Split (1)

train · 850k rows

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RichardsonTXCarpetCleaning/AreaRugCleaningRichardsonTX	RichardsonTXCarpetCleaning	2022-12-11T08:27:50Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T08:27:16Z	--- license: other --- Area Rug Cleaning Richardson TX https://carpetcleaning-richardson.com/area-rug-cleaning.html (972) 454-9815 Do you need the best cleaning services in town from Rug Shampooers?Do you want to bring back the natural beauty of your rugs after they have lost their original appearance?By simply calling our professionals, Richardson TX Carpet Cleaning will be able to properly clean them for you, leaving them looking good and brightening up your home at any time.
RichardsonTXCarpetCleaning/CarpetStainRemovalRichardsonTX	RichardsonTXCarpetCleaning	2022-12-11T08:26:40Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T08:25:49Z	--- license: other --- Carpet Stain Removal Richardson TX https://carpetcleaning-richardson.com/carpet-stain-removal.html (972) 454-9815 One of the reasons our carpet stain cleaning is so popular with customers is that it is eco-friendly.Our products are safe for the home, pets, and children.We are able to quickly clean tough stains that you believe are permanent and cannot be removed from your carpet.You will quickly observe the disappearance of what you thought was a stain that would not go away.
RichardsonTXCarpetCleaning/RichardsonTXCarpetCleaning	RichardsonTXCarpetCleaning	2022-12-11T08:25:12Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T08:24:15Z	--- license: other --- Richardson TX Carpet Cleaning https://carpetcleaning-richardson.com/ (972) 454-9815 Pets are outlandish, and generally they are tomfoolery, and that is the explanation a large portion of us keep them. Notwithstanding, usually now and again they wreck in the house and right on the costly rug or carpet. A specialist from Richardson Texas Pet Stain Cleaning prescribes that it's fundamental to have the stain eliminated right away and inappropriate or lacking pet stain evacuation can set the color for all time and any further stain can harm your carpet completely or significantly more peeing can cause the scent that appears never to disappear.
CarpetCleaningAddisonTexas/CarpetCleaningAddisonTexas	CarpetCleaningAddisonTexas	2022-12-11T08:19:17Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T08:18:47Z	--- license: other --- Carpet Cleaning Addison Texas http://carpetcleaningaddison.com/ (972) 379-7364 Private floor covering cleaners will go to your home when expected to give you various administrations, for example, cover stain expulsion, profound rug cleaning, and one end to the other rug cleaning. A few stains become long-lasting sooner or later, particularly on the off chance that it isn't treated with the fitting medication. Sometime these neglected rug stains will be left everlastingly discernibly on the floor and nobody needs to see an undesirable stain destroying the picture of your exquisite home.Sometimes picking higher standards when in doubt is the correct approach. Our medicines have been tried and evaluated #1 for the most ideal outcomes that anyone could hope to find. Cover Cleaning Addison Texas is consistently on first in class with the most recent tests and updates for all important rug medicines, we are 100 percent sure that our tried cleaning items which have set us in the number 1 position will leave with only totally fulfilled.
luigisaetta/whisper-medium-it	luigisaetta	2022-12-11T08:19:08Z	18	2	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "whisper-event", "it", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-08T18:00:42Z	--- language: - it license: apache-2.0 tags: - generated_from_trainer - whisper-event datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: luigisaetta/whisper-medium-it results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 it type: mozilla-foundation/common_voice_11_0 config: it split: test args: it metrics: - name: Wer type: wer value: 5.7191 --- # luigisaetta/whisper-medium-it This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1452 - Wer: 5.7191 ## Model description This model is a fine-tuning of the OpenAI Whisper Medium model, on the specified dataset. ## Intended uses & limitations This model has been developed as part of the Hugging Face Whisper Fine Tuning sprint, December 2022. It is meant to spread the knowledge on how these models are built and can be used to develop solutions where it is needed ASR on the Italian Language. It has not been extensively tested. It is possible that on other datasets the accuracy will be lower. Please, test it before using it. ## Training and evaluation data Trained and tested on Mozilla Common Voice, vers. 11 ## Training procedure The script run.sh, and the Python file, used for the training are saved in the repository. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1216 \| 0.2 \| 1000 \| 0.2289 \| 10.0594 \| \| 0.1801 \| 0.4 \| 2000 \| 0.1851 \| 7.6593 \| \| 0.1763 \| 0.6 \| 3000 \| 0.1615 \| 6.5258 \| \| 0.1337 \| 0.8 \| 4000 \| 0.1506 \| 6.0427 \| \| 0.0742 \| 1.05 \| 5000 \| 0.1452 \| 5.7191 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
GreenCarpetCleaningGarland/GreenCarpetCleaningGarland	GreenCarpetCleaningGarland	2022-12-11T08:12:46Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T08:12:22Z	--- license: other --- Green Carpet Cleaning Garland http://garlandcarpetcleaner.com/ (972) 256-8544 One of methods we follow at cover cleaning is "Steam Cleaning Administration" that depends on utilizing minimal high temp water and more steam, centering steam - which infiltrating into profound on spots and stain to dissolve every one of them even the hardest ones and kill all poisons from your rug. Then, at that point, the job of our compelling green items starts to clear this large number of components, returning your floor covering shimmered and bright. At last, we utilize our excellent dry machines, so your rug will be full dry inside no time. We have specific floor covering steam cleaners, so they know how to follow the high amazing skill simultaneously, safeguarding your rug from any harms.
