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--- |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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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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widget: |
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- text: 'My wife is a horder. she spends hours each week sorting through her piles, |
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moving them from one room to another, looking through things and trying to find |
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things that have been lost. I''ve tried to tell her that if you don''t have all |
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this crap you don''t have to take time to move and remove it. But it doesn''t |
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do any good. I feel bad for her as her stuff rules her life. Yesterday i found |
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a bowl of batteries that she is saving because she can''t find her battery tester |
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to tell which batteries are good or not. i wanted to buy a new battery tester |
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so we could test the batteries and throw out the dead ones, but she said why buy |
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a tester when she knows that she has one somewhere, she just has to find it. This |
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is typical. I keep my office clean and retreat in there, although she occasionally |
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will "clean up" by throwing her things into my office just "temporarily" and |
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gets mad at me when i move them back. i love her, I hate her crap. |
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|
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' |
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- text: 'If we were having two commercial airplane crashes per day that killed 500 |
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people and we hadn’t figured a way to stop it after three years, we wouldn’t just |
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declare the emergency over and pretend that is the new normal. The emergency with |
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covid isn’t over, we have simply given up and surrendered to the virus. |
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' |
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- text: 'The article might have noted that 59 members of the German military died |
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on active service in Afghanistan, with 245 WIA.It had also taken part in the NATO |
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war in Kosovo in 1999. This included Luftwaffe aircraft bombing Belgrade (very |
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ironically). |
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' |
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- text: 'Jen that was a prop plane.in Buffalo....but still awfulAlso there was a Delta |
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jet accident in 2006 on kentucky ...the plane took of on the wrong runway...49 |
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killed |
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|
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' |
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- text: 'To make a blanket statement that most juveniles who sexually abuse rarely |
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abuse as adults does an extreme disservice to the questioner, your readers, and |
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to the limited but complex research on the topic. This research does not definitively |
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support your claim. Remember that, as in this case, most cases of sexual abuse |
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by juveniles goes unreported. Studies asking adult abusers about their juvenile |
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actions, in fact, indicate the opposite of your claim. See <a href="https://smart.ojp.gov/somapi/chapter-3-recidivism-juveniles-who-commit-sexual-offenses" |
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target="_blank">https://smart.ojp.gov/somapi/chapter-3-recidivism-juveniles-who-commit-sexual-offenses</a>Unfortunately, |
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family denial denies children treatment, denies the system accurate statistics, |
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research, and informed approaches to treatment, and denies betrothed people information |
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and conversations that could prevent the secret generational continuation of sexual |
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abuse.If the sister-in-law had been able to share her secret with your questioner, |
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family repercussions would likely have been severe. There are so many reasons |
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abuse survivors do not speak out. This kind of enforced secrecy allows child sexual |
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abuse to flourish. Still, sharing with her sister-in-law-to-be could have led |
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to valuable discussions and possibly delayed treatment for the man who had abused |
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his sister as a child. Perhaps it still can. |
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|
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' |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/all-mpnet-base-v2 |
