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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: 'Please Find Enclosed The Press Release Titled ''Energy Transition Among The |
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Top 3 Priorities For 73 Percent Of Companies: Infosys-HFS Research Study''' |
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- text: Financial Results For The Quarter Ended June 30, 2023, And Declaration Of |
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Interim Dividend |
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- text: successfully started |
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- text: Board Meeting Intimation for Notice Of The Board Meeting Dt. August 03, 2023 |
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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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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: 0.8807339449541285 |
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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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### 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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| 2 | <ul><li>'Board Meeting Outcome for Board Meeting - Unaudited Financial Results For The Quarter And Nine Months Ended December 31, 2022'</li><li>'Board Meeting Outcome for Outcome Of Board Meeting Held On 20Th July, 2023'</li><li>'Board Meeting Outcome for Financial Results For The Fourth Quarter (Q4) And Year Ended March 31, 2023 And Recommendation Of Dividend'</li></ul> | |
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| 6 | <ul><li>'Results - Financial Results For Quarter And Nine Months Ended December 31, 2022'</li><li>"Updated Independent Auditor'S Report On The Consolidated Financial Statements As At And For The Year Ended March 31, 2023, Prepared Under Indian Accounting Standards, Issued On April 13, 2023"</li><li>'Financial Results For The Quarter And Nine Month Period Ended December 31, 2022 And Declaration Of Third Interim Dividend'</li></ul> | |
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| 5 | <ul><li>'Regulation 30 Of The SEBI (Listing Obligations And Disclosure Requirements) Regulations 2015: Disclosure Of Change in Accounting Policies'</li><li>'Regulation 30 Of The SEBI (Listing Obligations And Disclosure Requirements) Regulations 2015: Disclosure Of Appointment of Key Managerial Personnel'</li><li>'Regulation 30 Of The SEBI (Listing Obligations And Disclosure Requirements) Regulations 2015: Disclosure Of Change in Listing Status'</li></ul> | |
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| 3 | <ul><li>'Earnings Call For Q1 And Half-Yearly Financial Results - FY 2023'</li><li>'Earnings Call Of ABC Holdings - Emerging Markets Perspective'</li><li>'Audio / Video Recording - Earnings Call - Technology and Innovation Highlights'</li></ul> | |
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| 0 | <ul><li>'Transcripts of Town Hall Meeting with Stakeholders'</li><li>'Clarification on Market Rumors Regarding Product Recall'</li><li>'Media Release By Reliance Jio Infocomm Limited'</li></ul> | |
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| 1 | <ul><li>"Order Passed By The Hon'Ble National Company Law Tribunal, Mumbai Bench, Sanctioning The Scheme Of Arrangement Between Reliance Projects & Property Management Services Limited And Its Shareholders And Creditors & Reliance Industries Limited And Its Shareholders And Creditors ('Scheme') - Further Update"</li><li>'Update To The Disclosure Dated August 23, 2023 On Investment By Qatar Holding LLC In Reliance Retail Ventures Limited, A Subsidiary Of The Company'</li><li>'Announcement under Regulation 30 (LODR)-Updates on Acquisition'</li></ul> | |
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| 7 | <ul><li>'Cloud For Organizational Growth And Transformation Is Three Times More Important Than Cloud For Cost Optimization: Infosys Research'</li><li>'Infosys Rated A Leader In Multicloud Managed Services Providers And Cloud Migration And Managed Service Partners By Independent Research Firm'</li><li>'Infosys Collaborates with Leading Universities for Research and Development'</li></ul> | |
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| 4 | <ul><li>'In accordance with SEBI (LODR) regulations an intimation has been officially conveyed regarding the record date for Shareholders and ESOP Holders of NNL following the approval of the Merger Scheme by the National Company Law Tribunal Chennai Bench.'</li><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>'Grant Of Stock Options Under The Employee Stock Option Scheme Of The Bank (ESOP Scheme).'</li></ul> | |
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| 8 | <ul><li>'Announcement under Regulation 30 (LODR)-Resignation of Head of Marketing'</li><li>'Resignation Of Shri Rajesh B. Ambani From The Board Of The Company - Disclosure Dated September 5'</li><li>'Announcement under Regulation 30 (LODR)-Resignation of Chief Operating Officer (COO)'</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** | 0.8807 | |
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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/setfitmkrt") |
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# Run inference |
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preds = model("successfully started") |
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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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## 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 | 15.0265 | 70 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 142 | |
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| 1 | 130 | |
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| 2 | 310 | |
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| 3 | 61 | |
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| 4 | 42 | |
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| 5 | 61 | |
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| 6 | 191 | |
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| 7 | 6 | |
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| 8 | 38 | |
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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 | 0.1833 | - | |
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| 0.0814 | 50 | 0.125 | - | |
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| 0.1629 | 100 | 0.0628 | - | |
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| 0.2443 | 150 | 0.0361 | - | |
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| 0.3257 | 200 | 0.0333 | - | |
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| 0.4072 | 250 | 0.0116 | - | |
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| 0.4886 | 300 | 0.0253 | - | |
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| 0.5700 | 350 | 0.0231 | - | |
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| 0.6515 | 400 | 0.0037 | - | |
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| 0.7329 | 450 | 0.0144 | - | |
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| 0.8143 | 500 | 0.0095 | - | |
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| 0.8958 | 550 | 0.0161 | - | |
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| 0.9772 | 600 | 0.0104 | - | |
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| 1.0586 | 650 | 0.0064 | - | |
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| 1.1401 | 700 | 0.0018 | - | |
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| 1.2215 | 750 | 0.0107 | - | |
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| 1.3029 | 800 | 0.0035 | - | |
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| 1.3844 | 850 | 0.0056 | - | |
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| 1.4658 | 900 | 0.0142 | - | |
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| 1.5472 | 950 | 0.014 | - | |
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| 1.6287 | 1000 | 0.0109 | - | |
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| 1.7101 | 1050 | 0.0252 | - | |
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| 1.7915 | 1100 | 0.0093 | - | |
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| 1.8730 | 1150 | 0.0048 | - | |
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| 1.9544 | 1200 | 0.0063 | - | |
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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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