CarpetCleaningMesquiteTX/DryerVentCleaningMesquiteTX	CarpetCleaningMesquiteTX	2022-12-11T08:01:27Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T08:01:08Z	--- license: other --- Dryer Vent Cleaning Mesquite TX http://mesquitecarpetcleaningtx.com/dryer-vent-cleaning.html (469) 213-8132 When you wash a lot each week, your dryer often works very hard to dry your clothes.It is safe to assume that your dry uses a lot of electricity in your home because it is used constantly.
CarpetCleaningMesquiteTX/AirDuctCleaningMesquiteTX	CarpetCleaningMesquiteTX	2022-12-11T08:00:43Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T08:00:17Z	--- license: other --- Air Duct Cleaning Mesquite TX http://mesquitecarpetcleaningtx.com/air-duct-cleaning.html (469) 213-8132 Cleaning the air ducts is very important.We ensure that your carpets, tile flooring, and rugs are kept clean and in good condition.We can deal with a variety of heater and air conditioner cleaning issues in addition to cleaning air ducts.Your air ducts can be cleaned quickly and inexpensively of dust and debris.No matter how big or small the job is, our team of certified and professionally trained technicians will complete it correctly.
CarpetCleaningMesquiteTX/TileGroutCleaningMesquiteTX	CarpetCleaningMesquiteTX	2022-12-11T07:59:54Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:59:32Z	--- license: other --- Tile Grout Cleaning Mesquite TX http://mesquitecarpetcleaningtx.com/tile-grout-cleaning.html (469) 213-8132 Your home is your very own castle, and you make every effort to keep it spotless and inviting at all times.However, you will discover that many tasks, including tile and grout cleaning, take up too much of your time.If you live in a house that is entirely tiled, you are aware that it is difficult to maintain the tiles' brightness and shine.
CarpetCleaningMesquiteTX/RugCleaningMesquiteTX	CarpetCleaningMesquiteTX	2022-12-11T07:58:08Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:57:46Z	--- license: other --- Rug Cleaning Mesquite TX http://mesquitecarpetcleaningtx.com/rug-cleaning.html (469) 213-8132 Carpet and area rug manufacturers recommend using the free hot water extraction system from Our Rug Cleaning.Carpet Cleaning Mesquite TX can also clean some area rugs at a lower temperature, depending on how many fibers they have. These rugs need to be cleaned with cool water routines.Using a high-controlled cleaning process and a deposit-free cleaning result, we remove all dirt, sand, coarseness, and grime from the area rugs.
CarpetCleaningMesquiteTX/CarpetCleaningMesquiteTX	CarpetCleaningMesquiteTX	2022-12-11T07:57:15Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:56:56Z	--- license: other --- Carpet Cleaning Mesquite TX http://mesquitecarpetcleaningtx.com/ (469) 213-8132 The most ideal way to discard these bugs is expert and master steam cleaning with a truck mount. Cover Cleaning Mesquite TX will give you the total cleaning Administration that you expect from truly capable administrators. Our cleaners assurance to constantly give total, compelling, high audit cover administration and cleaning all over Mesquite TX and its district. We have bewildering cleaning counselors who are accessible to return to work for cleaning administrations over the course of the day nearby.
CarpetCleaningMckinneyTX/CarpetCleaningMckinneyTX	CarpetCleaningMckinneyTX	2022-12-11T07:53:59Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:53:36Z	--- license: other --- Carpet Cleaning Mckinney TX https://carpetcleaningmckinneytx.com/ (469) 702-1202 Individuals search for elite administrations to keep their homes tidy and cutting-edge. We are certain about what we do in light of the fact that, we consolidate our long stretches of involvement in the cutting edge gear, drawing out the ideal outcome. For instance, our steam clean floor coverings technique guarantees the oil stains on your rug are for all time cleaned out with little water. Your rug will have insignificant drying time and be back on the floor quicker than expected.
FortWorthCarpetCleaning/UpholsteryCleaningFortWorthTX	FortWorthCarpetCleaning	2022-12-11T07:51:04Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:50:42Z	--- license: other --- Upholstery Cleaning Fort Worth TX https://txfortworthcarpetcleaning.com/upholstery-cleaning.html (817) 523-1237 When you sit on your upholstery, you inhale allergens, dirt, and dust that are trapped in its fibers.Therefore, if you want to ensure the safety of your upholstery—especially if you have children or pets—you need to hire experts in carpet cleaning for upholstery in Worth, Texas.We have the best upholstery cleaners who will come to your house and do an excellent job of cleaning it.Understanding the various fibers of your furniture is important to our technicians because it helps them choose effective and safe cleaning methods.When you hire us, we promise to give you a lot of attention and care, and we won't start cleaning your upholstery until we make sure the products we use are safe for the kind of fabric it is made of.
