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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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- f1 |
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- precision |
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- recall |
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widget: |
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- text: ' I''ll pay $1,000 if anyone can find a published study that ChatGPT confirms |
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merely attempts to refute the OPV AIDS theory without desperately resorting to |
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a pathetic strawman. |
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' |
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- text: my disappointment is immeasurable and my day is ruined. any idea if they will |
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ever fix it or is it just permanent? i feel like just wow man just freaking wow |
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- text: The stuff chatgpt gives is entirely too scripted *and* impractical, which |
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is what I'm trying to avoid :/ |
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- text: 'my experience with product product and brand: it''s amazing and not a bit |
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scary. despite the articles about product''s personality, my experience shows |
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the opposite: it''s useful, friendly, and truly amazing technology.' |
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- text: product is a massive energy hog. have a bunch of tabs open and your computer |
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will come to a crawl. also, ad blocking is terrible on product company ads) because |
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product apparently has a "whitelist" of ads that it refuses to be blocked. company |
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is way better |
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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: 0.5192307692307693 |
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name: Accuracy |
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- type: f1 |
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value: |
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- 0.2641509433962264 |
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- 0.1553398058252427 |
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- 0.6593406593406593 |
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name: F1 |
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- type: precision |
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value: |
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- 0.1590909090909091 |
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- 0.09090909090909091 |
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- 0.9375 |
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name: Precision |
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- type: recall |
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value: |
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- 0.7777777777777778 |
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- 0.5333333333333333 |
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- 0.5084745762711864 |
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name: Recall |
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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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<!-- - **License:** 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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| peak | <ul><li>" I used Word on Microsoft 10 on my laptop to type up my manuscript, and when I uploaded it onto KDP, it was automatically formatted perfectly for Kindle E-book. I didn't need to make any adjustments (thankfully)."</li><li>'feeling myself getting obsessed with/addicted to ChatGPT and the entire generative AI universe and its evolution. \n\ndelightful to have another really big, seemingly biggest yet tech to go deep on and obsess over and think about implications of for the foreseeable future'</li><li>'1/2 obsidian translate amazing plugin currently in beta. it can translate text in to multiple languages using multiple services. i just hooked it up to a free product translation account, and i am stunned by its accuracy. tft'</li></ul> | |
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| pit | <ul><li>"Looks like I got a new Microsoft 365 update last night. Now when I go to Options or Print, I crash. It's happening on multiple files. Probably other issues too, but haven't experimented much beyond that. Windows 11 and, obviously, the most up-to-date PPT. Fortunately I don't need PowerPoint right now - except to answer questions here - so I guess I'll just stick it out to see what happens before I do a repair/reinstall. Update: Quick repair didn't work. Full repair that I believe is a full reinstall didn't work."</li><li>'my disappointment is immeasurable and my day is ruined. any idea if they will ever fix it or is it just permanent? i feel like just wow man just freaking wow'</li><li>'between 100 pages of the packet devoted to some crumbly looking old house and the powerpoint about the importance of the military industrial complex, this meeting has me feeling hostile.'</li></ul> | |
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| neither | <ul><li>" Elevate your game with these mind-blowing ChatGPT prompts! \n\nWhether you're diving into knowledge, refining your skills, or making decisions, let be your guide to excellence. \n\nReady to unlock the power of AI? \n\n "</li><li>"As an alternative you can always use Ask Sage ( Basically the gov version of ChatGPT and allowed to be used for CUI. It's what I use on NMCI and I've never had any problems!"</li><li>" I'll pay $1,000 if anyone can find a published study that ChatGPT confirms merely attempts to refute the OPV AIDS theory without desperately resorting to a pathetic strawman.\n\n"</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | F1 | Precision | Recall | |
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|:--------|:---------|:-------------------------------------------------------------|:--------------------------------------------------|:-------------------------------------------------------------| |
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| **all** | 0.5192 | [0.2641509433962264, 0.1553398058252427, 0.6593406593406593] | [0.1590909090909091, 0.09090909090909091, 0.9375] | [0.7777777777777778, 0.5333333333333333, 0.5084745762711864] | |
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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("tjmooney98/725_test_model") |
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# Run inference |
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preds = model("The stuff chatgpt gives is entirely too scripted *and* impractical, which is what I'm trying to avoid :/") |
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``` |
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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 | 18 | 38.0667 | 91 | |
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| Label | Training Sample Count | |
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|:--------|:----------------------| |
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| pit | 5 | |
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| peak | 5 | |
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| neither | 5 | |
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### Training Hyperparameters |
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- batch_size: (5, 5) |
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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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- 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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| 0.0333 | 1 | 0.1809 | - | |
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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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