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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: Outcome Of Board Meeting Of Mahindra & Mahindra Limited Held On 4Th August, |
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2023 |
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- text: Board Meeting Intimation for Considering And Taking On Record The Audited |
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Standalone And Unaudited Consolidated Financial Results Of The Company For The |
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Quarter And Nine Months Ended December 31, 2022. |
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- text: 'Board Meeting Intimation for Intimation Regarding Holding Of Meeting Of The |
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Board Of Directors: - Un-Audited Financial Results For The Quarter Ended June |
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30, 2023' |
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- text: Report Of Auditors On Financial Statements For The Quarter Ended September |
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30 2031 With UDIN |
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- text: Infosys Unveils New AI-Powered Solutions for Enhanced Customer Experience |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/all-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: 0.9557522123893806 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-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:** 384 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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### 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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## Lables |
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0-press release/advertisement/newspaper publication<br> |
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1-business updates/strategic announcemet/clarification sought<br> |
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2-Investor meetings/board meeting<br> |
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3-earnings call transcript<br> |
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4-esop/esps<br> |
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5-violation/litigation/penalty<br> |
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6-auditors report/result<br> |
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7-research<br> |
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8-resignation<br> |
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| Label | Examples | |
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|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 0 | <ul><li>'Announcement under Regulation 30 (LODR)-Press Release / Media Release'</li><li>'Media Release By The Company'</li><li>'Clarification on Market Rumors Regarding Product Recall'</li></ul> | |
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| 1 | <ul><li>'Corporate Insolvency Resolution Process (CIRP)-Updates - Corporate Insolvency Resolution Process (CIRP)'</li><li>'Notice Of Record Date For Bonus Issue'</li><li>"Update To Disclosure Under Regulation 30 Of SEBI (Listing Obligations And Disclosure Requirements) Regulations, 2015 - Resolution Plan Jointly Submitted By Reliance Industries Limited And Assets Care & Reconstruction Enterprise Limited For The Resolution Of Sintex Industries Limited, Approved By Hon'Ble National Company Law Tribunal, Ahmedabad Bench"</li></ul> | |
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| 2 | <ul><li>'Board Meeting - Un-Audited Financial Results For The Quarter Ended June 30, 2023'</li><li>'Board Meeting Outcome for Interim Dividend For The Financial Year 2022-23'</li><li>'Board Meeting Intimation for Board Meeting - 3Rd February, 2023'</li></ul> | |
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| 3 | <ul><li>'Earnings Call - Intimation'</li><li>'Presentation On Earnings Call Update - Consolidated And Standalone Audited Financial Results Of The Bank For The Financial Year Ended March 31, 2023'</li></ul> | |
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| 4 | <ul><li>'An official announcement under SEBI (LODR) has been made declaring the notification of the record date for ESOP Holders and Shareholders post the successful completion of the Amalgamation between XYZ Systems Ltd and our Company.'</li><li>'An official announcement under Regulation 30 (LODR) has been released concerning the successful merger of Quantum Software Solutions Limited with the company.'</li><li>'Grant Of Stock Options Under The Employee Stock Option Scheme Of The Bank (ESOP Scheme).'</li></ul> | |
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| 5 | <ul><li>'Intimation Regarding Change in Compliance Officer Under Regulation 30 Of SEBI (Listing Obligations and Disclosure Requirements) Regulations'</li><li>'Disclosure Under Regulation 30 Of SEBI LODR Regulations (Merger or Demerger)'</li><li>'Regulation 30 Of The SEBI (Listing Obligations And Disclosure Requirements) Regulations 2015: Disclosure Of Appointment of Key Managerial Personnel'</li></ul> | |
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| 6 | <ul><li>'Statement Of Unaudited Standalone And Consolidated Financial Results Of The Company For The Quarter And Nine Months Ended 31St December, 2022'</li><li>'Unaudited Financial Results'</li><li>'Statement Of Audited Standalone And Consolidated Financial Results Of The Company For The Quarter And Year Ended 31St March, 2023'</li></ul> | |
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| 7 | <ul><li>"Energizing Change: Infosys-HFS Research Unveils Companies' Top 3 Priorities in the Energy Transition Era,"</li><li>'Infosys Rated A Leader In Multicloud Managed Services Providers And Cloud Migration And Managed Service Partners By Independent Research Firm'</li><li>'Cloud For Organizational Growth And Transformation Is Three Times More Important Than Cloud For Cost Optimization: Infosys Research'</li></ul> | |
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| 8 | <ul><li>'Resignation Of Smt. Nita M. Ambani From The Board Of The Company - Disclosure Dated August 28'</li><li>'Announcement under Regulation 30 (LODR)-Resignation of Head of Customer Relations'</li><li>'Announcement under Regulation 30 (LODR)-Resignation of Head of Human Resources'</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** | 0.9558 | |
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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("krish2505/setfitmkrt2") |
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# Run inference |
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preds = model("Infosys Unveils New AI-Powered Solutions for Enhanced Customer Experience") |
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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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### Recommendations |
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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 | 1 | 14.7272 | 50 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 142 | |
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| 1 | 134 | |
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| 2 | 298 | |
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| 3 | 66 | |
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| 4 | 43 | |
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| 5 | 53 | |
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| 6 | 202 | |
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| 7 | 34 | |
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| 8 | 36 | |
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### Training Hyperparameters |
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- batch_size: (64, 64) |
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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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- 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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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0016 | 1 | 0.1754 | - | |
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| 0.0794 | 50 | 0.0917 | - | |
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| 0.1587 | 100 | 0.0534 | - | |
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| 0.2381 | 150 | 0.0521 | - | |
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| 0.3175 | 200 | 0.0352 | - | |
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| 0.3968 | 250 | 0.0062 | - | |
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| 0.4762 | 300 | 0.0159 | - | |
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| 0.5556 | 350 | 0.0151 | - | |
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| 0.6349 | 400 | 0.0207 | - | |
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| 0.7143 | 450 | 0.0129 | - | |
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| 0.7937 | 500 | 0.0186 | - | |
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| 0.8730 | 550 | 0.0083 | - | |
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| 0.9524 | 600 | 0.002 | - | |
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| 1.0317 | 650 | 0.0081 | - | |
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| 1.1111 | 700 | 0.0263 | - | |
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| 1.1905 | 750 | 0.0118 | - | |
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| 1.2698 | 800 | 0.0196 | - | |
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| 1.3492 | 850 | 0.011 | - | |
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| 1.4286 | 900 | 0.0153 | - | |
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| 1.5079 | 950 | 0.0015 | - | |
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| 1.5873 | 1000 | 0.0156 | - | |
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| 1.6667 | 1050 | 0.0215 | - | |
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| 1.7460 | 1100 | 0.0022 | - | |
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| 1.8254 | 1150 | 0.003 | - | |
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| 1.9048 | 1200 | 0.0033 | - | |
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| 1.9841 | 1250 | 0.0155 | - | |
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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: 4.36.2 |
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- PyTorch: 2.0.0 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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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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