FortWorthCarpetCleaning/RugCleaningFortWorthTX	FortWorthCarpetCleaning	2022-12-11T07:49:51Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:49:30Z	--- license: other --- Rug Cleaning Fort Worth TX https://txfortworthcarpetcleaning.com/rug-cleaning.html (817) 523-1237 Carpet cleaning Fort Worth TX is nearby and able to provide you with professional cleaning services if you require an efficient and high-quality rug cleaning service.Simply contact our professionals, and your rug will regain its vibrant color and stunning appearance.We use products and equipment that enable us to provide you with the best results, such as rug shampooing, which enables us to restore your rug's beautiful appearance and the amazing scent that permeates your entire home.Call us for $20 off these services if you need them.
GreenCarpetCleaningGrandPrairie/GreenCarpetCleaningGrandPrairie	GreenCarpetCleaningGrandPrairie	2022-12-11T07:44:13Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:43:51Z	--- license: other --- Green Carpet Cleaning Grand Prairie https://grandprairiecarpetcleaningtx.com/ (214) 301-3659 We give Floor covering Stain Expulsion that utilizes harmless to the ecosystem items. We lead the way with regards to dealing with the climate. Every one of our items are natural and are great for the environment, yet additionally for your pets and youngsters.
seastar105/whisper-small-ko-zeroth	seastar105	2022-12-11T07:42:51Z	5	2	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "whisper-event", "ko", "dataset:kresnik/zeroth_korean", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-11T00:49:45Z	--- language: - ko license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer - whisper-event datasets: - kresnik/zeroth_korean metrics: - wer model-index: - name: Whisper Small Korean results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Zeroth Korean type: kresnik/zeroth_korean config: clean split: test args: 'split: test' metrics: - name: Wer type: wer value: 6.761029965366662 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Korean This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Zeroth Korean dataset. It achieves the following results on the evaluation set: - Loss: 0.0899 - Wer: 6.7610 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1277 \| 0.72 \| 1000 \| 0.1489 \| 12.2271 \| \| 0.0379 \| 1.44 \| 2000 \| 0.1053 \| 6.7159 \| \| 0.0138 \| 2.16 \| 3000 \| 0.0918 \| 6.0382 \| \| 0.0141 \| 2.87 \| 4000 \| 0.0899 \| 6.7610 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0a0+d0d6b1f - Datasets 2.7.1 - Tokenizers 0.13.2
CarpetCleaningPlanoTX/DryerVentCleaningPlanoTX	CarpetCleaningPlanoTX	2022-12-11T07:35:21Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:34:56Z	--- license: other --- Dryer Vent Cleaning Plano TX https://carpetcleaningplanotx.com/dryer-vent-cleaning.html ‪(469) 444-1903‬ It's best not to do electrical work at home if you don't have the knowledge, skills, or equipment.However, you may be concerned about the reason why your relatively new drying machine takes so long to dry your clothes.This service requirement will be met by our Dryer Vent Cleaners.You should soon be enjoying a machine that moves quickly.
CarpetCleaningPlanoTX/AirVentCleaningPlanoTX	CarpetCleaningPlanoTX	2022-12-11T07:34:27Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:34:07Z	--- license: other --- Air Vent Cleaning Plano TX https://carpetcleaningplanotx.com/air-vent-cleaning.html ‪(469) 444-1903‬ Cleaning air vents need not be difficult.Carpet Cleaning Plano in Texas is a team of experienced air vent cleaners who know how to do the job right.Professionals with certifications make up our team of technicians, who will arrive in our cutting-edge mobile cleaning units.
CarpetCleaningPlanoTX/AirDuctCleaningPlanoTX	CarpetCleaningPlanoTX	2022-12-11T07:33:31Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:33:09Z	--- license: other --- Air Duct Cleaning Plano TX https://carpetcleaningplanotx.com/air-duct-cleaning.html ‪(469) 444-1903‬ Airborne irritants are bad for your health, according to studies and other health research for a long time.Mold, pollen, and dust are examples.Your capacity to breathe is seriously impacted by these.Allergies and other respiratory issues are brought on by these pollutants.They may occasionally carry out attacks that can be fatal.What is the most important way to keep the air in your home, place of business, or place of business clean?It is cleaning air ducts.
CarpetCleaningPlanoTX/UpholsteryCleaningPlanoTX	CarpetCleaningPlanoTX	2022-12-11T07:31:41Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:31:20Z	--- license: other --- Upholstery Cleaning Plano TX https://carpetcleaningplanotx.com/upholstery-cleaning.html ‪(469) 444-1903‬ We remove stains from sofas.When you have a nice, comfortable sofa in your home, spills are common.On that new couch, game day weekends can be difficult.When they are excited about who is winning on the playing field, friends, family, and pets can cause havoc.After a party, upholstery cleaning is not a problem.We can arrive with our mobile unit, which simplifies the task.
CarpetCleaningPlanoTX/RugCleaningPlanoTX	CarpetCleaningPlanoTX	2022-12-11T07:30:50Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:30:22Z	--- license: other --- Rug Cleaning Plano TX https://carpetcleaningplanotx.com/rug-cleaning.html ‪(469) 444-1903‬ Put your carpets, rugs, and other cleaning needs at risk.Avoid immersing them in hazardous and wasteful chemical processes in particular.We use cutting-edge Green Rug Cleaners services at carpet cleaning Plano, Texas.Texas cannot match these.Rug cleaning is safe and good for the environment thanks to our cutting-edge washing technology.This will not harm your property or put your friends, family, or pets in danger.
