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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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datasets: |
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- Kevinger/hub-report-dataset |
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metrics: |
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- accuracy |
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widget: |
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- text: 'FOXBOROUGH — With Bill Belichick gone and no clear heir to his personnel |
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throne in New England, it remains murky who will have final say on the roster |
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as the offseason gets rolling. |
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At Jerod Mayo’s introductory press conference, Robert Kraft said it’d be collaborative |
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approach for now, but sought to debunk the idea that ownership will be more involved. |
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He said his family will continue to delegate to the football operations staff |
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as they have since purchasing the team in 1994. |
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BET ANYTHING GET $250 BONUS ESPN BET CLAIM OFFER MASS 21+ and present in MA, NJ, |
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PA, VA, MD, WV, TN, LA, KS, KY, CO, AZ, IL, IA, IN, OH, MI. Gambling problem? |
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“It will be the same input that we’ve had for the last three decades: We try to |
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hire the best people we can find and let them do their job and hold them accountable,” |
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Kraft said. “If you get involved and tell them what to do or try to influence |
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them, you can’t hold them responsible and have them accountable. It’ll be within |
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the people’s discretion who are the decision makers to do it, and if we’ve hired |
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the wrong people, then we’ll have to make a change. But we’re going to try to |
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enjoy it as fans.” |
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Kraft said there’s only one situation where ownership will get involved in football |
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ops, and that’s when it comes off-the-field issues. |
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“The only area that we have really weighed in is when it comes to bringing in |
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people that we might think are not the right character to be here and they have |
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done things in their past,” Kraft said. “That’s the only time we’ve really weighed |
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in.”' |
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- text: 'My mom loved Christmas so much, she would sometimes leave the tree up until |
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April. |
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She dyed a sheet blue for the sky behind the crèche and made a star of tin foil. |
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The cradle would stay empty until Christmas morning; when we tumbled downstairs, |
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the baby would be in his place, and the house would smell of roasting turkey. |
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Mom always took it personally if you didn’t wear red or green on Christmas, and |
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she signed all the presents “Love, Baby Jesus,” “Love, Virgin Mary” or “Love, |
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St. Joseph.” |
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(My brother Kevin was always upset that Joseph got short shrift, disappearing |
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from the Bible; why wasn’t he around to boast about Jesus turning water into wine?) |
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We went to midnight Mass back then, and it was magical, despite some boys wearing |
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Washington Redskins bathrobes as they carried presents down the aisle for Baby |
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Jesus.' |
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- text: 'It is the first time that this food-dunking behavior has been documented |
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in parrots — it has also been observed in grackles and crows. And it was a serendipitous |
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discovery for the lab, which typically relies on meticulously planned experiments |
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to test the cockatoos’ renowned problem-solving skills. “But sometimes we get |
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gifted with accidental things that just happen,” Dr. Auersperg said. |
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Goffin’s cockatoos are known for their ability to use and manipulate objects. |
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In earlier studies, Dr. Auersperg and her colleagues found, for instance, that |
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the birds could open locked puzzle boxes and make their own tools to obtain out-of-reach |
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food. |
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But the researchers at the Goffin Lab did not typically pay close attention to |
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the birds’ behavior at lunch, said Jeroen Zewald, a doctoral student in the lab |
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and another author of the study. Until, one day last summer, they noticed something |
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curious. An affectionate male bird named Pipin — “the gentleman of the group,” |
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Mr. Zewald said — was dunking his food into the tub of water typically used for |
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drinking and bathing. Two other birds in the lab, Kiwi and Muki, turned out to |
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be dunkers, too, the researchers noticed. |
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To study the behavior more systematically, Mr. Zewald and Dr. Auersperg spent |
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12 days observing the birds’ lunchtime behaviors. In total, seven of the 18 birds |
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were observed dunking food at least once, they found. (Still, Pipin, Kiwi and |
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Muki were the undisputed dunkmasters, racking up many more “dunking events” than |
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the other birds.)' |
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- text: 'SAN FRANCISCO — Celtics fans held their breath midway through Tuesday’s game |
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against the Warriors. C’s star Jayson Tatum appeared to roll his left ankle as |
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he hobbled back to the locker room with 7:45 left in the first quarter. |
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On the bright side, Tatum retreated to the Celtics locker room under his own power. |
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Tatum appeared to step on the Warriors’ Brandin Podziemski shoe midway through |
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play. Fortunately, it didn’t look like there was too much force on the play as |
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Tatum went to the locker room after twisting his ankle. Here’s a look at the play. |
