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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: and importance of the climate crisis requires everyone to play their part. |
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- text: The Group has unused tax losses carried forward of 512m, primarily UK capital |
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losses, on which no deferred tax is recognised. |
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- text: If an acquirer of shares is not prepared to provide this declaration, the |
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Board may refuse to register him as a shareholder with the right to vote. |
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- text: The Company will also make every effort to improve the effectiveness of its |
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sustainability reporting. |
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- text: The Company maintains sufficient liquidity and has a variety of contingent |
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liquidity resources to manage liquidity across a range of economic scenarios. |
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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: 0.9571788413098237 |
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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:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co./datasets/unknown) --> |
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<!-- - **Language:** 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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| 0.0 | <ul><li>'The concentration of our sales in our fourth fiscal quarter increases this impact as the revenue impact of most fourth fiscal quarter subscription sales will not be realized until the following fiscal year.'</li><li>'The deposit insurance fund of the FDIC insures deposit accounts in HomeTrust Bank up to 250,000 per separately insured deposit ownership right or category.'</li><li>'On 19 October 2020 the Company entered into a 25 million revolving credit facility agreement with State Street Bank International GmbH.'</li></ul> | |
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| 1.0 | <ul><li>'The incredible rise in the price of fuel and emission allowances shaped the trajectory of our most important wholesale electricity markets in Europe.'</li><li>'As part of this process we understood our impact linked to home working as a new material source of carbon emissions.'</li><li>'Per SASB Industry Standard (October 2018) for Iron Steel Producers, the percentage of water recycled is calculated as the volume, in thousands of cubic meters, recycled divided by the volume of water withdrawn.'</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** | 0.9572 | |
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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("mitra-mir/setfit-model-ESG-environmental") |
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# Run inference |
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preds = model("and importance of the climate crisis requires everyone to play their part.") |
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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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### 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 | 4 | 24.5578 | 112 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0.0 | 149 | |
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| 1.0 | 50 | |
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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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- l2_weight: 0.01 |
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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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| 0.0020 | 1 | 0.3928 | - | |
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| 0.1004 | 50 | 0.1952 | - | |
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| 0.2008 | 100 | 0.0054 | - | |
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| 0.3012 | 150 | 0.0004 | - | |
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| 0.4016 | 200 | 0.0003 | - | |
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| 0.5020 | 250 | 0.0002 | - | |
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| 0.6024 | 300 | 0.0002 | - | |
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| 0.7028 | 350 | 0.0001 | - | |
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| 0.8032 | 400 | 0.0001 | - | |
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| 0.9036 | 450 | 0.0001 | - | |
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### Framework Versions |
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- Python: 3.11.6 |
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- SetFit: 1.1.0 |
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- Sentence Transformers: 3.2.1 |
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- Transformers: 4.43.4 |
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- PyTorch: 2.4.1+cu121 |
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- Datasets: 3.0.1 |
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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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