CarpetCleaningPlanoTX/CarpetStainRemovalPlanoTX	CarpetCleaningPlanoTX	2022-12-11T07:29:56Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:29:29Z	--- license: other --- Carpet Stain Removal Plano TX https://carpetcleaningplanotx.com/carpet-stain-removal.html ‪(469) 444-1903‬ Carpet Cleaning Plano in Texas is the company of choice for the majority of customers when it comes to stain removal.We have the best-trained staff and professional technology.We will get rid of even the worst stain.That is if it comes from your upholstery, fabrics, curtains, and carpets.Try us out today, and you'll see why the majority of people prefer us to everyone else.
MaviBogaz/ppo-LunarLander-v2	MaviBogaz	2022-12-11T07:27:05Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-11T07:26:38Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 282.84 +/- 20.56 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
CandyCarpetCleaningIrving/DryerVentCleaningIrvingTX	CandyCarpetCleaningIrving	2022-12-11T07:22:36Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:21:49Z	--- license: other --- Dryer Vent Cleaning Irving TX ‪(214) 744-3341‬ https://carpetcleaninginirving.com/dryer-vent.html We can assist you if you need Lint Buildup Removal in Irving, Texas.Our cleaning technicians have a lot of knowledge and experience to help you.Your washing machine won't dry your clothes as well as it used to when it had a lot of this material in it.
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CandyCarpetCleaningIrving/TileGroutCleaningIrvingTX	CandyCarpetCleaningIrving	2022-12-11T07:18:00Z	0	0	null	[ "region:us" ]	null	2022-12-11T07:17:20Z	Tile Grout Cleaning Irving TX license: other https://carpetcleaninginirving.com/tile-grout.html ‪(214) 744-3341‬ We are available and can assist you at any time if you require Tile and Grout Cleaners in Irving, Texas who view this occupation as a career and make significant investments in comprehending the most effective ways to serve their customers.It's possible that the household cleaners you use are actually making your tile dirty.This includes your mop, which occasionally mixes grease, spills, and dirt with the grout.
CandyCarpetCleaningIrving/RugCleaningIrvingTX	CandyCarpetCleaningIrving	2022-12-11T07:15:12Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:12:39Z	--- license: other --- Rug Cleaning Irving TX https://carpetcleaninginirving.com/rug.html ‪(214) 744-3341‬ We can help you with Area Rug Cleaning in Irving, Texas, if you need it.We have developed superior cleaning techniques that can bring out the beauty of this home accent, especially if it hasn't been cleaned in a while.
CandyCarpetCleaningIrving/CandyCarpetCleaningIrving	CandyCarpetCleaningIrving	2022-12-11T07:11:02Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:10:41Z	--- license: other --- Candy Carpet Cleaning Irving https://carpetcleaninginirving.com/ ‪(214) 744-3341‬ We utilize strong cleaning procedures and an exceptionally present day and high level hardware to eliminate every one of the stains from your floor covering and simultaneously shield the varieties and the fiber from any harm. We additionally use eco-accommodating cleaning items that are 100% safe for your children and pets also. Toward the finish of our cleaning cycle we will apply a defensive covering that will shield the rug from any future stains.
muhtasham/small-mlm-imdb-target-tweet	muhtasham	2022-12-11T07:07:25Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T07:03:22Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy - f1 model-index: - name: small-mlm-imdb-target-tweet results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: emotion split: train args: emotion metrics: - name: Accuracy type: accuracy value: 0.7406417112299465 - name: F1 type: f1 value: 0.7432065579579084 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # small-mlm-imdb-target-tweet This model is a fine-tuned version of [muhtasham/small-mlm-imdb](https://huggingface.co/muhtasham/small-mlm-imdb) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 2.2131 - Accuracy: 0.7406 - F1: 0.7432 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5821 \| 4.9 \| 500 \| 0.8006 \| 0.7540 \| 0.7514 \| \| 0.1013 \| 9.8 \| 1000 \| 1.1662 \| 0.7567 \| 0.7562 \| \| 0.0236 \| 14.71 \| 1500 \| 1.5152 \| 0.7540 \| 0.7518 \| \| 0.0125 \| 19.61 \| 2000 \| 1.6963 \| 0.7620 \| 0.7581 \| \| 0.0068 \| 24.51 \| 2500 \| 1.9273 \| 0.7380 \| 0.7383 \| \| 0.0042 \| 29.41 \| 3000 \| 2.0042 \| 0.7487 \| 0.7500 \| \| 0.0041 \| 34.31 \| 3500 \| 2.2131 \| 0.7406 \| 0.7432 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
Sanjay-Papaiahgari/ppo-Huggy	Sanjay-Papaiahgari	2022-12-11T07:06:57Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-11T07:06:49Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: Sanjay-Papaiahgari/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
CleaningCarpetDallas/WaterDamageRestorationDallasTX	CleaningCarpetDallas	2022-12-11T07:05:33Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:05:13Z	--- license: other --- http://cleaningcarpetdallas.com/water-damage-restoration.html (972) 643-8799 Another service you can expect from Cleaning Carpet Dallas TX is water damage restoration.Do you live in a Texas building that has been flooded by a natural disaster?Please inform our staff if you have residential or commercial architecture that has been damaged by a hurricane or flood.