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BET ANYTHING GET $250 BONUS ESPN BET CLAIM OFFER MASS 21+ and present in MA, NJ, |
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PA, VA, MD, WV, TN, LA, KS, KY, CO, AZ, IL, IA, IN, OH, MI. Gambling problem? |
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Call 1-800-Gambler. |
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Up to that point, Tatum had put up four points, two rebounds and one assist on |
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2-for-3 shooting in four minutes. |
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The injury didn’t appear serious initially, and that was indeed the case. Tatum |
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returned to the Celtics bench with 2:19 left in the first quarter, walking under |
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his own power and without a limp. Tatum made his return to the game to start the |
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second quarter. |
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This story will be updated.' |
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- text: 'A new episode of “Love After Lockup” will air on Friday, Jan. 12 on WE Tv |
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at 9 p.m. ET. |
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The new episode can also be streamed live on Philo, DirecTV Stream and fuboTV. |
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All platforms offer a free trial for those interested in signing up for an account. |
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“Love After Lockup” is said to be a spin off from WE Tv’s “Love During Lockup” |
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as couples navigate their love lives through prison. The show will show inmates |
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struggle to keep their love through video dates, letters and phone calls. But |
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there’s no telling who can and can’t handle the cell wall that separates the couples. |
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In the new episode, “Tayler confronts Chance; Melissa reveals secret surgery plans. |
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Tensions flare as Kerok seeks Bri’s family’s acceptance. Shavel’s shower explodes |
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as the mothers-in-law face off again. Mike comes clean; Blaine’s confession sends |
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Lindsay spiraling.” |
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How can I watch if I don’t have cable? |
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If you don’t have access to cable television, you can stream “Love After Lockup” |
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on streaming platforms Philo, DirecTV Stream and fuboTV. |
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What is Philo? |
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Philo is an over-the-top internet live TV streaming service that offers 60+ entertainment |
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and lifestyle channels for the budget-friendly price of $25/month. |
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If you purchase a product or register for an account through one of the links |
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on our site, we may receive compensation. |
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What is FuboTV? |
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FuboTV is an over-the-top internet live TV streaming service that offers more |
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than 100 channels, such as sports, news, entertainment and local channels. |
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What is DirecTV Stream? |
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The streaming platform offers a plethora of content including streaming the best |
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of live and On Demand, starting with more than 75 live TV channels.' |
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pipeline_tag: text-classification |
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inference: false |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-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: Kevinger/hub-report-dataset |
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type: Kevinger/hub-report-dataset |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.5086206896551724 |
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name: Accuracy |
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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 trained on the [Kevinger/hub-report-dataset](https://huggingface.co/datasets/Kevinger/hub-report-dataset) dataset 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 OneVsRestClassifier 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 OneVsRestClassifier instance |
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- **Maximum Sequence Length:** 512 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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- **Training Dataset:** [Kevinger/hub-report-dataset](https://huggingface.co/datasets/Kevinger/hub-report-dataset) |
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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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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.5086 | |
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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("Kevinger/setfit-hub-multilabel-example") |
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# Run inference |
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preds = model("My mom loved Christmas so much, she would sometimes leave the tree up until April. |
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She dyed a sheet blue for the sky behind the crèche and made a star of tin foil. The cradle would stay empty until Christmas morning; when we tumbled downstairs, the baby would be in his place, and the house would smell of roasting turkey. |
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Mom always took it personally if you didn’t wear red or green on Christmas, and she signed all the presents “Love, Baby Jesus,” “Love, Virgin Mary” or “Love, St. Joseph.” |
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(My brother Kevin was always upset that Joseph got short shrift, disappearing from the Bible; why wasn’t he around to boast about Jesus turning water into wine?) |
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We went to midnight Mass back then, and it was magical, despite some boys wearing Washington Redskins bathrobes as they carried presents down the aisle for Baby Jesus.") |
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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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### 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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## 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 | 12 | 526.0625 | 6633 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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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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| 0.0031 | 1 | 0.1691 | - | |
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| 0.1562 | 50 | 0.0678 | - | |
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| 0.3125 | 100 | 0.0949 | - | |
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| 0.4688 | 150 | 0.0083 | - | |
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| 0.625 | 200 | 0.0048 | - | |
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| 0.7812 | 250 | 0.0011 | - | |
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| 0.9375 | 300 | 0.0005 | - | |
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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: 2.3.1 |
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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: 0.15.1 |
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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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