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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: Ruukki Group calculates that it has lost EUR 4mn in the failed project . |
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- text: The Tecnomen Convergent Charging solution includes functionality for prepaid |
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and post-paid billing , charging and rating of voice calls , video calls , raw |
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data traffic and any type of content services in both mobile and fixed networks |
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. |
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- text: The combined value of the planned investments is about EUR 30mn . |
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- text: The Diameter Protocol is developed according to the standards IETF RFC 3588 |
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and IETF RFC 3539 . |
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- text: Below are unaudited consolidated results for Aspocomp Group under IFRS reporting |
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standards . |
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pipeline_tag: text-classification |
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inference: true |
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base_model: BAAI/bge-small-en-v1.5 |
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model-index: |
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- name: SetFit with BAAI/bge-small-en-v1.5 |
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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: 0.9426048565121413 |
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name: Accuracy |
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--- |
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# SetFit with BAAI/bge-small-en-v1.5 |
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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 [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
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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:** 3 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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| positive | <ul><li>'HELSINKI ( AFX ) - Nokian Tyres reported a fourth quarter pretax profit of 61.5 mln eur , up from 48.6 mln on the back of strong sales .'</li><li>'Equity ratio was 60.9 % compared to 54.2 % In the third quarter of 2007 , net sales of the Frozen Foods Business totaled EUR 11.0 , up by about 5 % from the third quarter of 2006 .'</li><li>"`` After a long , unprofitable period the Food Division posted a profitable result , which speaks of a healthier cost structure and a new approach in business operations , '' Rihko said ."</li></ul> | |
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| neutral | <ul><li>'Their names have not yet been released .'</li><li>'The contract includes design , construction , delivery of equipment , installation and commissioning .'</li><li>"Tieto 's service is also used to send , process and receive materials related to absentee voting ."</li></ul> | |
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| negative | <ul><li>'The company confirmed its estimate for lower revenue for the whole 2009 than the year-ago EUR93 .9 m as given in the interim report on 5 August 2009 .'</li><li>'Acando AB ( ACANB SS ) fell 8.9 percent to 13.35 kronor , the lowest close since Dec. 11 .'</li><li>'Okmetic expects its net sales for the first half of 2009 to be less than in 2008 .'</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** | 0.9426 | |
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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("moshew/bge-small-en-v1.5-SetFit-FSA") |
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# Run inference |
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preds = model("The combined value of the planned investments is about EUR 30mn .") |
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``` |
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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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<!-- |
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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 | 2 | 22.4020 | 60 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| negative | 266 | |
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| neutral | 1142 | |
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| positive | 403 | |
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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: 10 |
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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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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0004 | 1 | 0.2832 | - | |
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| 0.0221 | 50 | 0.209 | - | |
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| 0.0442 | 100 | 0.1899 | - | |
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| 0.0663 | 150 | 0.1399 | - | |
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| 0.0883 | 200 | 0.1274 | - | |
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| 0.1104 | 250 | 0.0586 | - | |
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| 0.1325 | 300 | 0.0756 | - | |
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| 0.1546 | 350 | 0.0777 | - | |
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| 0.1767 | 400 | 0.0684 | - | |
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| 0.1988 | 450 | 0.0311 | - | |
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| 0.2208 | 500 | 0.0102 | - | |
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| 0.2429 | 550 | 0.052 | - | |
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| 0.2650 | 600 | 0.0149 | - | |
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| 0.2871 | 650 | 0.1042 | - | |
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| 0.3092 | 700 | 0.061 | - | |
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| 0.3313 | 750 | 0.0083 | - | |
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| 0.3534 | 800 | 0.0036 | - | |
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| 0.3754 | 850 | 0.002 | - | |
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| 0.3975 | 900 | 0.0598 | - | |
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| 0.4196 | 950 | 0.0036 | - | |
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| 0.4417 | 1000 | 0.0027 | - | |
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| 0.4638 | 1050 | 0.0617 | - | |
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| 0.4859 | 1100 | 0.0015 | - | |
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| 0.5080 | 1150 | 0.0022 | - | |
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| 0.5300 | 1200 | 0.0016 | - | |
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| 0.5521 | 1250 | 0.0009 | - | |
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| 0.5742 | 1300 | 0.0013 | - | |
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| 0.5963 | 1350 | 0.0009 | - | |
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| 0.6184 | 1400 | 0.0015 | - | |
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| 0.6405 | 1450 | 0.0018 | - | |
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| 0.6625 | 1500 | 0.0015 | - | |
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| 0.6846 | 1550 | 0.0018 | - | |
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| 0.7067 | 1600 | 0.0016 | - | |
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| 0.7288 | 1650 | 0.0022 | - | |
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| 0.7509 | 1700 | 0.0013 | - | |
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| 0.7730 | 1750 | 0.0108 | - | |
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| 0.7951 | 1800 | 0.0016 | - | |
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| 0.8171 | 1850 | 0.0021 | - | |
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| 0.8392 | 1900 | 0.002 | - | |
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| 0.8613 | 1950 | 0.0015 | - | |
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| 0.8834 | 2000 | 0.0016 | - | |
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| 0.9055 | 2050 | 0.0028 | - | |
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| 0.9276 | 2100 | 0.0013 | - | |
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| 0.9496 | 2150 | 0.0019 | - | |
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| 0.9717 | 2200 | 0.0075 | - | |
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| 0.9938 | 2250 | 0.0015 | - | |
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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: 2.5.1 |
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- Transformers: 4.38.1 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.18.0 |
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- Tokenizers: 0.15.2 |
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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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