CleaningCarpetDallas/DryerVentCleaningDallasTX	CleaningCarpetDallas	2022-12-11T07:04:43Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:04:23Z	--- license: other --- http://cleaningcarpetdallas.com/dryer-vent-cleaning.html (972) 643-8799 Another skill that our Dallas technicians have mastered is cleaning dryer vents.Do you believe that the level of operation of your drying machine is lower than its normal and typical performance?Please let us know if you think there may be clogged ducts and vents so we can assist you.
muhtasham/mini-mlm-imdb-target-tweet	muhtasham	2022-12-11T07:03:10Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T07:00:40Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy - f1 model-index: - name: mini-mlm-imdb-target-tweet results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: emotion split: train args: emotion metrics: - name: Accuracy type: accuracy value: 0.767379679144385 - name: F1 type: f1 value: 0.7668830990510893 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mini-mlm-imdb-target-tweet This model is a fine-tuned version of [muhtasham/mini-mlm-imdb](https://huggingface.co/muhtasham/mini-mlm-imdb) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.3042 - Accuracy: 0.7674 - F1: 0.7669 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8543 \| 4.9 \| 500 \| 0.6920 \| 0.7674 \| 0.7571 \| \| 0.3797 \| 9.8 \| 1000 \| 0.7231 \| 0.7727 \| 0.7709 \| \| 0.1668 \| 14.71 \| 1500 \| 0.9171 \| 0.7594 \| 0.7583 \| \| 0.068 \| 19.61 \| 2000 \| 1.1558 \| 0.7647 \| 0.7642 \| \| 0.0409 \| 24.51 \| 2500 \| 1.3042 \| 0.7674 \| 0.7669 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
CleaningCarpetDallas/AirDuctCleaningDallasTX	CleaningCarpetDallas	2022-12-11T07:02:43Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T07:02:20Z	--- license: other --- http://cleaningcarpetdallas.com/air-duct-cleaning.html (972) 643-8799 For the health and safety of you and your family, hiring a mold removal service is crucial.If you don't take care of your ducts, you could end up with mold, mildew, and other harmful contaminants in them.Every time you use your air conditioner or heater, these will be moving around your house in the event of this.
muhtasham/tiny-mlm-imdb-target-tweet	muhtasham	2022-12-11T07:00:29Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T06:56:20Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy - f1 model-index: - name: tiny-mlm-imdb-target-tweet results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: emotion split: train args: emotion metrics: - name: Accuracy type: accuracy value: 0.6925133689839572 - name: F1 type: f1 value: 0.7003562110650444 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny-mlm-imdb-target-tweet This model is a fine-tuned version of [muhtasham/tiny-mlm-imdb](https://huggingface.co/muhtasham/tiny-mlm-imdb) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.5550 - Accuracy: 0.6925 - F1: 0.7004 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.159 \| 4.9 \| 500 \| 0.9977 \| 0.6364 \| 0.6013 \| \| 0.7514 \| 9.8 \| 1000 \| 0.8549 \| 0.7112 \| 0.7026 \| \| 0.5011 \| 14.71 \| 1500 \| 0.8516 \| 0.7032 \| 0.6962 \| \| 0.34 \| 19.61 \| 2000 \| 0.9019 \| 0.7059 \| 0.7030 \| \| 0.2258 \| 24.51 \| 2500 \| 0.9722 \| 0.7166 \| 0.7164 \| \| 0.1607 \| 29.41 \| 3000 \| 1.0724 \| 0.6979 \| 0.6999 \| \| 0.1127 \| 34.31 \| 3500 \| 1.1435 \| 0.7193 \| 0.7169 \| \| 0.0791 \| 39.22 \| 4000 \| 1.2807 \| 0.7059 \| 0.7069 \| \| 0.0568 \| 44.12 \| 4500 \| 1.3849 \| 0.7139 \| 0.7159 \| \| 0.0478 \| 49.02 \| 5000 \| 1.5550 \| 0.6925 \| 0.7004 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
CleaningCarpetDallas/UpholsteryCleaningDallasTX	CleaningCarpetDallas	2022-12-11T06:58:59Z	0	0	null	[ "license:other", "region:us" ]	null	2022-12-11T06:58:36Z	--- license: other --- http://cleaningcarpetdallas.com/upholstery-cleaning.html (972) 643-8799 Spots and stains on your microfiber sofa, couch, or loveseat can seriously ruin the appearance of your living room.You won't stand out with your gourmet and designer rugs, grandfather clocks, and artwork, and you'll also make your friends laugh.