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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: 1.0 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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:** 384 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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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| yes | <ul><li>'40K Americans a year die in auto accidents. Countless more die from cancer, guns, suicide. Many of these deaths are preventable but you can still buy cigarettes and guns. Yesterday Ken Block died while snowmobiling -- he is most famous for his automotive exploits, either driving in rally stages or in gymkhana stunt driving videos. Jeremy Renner was hit by a plow truck and airlifted to a hospital yesterday.You can look at anything Red Bull sponsors and see people BASE jumping, wearing squirrel suits flying through mountain passes, or free climbing at deathly heights. They even sponsored a parachute drop from space.Athletes have died while playing other sports that are seemingly "safe". Kids still play with aluminum bats. Hockey players routinely check each other. Men\'s lacrosse has pads and allows physical contact, not quite as much as football, but not far off, either. MMA leagues are very popular, too -- and feature men and women both. Not to mention boxing, which fills arenas with title matches.\n'</li><li>'My one and only flight on a 74 was from NYC to Amsterdam, Amsterdam to Dubai. We stopped in Amsterdam for 90 minutes while they did a security sweep, got on the same plane to Dubai. That plane barring unforeseen tragedy will fly on at least for 40 years.\n'</li><li>'That is a pretty darned low body count compared to independent estimates.Near the end of December it was reported that..."Around 9,000 people in China are probably dying each day from COVID-19, UK-based health data firm Airfinity "Based on a rate of 9k per day over the same period, the Chinese Government estimate should be closer to 315,000 deaths due to COVID\n'</li></ul> | |
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| no | <ul><li>'A cautionary story: My mother and my brother are both alcoholics. Several years apart they each suffered a traumatic brain injury while drinking. Within a few years, due to their brain injuries, they each developed vascular dementia. They now reside in the same care facility. It is endless sadness thinking of the lives they should be leading, and everything that has been lost to alcohol.\n'</li><li>"I've concluded that if states such as Mississippi, Alabama, Kentucky, Arkansas, Louisiana, South Carolina, Nebraska, et al, want to leave the Union, they should. And, yes, that includes Texas as well. If one looks at critical performance levels such as life expectancy, infant mortality, income, educational levels, access to health care and more (even voter participation levels), invariably this group ranks at the bottom. And, if one looks at federal spending levels, these states receive more than they contribute. Essentially, their ROI is terrible. Yet, what they have in their states is what they envision for the country as a whole. No, thanks. Let them go. I think that is what they'd prefer anyway. How they provide for their common defense and promote their own general welfare is up to them.\n"</li><li>'Unless the US gov\'t has borrowed euros or rubles or renminbi that we don\'t know about, it has zero "debt." What we\'re looking at is $31.5 trillion of ACCUMULATED DEFICITS expressed in US dollars. Why would the US Treasury/Federal Reserve ever have to borrow money that it can create at will out of thin air? And "debt" implies that we\'re going to pay it back, which is an absurd thought. Balances have to balance. If the public sector of our economy shows a deficit of $31.5 trillion, this means by force of logic that the private sector (plus the rest of the world) must show a SURPLUS of $31.5 trillion. That\'s our aggregate financial wealth. Trying to pay back the "debt" would drive us all into extreme poverty.BTW, it\'s one thing to say that the Fed is raising interest rates to fight inflation, and it\'s quite another thing (and incorrect) to claim the market is demanding higher rates. That is simply nonsense. The Fed sets interest rates and if you don\'t like it you can just go pound sand. The Fed doesn\'t sell bonds to "borrow" back the money it has just spent into the economy. It does so to remove excess currency from the banking system in order to meet its target interest rate. If it didn\'t, all banks would be flush with currency and never have to borrow from another bank to settle its accounts. This would drive interest rates down to zero.The notion that the gov\'t borrows its own currency and accumulates \'debt" is a zombie idea that just refuses to die.\n'</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** | 1.0 | |
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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("davidadamczyk/setfit-model-6") |
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# Run inference |
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preds = model("Jen that was a prop plane.in Buffalo....but still awfulAlso there was a Delta jet accident in 2006 on kentucky ...the plane took of on the wrong runway...49 killed |
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") |
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``` |
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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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*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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*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 | 9 | 127.2 | 277 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| no | 18 | |
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| yes | 22 | |
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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: 120 |
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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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- l2_weight: 0.01 |
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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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| 0.0017 | 1 | 0.4205 | - | |
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| 0.0833 | 50 | 0.1936 | - | |
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| 0.1667 | 100 | 0.0058 | - | |
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| 0.25 | 150 | 0.0003 | - | |
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| 0.3333 | 200 | 0.0002 | - | |
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| 0.4167 | 250 | 0.0001 | - | |
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| 0.5 | 300 | 0.0001 | - | |
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| 0.5833 | 350 | 0.0001 | - | |
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| 0.6667 | 400 | 0.0001 | - | |
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| 0.75 | 450 | 0.0001 | - | |
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| 0.8333 | 500 | 0.0001 | - | |
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| 0.9167 | 550 | 0.0001 | - | |
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| 1.0 | 600 | 0.0001 | - | |
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### Framework Versions |
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- Python: 3.10.13 |
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- SetFit: 1.1.0 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.45.2 |
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- PyTorch: 2.4.0+cu124 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.20.0 |
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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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