HelgeKn commited on
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Add SetFit model

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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: 'The Vitorian team knew to make up for the significant absences of Herrmann
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+ , Oleson , Huertas and Micov with a big dose of involvement and teamwork , even
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+ though it had to hold out until the end to take the victory . '
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+ - text: '`` But why pay her bills ? '
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+ - text: 'In the body , pemetrexed is converted into an active form that blocks the
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+ activity of the enzymes that are involved in producing nucleotides ( the building
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+ blocks of DNA and RNA , the genetic material of cells ) . '
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+ - text: '`` The daily crush of media tweets , cameras and reporters outside the courthouse
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+ , '''' the lawyers wrote , `` was unlike anything ever seen here in New Haven
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+ and maybe statewide . '''' '
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+ - text: 'However , in both studies , patients whose cancer was not affecting squamous
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+ cells had longer survival times if they received Alimta than if they received
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+ the comparator . '
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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: 0.16158940397350993
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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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 7 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>'There were relatively few cases reported of attempts to involve users in service planning but their involvement in service provision was found to be more common . '</li><li>"At St. Mary 's Church in Ilminster , Somerset , the bells have fallen silent following a dust-up over church attendance . "</li><li>'Treatment should be delayed or discontinued , or the dose reduced , in patients whose blood counts are abnormal or who have certain other side effects . '</li></ul> |
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+ | 6 | <ul><li>'If you were especially helpful in a corrupt scheme you received not just cash in a bag , but equity . '</li><li>"Moreover , conservatives argue that it 's Justice Elena Kagan who has an ethical issue , not Scalia and Thomas . "</li><li>'No one speaks , and the snaking of the ropes seems to make as much sound as the bells themselves , muffled by the ceiling . '</li></ul> |
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+ | 2 | <ul><li>'In and around all levels of government in the U.S. are groups of people who can best be described as belonging to a political insider commercial party . '</li><li>'The report and a casebook of initiatives will be published in 1996 and provide the backdrop for a conference to be staged in Autumn , 1996 . '</li><li>'This building shook like hell and it kept getting stronger . '</li></ul> |
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+ | 0 | <ul><li>'For months the Johns Hopkins researchers , using gene probes , experimentally crawled down the length of chromosome 17 , looking for the smallest common bit of genetic material lost in all tumor cells . '</li><li>'It explains how the Committee for Medicinal Products for Human Use ( CHMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . '</li><li>'-- Most important of all , schools should have principals with a large measure of authority over the faculty , the curriculum , and all matters of student discipline . '</li></ul> |
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+ | 5 | <ul><li>': = : It is used to define a variable value . '</li><li>'I could also see the clouds across the bay from the horrible fire in the Marina District of San Francisco . '</li><li>'The man with the clipboard represented a halfhearted attempt to introduce a bit of les sportif into our itinerary . '</li></ul> |
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+ | 4 | <ul><li>"First , why ticket splitting has increased and taken the peculiar pattern that it has over the past half century : Prior to the election of Franklin Roosevelt as president and the advent of the New Deal , government occupied a much smaller role in society and the prisoner 's dilemma problem confronting voters in races for Congress was considerably less severe . "</li><li>'The second quarter was more of the same , but the Alavan team opted for the inside game of Barac and the work of Eliyahu , who was greeted with whistles and applause at his return home , to continue increasing their lead by half-time ( 34-43 ) . '</li><li>'In 2005 , the fear of invasion of the national territory by hordes of Polish plumbers was felt both on the Left and on the Right . '</li></ul> |
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+ | 1 | <ul><li>'`` Progressive education `` ( as it was once called ) is far more interesting and agreeable to teachers than is disciplined instruction . '</li><li>"Ringing does become a bit of an obsession , `` admits Stephanie Pattenden , master of the band at St. Mary Abbot and one of England 's best female ringers . "</li><li>"He says the neighbors complain , but I do n't believe it . "</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** | 0.1616 |
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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("HelgeKn/SemEval-multi-label-v2")
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+ # Run inference
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+ preds = model("`` But why pay her bills ? ")
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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 | 6 | 25.8929 | 75 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 8 |
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+ | 1 | 8 |
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+ | 2 | 8 |
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+ | 3 | 8 |
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+ | 4 | 8 |
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+ | 5 | 8 |
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+ | 6 | 8 |
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+
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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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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0071 | 1 | 0.2758 | - |
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+ | 0.3571 | 50 | 0.1622 | - |
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+ | 0.7143 | 100 | 0.0874 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.9.13
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+ - SetFit: 1.0.1
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+ - Sentence Transformers: 2.2.2
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+ - Transformers: 4.36.0
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+ - PyTorch: 2.1.1+cpu
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+ - Datasets: 2.15.0
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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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