sanchit-gandhi/whisper-small-fr-1k-steps	sanchit-gandhi	2022-12-11T06:58:16Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "fr", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-11T03:28:49Z	--- language: - fr license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small French results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 fr type: mozilla-foundation/common_voice_11_0 config: fr split: test args: fr metrics: - name: Wer type: wer value: 16.99780428461219 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small French This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 fr dataset. It achieves the following results on the evaluation set: - Loss: 0.3784 - Wer: 16.9978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3537 \| 1.0 \| 1000 \| 0.3784 \| 16.9978 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221210+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
muhtasham/base-vanilla-target-tweet	muhtasham	2022-12-11T06:56:07Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T06:46:39Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy - f1 model-index: - name: base-vanilla-target-tweet results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: emotion split: train args: emotion metrics: - name: Accuracy type: accuracy value: 0.7780748663101604 - name: F1 type: f1 value: 0.7772664883136655 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # base-vanilla-target-tweet This model is a fine-tuned version of [google/bert_uncased_L-12_H-768_A-12](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.8380 - Accuracy: 0.7781 - F1: 0.7773 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.3831 \| 4.9 \| 500 \| 0.9800 \| 0.7807 \| 0.7785 \| \| 0.0414 \| 9.8 \| 1000 \| 1.4175 \| 0.7754 \| 0.7765 \| \| 0.015 \| 14.71 \| 1500 \| 1.6411 \| 0.7754 \| 0.7708 \| \| 0.0166 \| 19.61 \| 2000 \| 1.5930 \| 0.7941 \| 0.7938 \| \| 0.0175 \| 24.51 \| 2500 \| 1.3934 \| 0.7888 \| 0.7852 \| \| 0.0191 \| 29.41 \| 3000 \| 1.9407 \| 0.7647 \| 0.7658 \| \| 0.0137 \| 34.31 \| 3500 \| 1.8380 \| 0.7781 \| 0.7773 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
darkvibes/vibes-v2	darkvibes	2022-12-11T06:40:14Z	0	2	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-12-11T06:29:27Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### VIBES-V2 Dreambooth model trained by darkvibes with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/darkvibes/vibes-v2/resolve/main/sample_images/cover2.JPG)
muhtasham/mini-vanilla-target-tweet	muhtasham	2022-12-11T06:37:03Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T06:33:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy - f1 model-index: - name: mini-vanilla-target-tweet results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: emotion split: train args: emotion metrics: - name: Accuracy type: accuracy value: 0.7540106951871658 - name: F1 type: f1 value: 0.7568814825340653 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mini-vanilla-target-tweet This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.5603 - Accuracy: 0.7540 - F1: 0.7569 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.9285 \| 4.9 \| 500 \| 0.7493 \| 0.7273 \| 0.7207 \| \| 0.4468 \| 9.8 \| 1000 \| 0.7630 \| 0.7460 \| 0.7437 \| \| 0.2194 \| 14.71 \| 1500 \| 0.8997 \| 0.7406 \| 0.7455 \| \| 0.1062 \| 19.61 \| 2000 \| 1.0822 \| 0.7433 \| 0.7435 \| \| 0.0568 \| 24.51 \| 2500 \| 1.2225 \| 0.7620 \| 0.7622 \| \| 0.0439 \| 29.41 \| 3000 \| 1.3475 \| 0.7513 \| 0.7527 \| \| 0.0304 \| 34.31 \| 3500 \| 1.4999 \| 0.7433 \| 0.7399 \| \| 0.0247 \| 39.22 \| 4000 \| 1.5603 \| 0.7540 \| 0.7569 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/base-mlm-tweet-target-imdb	muhtasham	2022-12-11T06:30:12Z	5	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T05:42:24Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: base-mlm-tweet-target-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.94368 - name: F1 type: f1 value: 0.9710240368784985 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # base-mlm-tweet-target-imdb This model is a fine-tuned version of [muhtasham/base-mlm-tweet](https://huggingface.co/muhtasham/base-mlm-tweet) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2137 - Accuracy: 0.9437 - F1: 0.9710 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.2617 \| 0.64 \| 500 \| 0.1863 \| 0.9342 \| 0.9660 \| \| 0.1778 \| 1.28 \| 1000 \| 0.1229 \| 0.9638 \| 0.9816 \| \| 0.1322 \| 1.92 \| 1500 \| 0.0893 \| 0.9699 \| 0.9847 \| \| 0.0756 \| 2.56 \| 2000 \| 0.4449 \| 0.9056 \| 0.9505 \| \| 0.063 \| 3.2 \| 2500 \| 0.3961 \| 0.9095 \| 0.9526 \| \| 0.0432 \| 3.84 \| 3000 \| 0.2137 \| 0.9437 \| 0.9710 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
