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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: As someone on the line between Millenial and GenZ, yeah. Bars are expensive |
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and loud, and ubers home are expensive. It's a lot more reasonable to pool a bit |
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of money, throw some food on a grill, and buy our own booze. We don't have the |
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disposable income to hang out at bars regularly. |
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- text: When we switch main focus from college football to college basketball, I can |
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report back on Collier. But I'll be interested to see what the guys who really |
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crunch tape on draft prospects say as these seasons progress. I know theres more |
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than a few here in the sub. A huge 3 with skills would be fun to stack next to |
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Wemby though. |
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- text: The gen Z kids I see are more risk averse in general, because exposure to |
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a lifetime on the internet has taught them that one mistake can ruin their lives. |
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It always blows my mind when boomers and Xers like me wonder why kids have such |
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high anxiety these days. It’s because they are regularly exposed to the judgement |
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and horrors of the world around them. We were raised in a protective bubble mentally, |
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in comparison |
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- text: Well I guess I would expect this from a beer garden but I totally agree, those |
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vibes don’t belong at Coachella |
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- text: Can Earned the Brewery Pioneer (Level 6) badge! Earned the I Believe in IPA! |
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(Level 5) badge! |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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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:** 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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| 1 | <ul><li>'I don’t drink much but I like wine tasting. We usually buy local wine to take to dinners and such as we rarely drink wine. (NGL, I am a beer drinker so I’m probably just pleb.)'</li><li>'Never do the bottom right 2 again, give you major banker/tech bro adult frat boy vibes. I can see you chugging a beer and talking about bitcoin with those looks. Upper left makes you look younger and great.'</li><li>'NGL I like pepsi much more than coke. I dunno why.'</li></ul> | |
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| 2 | <ul><li>'?? angolbryggeri - Hazy Crazy\n\n✴️ IPA\n\n?? Sweden ????\n\n??Abv 6.5%\n\n⭐️ 3.60 / 5.0 ~ avg 3.67\n\n?? systembolaget\n\n#beer #bier #birra #öl #cerveza #øl #craftbeer #ipa #dipa #tipa #sour #gose #berlinerweisse #paleale #pilsner #lager #stout #beeroftheday #beerphotografy #hantverksöl #untappd #beergeek #beerlover #ilovebeer #cheers #beerstagram #instabeer #beerporn #ängöl #sweden'</li><li>"I'm a feast kind of guy Bring out the roast pig and Flagons of ale"</li><li>'“Just grab me a beer” legend'</li></ul> | |
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| 0 | <ul><li>"My boys (Aged 20 and 26) have moved out so I can't say what they do in their own homes but when they lived with us they were supper straight laced and had no desire to explore Alcohol or Drugs. They were into Gaming or Sports not Partying. Weed is Legal here and as far as I know they are not into that either. They definitely don't smoke, maybe they do Gummies but that would be about it."</li><li>"Like you said cost is a big one. Plus I just wonder if younger generations might not be into it as much. I can't remember the beer company, but one is talking about making a non alcoholic drink, since the younger generation aren't drinking beer as much. "</li><li>'She just graduated and I know they drink occasionally, but it’s all Mike’s Lemonade and White Claw city. Very tame stuff. Her friend group also experimented with that fake pot stuff, I forget the name. I told her I wasn’t okay with that and I’d buy her actual pot (rec is legal in my state) if she was determined to try it, but they apparently all lost interest.'</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("bhaskars113/guinness-segments-model") |
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# Run inference |
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preds = model("Can Earned the Brewery Pioneer (Level 6) badge! Earned the I Believe in IPA! (Level 5) badge!") |
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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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--> |
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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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--> |
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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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--> |
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<!-- |
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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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--> |
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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 | 6 | 45.7143 | 135 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 14 | |
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| 1 | 14 | |
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| 2 | 14 | |
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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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- 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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| 0.0095 | 1 | 0.2908 | - | |
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| 0.4762 | 50 | 0.0394 | - | |
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| 0.9524 | 100 | 0.0021 | - | |
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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: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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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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