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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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- absa |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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base_model: BAAI/bge-small-en-v1.5 |
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metrics: |
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- accuracy |
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widget: |
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- text: mine employment is starker at mid-:The future of coal mine employment is starker |
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at mid-century. |
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- text: are considered relatively noisy - a major:But the Type 094s, which carry China's |
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most advanced submarine-launched JL-3 missile, are considered relatively noisy |
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- a major handicap for military submarines. |
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- text: March this year raised an objection, saying that:Initially, the registrar's |
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office of the IHC in March this year raised an objection, saying that the high |
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court was not an appropriate forum and asked the petitioner to approach the relevant |
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authorities. |
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- text: mine closures is West Bengal.:Runa Sarkar, a professor at the Indian Institute |
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of Management Calcutta, said the coal mining region most affected by mine closures |
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is West Bengal. |
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- text: FIA DG to proceed in accordance with the law.:When the petition was heard |
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by the chief justice, he asked the FIA DG to proceed in accordance with the law. |
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pipeline_tag: text-classification |
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inference: false |
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model-index: |
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- name: SetFit Polarity Model 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: 0.7065217391304348 |
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name: Accuracy |
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--- |
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# SetFit Polarity Model 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 Aspect Based Sentiment Analysis (ABSA). 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. In particular, this model is in charge of classifying aspect polarities. |
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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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This model was trained within the context of a larger system for ABSA, which looks like so: |
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1. Use a spaCy model to select possible aspect span candidates. |
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2. Use a SetFit model to filter these possible aspect span candidates. |
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3. **Use this SetFit model to classify the filtered aspect span candidates.** |
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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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- **spaCy Model:** en_core_web_lg |
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- **SetFitABSA Aspect Model:** [asadnaqvi/setfitabsa-aspect](https://huggingface.co/asadnaqvi/setfitabsa-aspect) |
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- **SetFitABSA Polarity Model:** [asadnaqvi/setfitabsa-polarity](https://huggingface.co/asadnaqvi/setfitabsa-polarity) |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 4 classes |
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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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| Informative | <ul><li>"The upcoming visit of Saudi Arabia:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"'s crown prince Mohammed bin Salman (MBS):The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>'to burnish his legitimacy after the international:The trip to India is evidently timed to burnish his legitimacy after the international opprobrium that followed the murder of The Washington Post journalist Jamal Khashoggi.'</li></ul> | |
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| Negative | <ul><li>'that followed the murder of The Washington:The trip to India is evidently timed to burnish his legitimacy after the international opprobrium that followed the murder of The Washington Post journalist Jamal Khashoggi.'</li><li>"Arabia's disastrous military intervention in Yemen or:India for its part has refrained from even a hint of disapproval of Saudi Arabia's disastrous military intervention in Yemen or its misguided attempts to isolate Qatar, never mind the brutal assassination of Khashoggi."</li><li>'condemn the Soviet invasion but privately urged:India sought to adopt a more nuanced stance; it did not openly condemn the Soviet invasion but privately urged Moscow to pull back.'</li></ul> | |
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| Positive | <ul><li>"in fostering stronger relations with countries in:Prime Minister Narendra Modi's government has invested considerable time and energy in fostering stronger relations with countries in West Asia."</li><li>"has invested considerable time and energy in fostering stronger:Prime Minister Narendra Modi's government has invested considerable time and energy in fostering stronger relations with countries in West Asia."</li><li>"security and economic ties with Saudi Arabia:Modi's visit to Riyadh in 2016 gave a fillip to security and economic ties with Saudi Arabia."</li></ul> | |
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| Ambivalent | <ul><li>"a hint of disapproval of Saudi Arabia:India for its part has refrained from even a hint of disapproval of Saudi Arabia's disastrous military intervention in Yemen or its misguided attempts to isolate Qatar, never mind the brutal assassination of Khashoggi."</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** | 0.7065 | |
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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 AbsaModel |
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# Download from the 🤗 Hub |
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model = AbsaModel.from_pretrained( |
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"asadnaqvi/setfitabsa-aspect", |
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"asadnaqvi/setfitabsa-polarity", |
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) |
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# Run inference |
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preds = model("The food was great, but the venue is just way too busy.") |
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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 | 11 | 27.7071 | 45 | |
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| Label | Training Sample Count | |
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|:------------|:----------------------| |
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| Ambivalent | 1 | |
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| Informative | 73 | |
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| Negative | 20 | |
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| Positive | 5 | |
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### Training Hyperparameters |
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- batch_size: (128, 128) |
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- num_epochs: (5, 5) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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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: True |
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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: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:----------:|:------:|:-------------:|:---------------:| |
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| 0.0217 | 1 | 0.2599 | - | |
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| **1.0870** | **50** | **0.0608** | **0.3526** | |
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| 2.1739 | 100 | 0.0253 | 0.4091 | |
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| 3.2609 | 150 | 0.0159 | 0.4497 | |
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| 4.3478 | 200 | 0.0035 | 0.4437 | |
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* The bold row denotes the saved checkpoint. |
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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: 2.7.0 |
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- spaCy: 3.7.4 |
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- Transformers: 4.40.1 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.19.0 |
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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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