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Commit
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Add SetFit model

Browse files
1_Pooling/config.json ADDED
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README.md ADDED
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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: Chapman hits winning double as Blue Jays complete sweep of Red Sox with 3-2
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+ victory
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+ - text: Opinion | The Election No One Seems to Want Is Coming Right at Us
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+ - text: How to watch The Real Housewives of Miami new episode free Jan. 10
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+ - text: Vitamin Sea Brewing set to open 2nd brewery and taproom in Mass.
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+ - text: Opinion | When the World Feels Dark, Seek Out Delight
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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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: 0.7060702875399361
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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+
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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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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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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:** 9 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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+
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+ ### Model Sources
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+
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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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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 3 | <ul><li>'A Reinvented True Detective Plays It Cool'</li><li>"It's owl season in Massachusetts. Here's how to spot them"</li><li>'Taylor Swift class at Harvard: Professor needs to hire more teaching assistants'</li></ul> |
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+ | 6 | <ul><li>'Springfield Mayor Domenic Sarno tests positive for COVID-19'</li><li>'How to Take Care of Your Skin in the Fall and Winter'</li><li>'Subbing plant-based milk for dairy options is a healthy decision'</li></ul> |
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+ | 2 | <ul><li>'Mattel Has a New Cherokee Barbie. Not Everyone Is Happy About It.'</li><li>'Who Is Alan Garber, Harvards Interim President?'</li><li>'Springfield Marine training in Japan near Mount Fuji (Photos)'</li></ul> |
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+ | 0 | <ul><li>'Heres which Northampton businesses might soon get all-alcohol liquor licenses'</li><li>'People in Business: Jan. 15, 2024'</li><li>'Come Home With Memories, Not a Shocking Phone Bill'</li></ul> |
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+ | 7 | <ul><li>'3 Patriots vs. Chiefs predictions'</li><li>'Tuskegee vs. Alabama State How to watch college football'</li><li>'WMass Boys Basketball Season Stats Leaders: Who leads the region by class?'</li></ul> |
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+ | 8 | <ul><li>'Biting Cold Sweeping U.S. Hits the South With an Unfamiliar Freeze'</li><li>'Some Sunday storms and sun - Boston News, Weather, Sports'</li><li>'More snow on the way in Mass. on Tuesday with slippery evening commute'</li></ul> |
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+ | 4 | <ul><li>'title'</li><li>'This sentence is label'</li><li>'This sentence is label'</li></ul> |
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+ | 1 | <ul><li>'Two cars crash through former Boston Market in Saugus'</li><li>'U.S. Naval Officer Who Helped China Is Sentenced to 2 Years in Prison'</li><li>'American Airlines flight attendant arrested after allegedly filming teenage girl in bathroom on flight to Boston - Boston News, Weather, Sports'</li></ul> |
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+ | 5 | <ul><li>'Opinion | Why Wasnt DeSantis the Guy?'</li><li>'Reports Say Pope Francis Is Evicting U.S. Cardinal From His Vatican Home'</li><li>'Biden Says Its Self-Evident That Trump Supported an Insurrection'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.7061 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("Kevinger/setfit-hub-report")
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+ # Run inference
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+ preds = model("Opinion | When the World Feels Dark, Seek Out Delight")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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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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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ <!--
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+ ### Recommendations
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+
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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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+
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+ ## Training Details
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+
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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 | 7.2993 | 21 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 16 |
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+ | 1 | 16 |
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+ | 2 | 16 |
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+ | 3 | 16 |
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+ | 4 | 9 |
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+ | 5 | 16 |
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+ | 6 | 16 |
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+ | 7 | 16 |
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+ | 8 | 16 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 2)
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+ - num_epochs: (1, 16)
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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: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0010 | 1 | 0.3619 | - |
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+ | 0.0481 | 50 | 0.097 | - |
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+ | 0.0962 | 100 | 0.0327 | - |
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+ | 0.1442 | 150 | 0.0044 | - |
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+ | 0.1923 | 200 | 0.0013 | - |
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+ | 0.2404 | 250 | 0.0011 | - |
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+ | 0.2885 | 300 | 0.001 | - |
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+ | 0.3365 | 350 | 0.0008 | - |
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+ | 0.3846 | 400 | 0.001 | - |
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+ | 0.4327 | 450 | 0.0006 | - |
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+ | 0.4808 | 500 | 0.0008 | - |
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+ | 0.5288 | 550 | 0.0005 | - |
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+ | 0.5769 | 600 | 0.0012 | - |
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+ | 0.625 | 650 | 0.0005 | - |
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+ | 0.6731 | 700 | 0.0006 | - |
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+ | 0.7212 | 750 | 0.0004 | - |
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+ | 0.7692 | 800 | 0.0005 | - |
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+ | 0.8173 | 850 | 0.0005 | - |
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+ | 0.8654 | 900 | 0.0006 | - |
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+ | 0.9135 | 950 | 0.0014 | - |
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+ | 0.9615 | 1000 | 0.0006 | - |
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+
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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: 2.2.2
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+ - Transformers: 4.35.2
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+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.16.1
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+ - Tokenizers: 0.15.0
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+
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+ ## Citation
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+
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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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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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