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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: part-of-speech ( pos ) tagging is a fundamental language analysis task---part-of-speech |
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( pos ) tagging is a fundamental nlp task , used by a wide variety of applications |
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- text: the two baseline methods were implemented using scikit-learn in python---the |
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models were implemented using scikit-learn module |
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- text: semantic parsing is the task of converting a sentence into a representation |
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of its meaning , usually in a logical form grounded in the symbols of some fixed |
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ontology or relational database ( cite-p-21-3-3 , cite-p-21-3-4 , cite-p-21-1-11 |
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)---for this language model , we built a trigram language model with kneser-ney |
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smoothing using srilm from the same automatically segmented corpus |
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- text: the results show that our model can clearly outperform the baselines in terms |
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of three evaluation metrics---for the extractive or abstractive summaries , we |
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use rouge scores , a metric used to evaluate automatic summarization performance |
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, to measure the pairwise agreement of summaries from different annotators |
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- text: language models were built with srilm , modified kneser-ney smoothing , default |
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pruning , and order 5---the language model used was a 5-gram with modified kneserney |
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smoothing , built with srilm toolkit |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-TinyBERT-L6-v2 |
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--- |
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# SetFit with sentence-transformers/paraphrase-TinyBERT-L6-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-TinyBERT-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-TinyBERT-L6-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-TinyBERT-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-TinyBERT-L6-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:** 128 tokens |
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- **Number of Classes:** 2 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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| 0 | <ul><li>'the defacto standard metric in machine translation is bleu---from character representations , we propose to generate vector representations of entire tweets from characters in our tweet2vec model'</li><li>'arabic is a highly inflectional language with 85 % of words derived from trilateral roots ( alfedaghi and al-anzi 1989 )---chen et al derive bilingual subtree constraints with auto-parsed source-language sentences'</li><li>'labeling of sentence boundaries is a necessary prerequisite for many natural language processing tasks , including part-of-speech tagging and sentence alignment---we have proposed a model for video description which uses neural networks for the entire pipeline from pixels to sentences'</li></ul> | |
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| 1 | <ul><li>'in this paper , we present a comprehensive analysis of the relationship between personal traits and brand preferences---in previous research , in this study , we want to systematically investigate the relationship between a comprehensive set of personal traits and brand preferences'</li><li>'the 50-dimensional pre-trained word embeddings are provided by glove , which are fixed during our model training---we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training'</li><li>'we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings , which we do not optimize during training---we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings'</li></ul> | |
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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("whateverweird17/parasci3_1") |
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# Run inference |
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preds = model("the two baseline methods were implemented using scikit-learn in python---the models were implemented using scikit-learn module") |
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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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## 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 | 27 | 35.8125 | 54 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 8 | |
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| 1 | 8 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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- num_epochs: (10, 10) |
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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, 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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| 0.025 | 1 | 0.1715 | - | |
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| 1.25 | 50 | 0.0028 | - | |
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| 2.5 | 100 | 0.0005 | - | |
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| 3.75 | 150 | 0.0002 | - | |
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| 5.0 | 200 | 0.0003 | - | |
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| 6.25 | 250 | 0.0001 | - | |
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| 7.5 | 300 | 0.0002 | - | |
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| 8.75 | 350 | 0.0001 | - | |
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| 10.0 | 400 | 0.0001 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.1 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.33.0 |
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- PyTorch: 2.0.0 |
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- Datasets: 2.16.0 |
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- Tokenizers: 0.13.3 |
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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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