><li>'Suggest me some high ankle sneakers'</li><li>'Do you have any grocery & gourmet honey available?'</li><li>'Do you have any sneaker collaborations with artists?'</li></ul> | |
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| general faq | <ul><li>'How many cups of green tea should I drink daily to achieve the recommended therapeutic dosage of ECGC?'</li><li>'what is mashru silk'</li><li>'What specific compounds in Green Tea contribute to its antioxidant properties?'</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** | 0.8667 | |
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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("setfit_model_id") |
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# Run inference |
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preds = model("Are there any sarees with Fekwa Weave technique?") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 4 | 11.1737 | 28 | |
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| Label | Training Sample Count | |
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|:------------------------|:----------------------| |
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| Out of Scope | 35 | |
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| general faq | 24 | |
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| order tracking | 34 | |
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| product discoverability | 40 | |
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| product faq | 40 | |
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| product policy | 40 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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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 | 0.256 | - | |
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| 0.0213 | 50 | 0.2639 | - | |
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| 0.0425 | 100 | 0.2341 | - | |
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| 0.0638 | 150 | 0.0407 | - | |
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| 0.0851 | 200 | 0.0698 | - | |
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| 0.1063 | 250 | 0.014 | - | |
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| 0.1276 | 300 | 0.0069 | - | |
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| 0.1489 | 350 | 0.0099 | - | |
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| 0.1701 | 400 | 0.0014 | - | |
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| 0.1914 | 450 | 0.0007 | - | |
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| 0.2127 | 500 | 0.0006 | - | |
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| 0.2339 | 550 | 0.0005 | - | |
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| 0.2552 | 600 | 0.0006 | - | |
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| 0.2765 | 650 | 0.0005 | - | |
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| 0.2977 | 700 | 0.0002 | - | |
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| 0.3190 | 750 | 0.0005 | - | |
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| 0.3403 | 800 | 0.0003 | - | |
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| 0.3615 | 850 | 0.0003 | - | |
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| 0.3828 | 900 | 0.0002 | - | |
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| 0.4041 | 950 | 0.0003 | - | |
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| 0.4254 | 1000 | 0.0002 | - | |
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| 0.4466 | 1050 | 0.0002 | - | |
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| 0.4679 | 1100 | 0.0001 | - | |
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| 0.4892 | 1150 | 0.0002 | - | |
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| 0.5104 | 1200 | 0.0002 | - | |
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| 0.5317 | 1250 | 0.0001 | - | |
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| 0.5530 | 1300 | 0.0002 | - | |
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| 0.5742 | 1350 | 0.0002 | - | |
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| 0.5955 | 1400 | 0.0001 | - | |
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| 0.6168 | 1450 | 0.0002 | - | |
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| 0.6380 | 1500 | 0.0002 | - | |
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| 0.6593 | 1550 | 0.0001 | - | |
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| 0.6806 | 1600 | 0.0001 | - | |
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| 0.7018 | 1650 | 0.0001 | - | |
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| 0.7231 | 1700 | 0.0001 | - | |
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| 0.7444 | 1750 | 0.0001 | - | |
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| 0.7656 | 1800 | 0.0001 | - | |
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| 0.7869 | 1850 | 0.0001 | - | |
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| 0.8082 | 1900 | 0.0001 | - | |
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| 0.8294 | 1950 | 0.0001 | - | |
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| 0.8507 | 2000 | 0.0001 | - | |
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| 0.8720 | 2050 | 0.0001 | - | |
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| 0.8932 | 2100 | 0.0001 | - | |
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| 0.9145 | 2150 | 0.0002 | - | |
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| 0.9358 | 2200 | 0.0002 | - | |
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| 0.9570 | 2250 | 0.0002 | - | |
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| 0.9783 | 2300 | 0.0001 | - | |
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| 0.9996 | 2350 | 0.0001 | - | |
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| 1.0208 | 2400 | 0.0001 | - | |
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| 1.0421 | 2450 | 0.0002 | - | |
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| 1.0634 | 2500 | 0.0001 | - | |
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| 1.0846 | 2550 | 0.0001 | - | |
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| 1.1059 | 2600 | 0.0001 | - | |
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| 1.1272 | 2650 | 0.0002 | - | |
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| 1.1484 | 2700 | 0.0001 | - | |
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| 1.1697 | 2750 | 0.0001 | - | |
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| 1.1910 | 2800 | 0.0001 | - | |
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| 1.2123 | 2850 | 0.0001 | - | |
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| 1.2335 | 2900 | 0.0001 | - | |
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| 1.2548 | 2950 | 0.0001 | - | |
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| 1.2761 | 3000 | 0.0001 | - | |
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| 1.2973 | 3050 | 0.0001 | - | |
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| 1.3186 | 3100 | 0.0001 | - | |
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| 1.3399 | 3150 | 0.0001 | - | |
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| 1.3611 | 3200 | 0.0001 | - | |
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| 1.3824 | 3250 | 0.0001 | - | |
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| 1.4037 | 3300 | 0.0001 | - | |
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| 1.4249 | 3350 | 0.0001 | - | |
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| 1.4462 | 3400 | 0.0001 | - | |
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| 1.4675 | 3450 | 0.0001 | - | |
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| 1.4887 | 3500 | 0.0001 | - | |
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| 1.5100 | 3550 | 0.0001 | - | |
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| 1.5313 | 3600 | 0.0001 | - | |
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| 1.5525 | 3650 | 0.0001 | - | |
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| 1.5738 | 3700 | 0.0001 | - | |
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| 1.5951 | 3750 | 0.0001 | - | |
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| 1.6163 | 3800 | 0.0001 | - | |
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| 1.6376 | 3850 | 0.0 | - | |
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| 1.6589 | 3900 | 0.0001 | - | |
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| 1.6801 | 3950 | 0.0001 | - | |
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| 1.7014 | 4000 | 0.0001 | - | |
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| 1.7227 | 4050 | 0.0001 | - | |
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| 1.7439 | 4100 | 0.0001 | - | |
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| 1.7652 | 4150 | 0.0001 | - | |
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| 1.7865 | 4200 | 0.0001 | - | |
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| 1.8077 | 4250 | 0.0001 | - | |
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| 1.8290 | 4300 | 0.0001 | - | |
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| 1.8503 | 4350 | 0.0001 | - | |
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| 1.8715 | 4400 | 0.0 | - | |
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| 1.8928 | 4450 | 0.0001 | - | |
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| 1.9141 | 4500 | 0.0001 | - | |
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| 1.9353 | 4550 | 0.0001 | - | |
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| 1.9566 | 4600 | 0.0001 | - | |
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| 1.9779 | 4650 | 0.0001 | - | |
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| 1.9991 | 4700 | 0.0001 | - | |
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### Framework Versions |
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- Python: 3.10.16 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.40.2 |
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- PyTorch: 2.2.2 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
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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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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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