luigisaetta/whisper-atco2-medium	luigisaetta	2022-12-11T06:07:13Z	20	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:luigisaetta/atco2_normalized_augmented", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-10T19:11:52Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - luigisaetta/atco2_normalized_augmented metrics: - wer model-index: - name: whisper-atco2-medium results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: luigisaetta/atco2_normalized_augmented type: luigisaetta/atco2_normalized_augmented config: en split: test metrics: - name: Wer type: wer value: 17.50524109014675 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-atco2-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the luigisaetta/atco2_normalized_augmented dataset. It achieves the following results on the evaluation set: - Loss: 0.6129 - Wer: 17.5052 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 2.3939 \| 1.06 \| 50 \| 1.8493 \| 66.5618 \| \| 0.5127 \| 2.13 \| 100 \| 0.5119 \| 30.6080 \| \| 0.0626 \| 3.19 \| 150 \| 0.5410 \| 20.4403 \| \| 0.0157 \| 4.25 \| 200 \| 0.5775 \| 19.8113 \| \| 0.0107 \| 5.32 \| 250 \| 0.5552 \| 19.7065 \| \| 0.0044 \| 6.38 \| 300 \| 0.5723 \| 18.1342 \| \| 0.0013 \| 7.45 \| 350 \| 0.5763 \| 17.7149 \| \| 0.0005 \| 8.51 \| 400 \| 0.6053 \| 17.7149 \| \| 0.0004 \| 9.57 \| 450 \| 0.6109 \| 17.5052 \| \| 0.0004 \| 10.64 \| 500 \| 0.6129 \| 17.5052 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.11.0
Farras/mt5-small-kompas	Farras	2022-12-11T05:39:02Z	4	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-12-11T00:11:37Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Farras/mt5-small-kompas results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Farras/mt5-small-kompas This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 10.4473 - Validation Loss: 7.2048 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 230, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 15.0491 \| 7.8158 \| 0 \| \| 10.4473 \| 7.2048 \| 1 \| ### Framework versions - Transformers 4.25.1 - TensorFlow 2.10.0 - Datasets 2.7.1 - Tokenizers 0.13.2
aungmyatv8/ppo-LunarLander-v2	aungmyatv8	2022-12-11T05:23:17Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-11T05:04:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 252.93 +/- 21.79 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
odiaz1066/huggytraining	odiaz1066	2022-12-11T05:17:19Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-11T05:17:12Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: odiaz1066/huggytraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sagawa/ZINC-t5-v2	sagawa	2022-12-11T05:11:31Z	13	0	transformers	[ "transformers", "pytorch", "jax", "t5", "text2text-generation", "dataset:sagawa/ZINC-canonicalized", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-06T01:33:39Z	--- license: mit datasets: - sagawa/ZINC-canonicalized metrics: - accuracy model-index: - name: ZINC-deberta results: - task: name: Masked Language Modeling type: fill-mask dataset: name: sagawa/ZINC-canonicalized type: sagawa/ZINC-canonicalized metrics: - name: Accuracy type: accuracy value: 0.9475839734077454 --- # ZINC-t5 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset. It achieves the following results on the evaluation set: - Loss: 0.1228 - Accuracy: 0.9476 ## Model description We trained t5 on SMILES from ZINC using the task of masked-language modeling (MLM). Compared to ZINC-t5, ZINC-t5-v2 uses a character-level tokenizer, and it was also trained on ZINC. ## Intended uses & limitations This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning. As an example, We finetuned this model to predict products. The model is [here](https://huggingface.co/sagawa/ZINC-t5-productpredicition), and you can use the demo [here](https://huggingface.co/spaces/sagawa/predictproduct-t5). Using its encoder, we trained a regression model to predict a reaction yield. You can use this demo [here](https://huggingface.co/spaces/sagawa/predictyield-t5). ## Training and evaluation data We downloaded [ZINC data](https://drive.google.com/drive/folders/1lSPCqh31zxTVEhuiPde7W3rZG8kPgp-z) and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 22992522, and they were randomly split into train:validation=10:1. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-03 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:------:\|:--------:\|:---------------:\| \| 0.2090 \| 100000 \| 0.9264 \| 0.1860 \| \| 0.1628 \| 200000 \| 0.9349 \| 0.1613 \| \| 0.1632 \| 300000 \| 0.9395 \| 0.1467 \| \| 0.1451 \| 400000 \| 0.9435 \| 0.1345 \| \| 0.1311 \| 500000 \| 0.9465 \| 0.1261 \|
muhtasham/small-mlm-tweet-target-imdb	muhtasham	2022-12-11T05:07:45Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T04:57:53Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: small-mlm-tweet-target-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.88784 - name: F1 type: f1 value: 0.9405881854394441 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # small-mlm-tweet-target-imdb This model is a fine-tuned version of [muhtasham/small-mlm-tweet](https://huggingface.co/muhtasham/small-mlm-tweet) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4422 - Accuracy: 0.8878 - F1: 0.9406 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.3515 \| 0.64 \| 500 \| 0.1494 \| 0.9388 \| 0.9684 \| \| 0.2452 \| 1.28 \| 1000 \| 0.1439 \| 0.9450 \| 0.9717 \| \| 0.1956 \| 1.92 \| 1500 \| 0.2199 \| 0.9156 \| 0.9559 \| \| 0.1398 \| 2.56 \| 2000 \| 0.4328 \| 0.876 \| 0.9339 \| \| 0.1102 \| 3.2 \| 2500 \| 0.4422 \| 0.8878 \| 0.9406 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/tiny-mlm-tweet-target-imdb	muhtasham	2022-12-11T04:49:46Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T04:42:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: tiny-mlm-tweet-target-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.84864 - name: F1 type: f1 value: 0.9181235935606715 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny-mlm-tweet-target-imdb This model is a fine-tuned version of [muhtasham/tiny-mlm-tweet](https://huggingface.co/muhtasham/tiny-mlm-tweet) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4017 - Accuracy: 0.8486 - F1: 0.9181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5661 \| 0.64 \| 500 \| 0.3869 \| 0.8363 \| 0.9109 \| \| 0.3798 \| 1.28 \| 1000 \| 0.3730 \| 0.8390 \| 0.9125 \| \| 0.3283 \| 1.92 \| 1500 \| 0.2422 \| 0.9018 \| 0.9484 \| \| 0.2926 \| 2.56 \| 2000 \| 0.4156 \| 0.8210 \| 0.9017 \| \| 0.2713 \| 3.2 \| 2500 \| 0.3951 \| 0.8405 \| 0.9133 \| \| 0.2519 \| 3.84 \| 3000 \| 0.2170 \| 0.9118 \| 0.9539 \| \| 0.2329 \| 4.48 \| 3500 \| 0.4214 \| 0.8357 \| 0.9105 \| \| 0.2074 \| 5.12 \| 4000 \| 0.5114 \| 0.8032 \| 0.8909 \| \| 0.1898 \| 5.75 \| 4500 \| 0.4017 \| 0.8486 \| 0.9181 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
ahmed02/Stable-diffusion-1-4	ahmed02	2022-12-11T04:41:27Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-12-11T04:41:27Z	--- license: bigscience-openrail-m ---
sagawa/PubChem-10m-deberta	sagawa	2022-12-11T04:33:58Z	55	1	transformers	[ "transformers", "pytorch", "deberta", "fill-mask", "generated_from_trainer", "dataset:sagawa/pubchem-10m-canonicalized", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-05T07:12:42Z	--- license: mit tags: - generated_from_trainer datasets: - sagawa/pubchem-10m-canonicalized metrics: - accuracy model-index: - name: PubChem-10m-deberta results: - task: name: Masked Language Modeling type: fill-mask dataset: name: sagawa/pubchem-10m-canonicalized type: sagawa/pubchem-10m-canonicalized metrics: - name: Accuracy type: accuracy value: 0.9741235263046233 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # PubChem10m-deberta-base-output This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/pubchem-10m-canonicalized dataset. It achieves the following results on the evaluation set: - Loss: 0.0698 - Accuracy: 0.9741 ## Model description We trained deberta-base on SMILES from PubChem using the task of masked-language modeling (MLM). Its tokenizer is a character-level tokenizer trained on PubChem. ## Intended uses & limitations This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning. ## Training and evaluation data We downloaded [PubChem data](https://drive.google.com/file/d/1ygYs8dy1-vxD1Vx6Ux7ftrXwZctFjpV3/view) and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 9999960, and they were randomly split into train:validation=10:1. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 0.0855 \| 3.68 \| 100000 \| 0.0801 \| 0.9708 \| \| 0.0733 \| 7.37 \| 200000 \| 0.0702 \| 0.9740 \| ### Framework versions - Transformers 4.22.0.dev0 - Pytorch 1.12.0 - Datasets 2.4.1.dev0 - Tokenizers 0.11.6
JuandaBula/distilroberta-base-mrpc-glue-juanda-bula	JuandaBula	2022-12-11T04:29:55Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T03:10:58Z	--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 widget: - text: - >- Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion. - >- Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998. example_title: Not Equivalent - text: - >- Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier. - >- With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier. example_title: Equivalent model-index: - name: distilroberta-base-mrpc-glue-juanda-bula results: - task: name: Text Classification type: text-classification dataset: name: datasetX type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8333333333333334 - name: F1 type: f1 value: 0.870722433460076 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-mrpc-glue-juanda-bula This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 0.5684 - Accuracy: 0.8333 - F1: 0.8707 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5239 \| 1.09 \| 500 \| 0.6723 \| 0.7990 \| 0.8610 \| \| 0.3692 \| 2.18 \| 1000 \| 0.5684 \| 0.8333 \| 0.8707 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cpu - Datasets 2.7.1 - Tokenizers 0.13.2
redevaaa/fin3	redevaaa	2022-12-11T03:59:45Z	16	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:fin", "license:cc-by-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-11T03:32:16Z	--- license: cc-by-sa-4.0 tags: - generated_from_trainer datasets: - fin metrics: - precision - recall - f1 - accuracy model-index: - name: fin3 results: - task: name: Token Classification type: token-classification dataset: name: fin type: fin config: default split: train args: default metrics: - name: Precision type: precision value: 0.944 - name: Recall type: recall value: 0.9402390438247012 - name: F1 type: f1 value: 0.9421157684630739 - name: Accuracy type: accuracy value: 0.9921209540034072 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fin3 This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.0748 - Precision: 0.944 - Recall: 0.9402 - F1: 0.9421 - Accuracy: 0.9921 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 129 \| 0.0669 \| 0.8821 \| 0.9243 \| 0.9027 \| 0.9883 \| \| No log \| 2.0 \| 258 \| 0.0568 \| 0.9289 \| 0.9363 \| 0.9325 \| 0.9913 \| \| No log \| 3.0 \| 387 \| 0.0565 \| 0.9141 \| 0.9323 \| 0.9231 \| 0.9904 \| \| 0.0556 \| 4.0 \| 516 \| 0.0617 \| 0.9237 \| 0.9163 \| 0.92 \| 0.9904 \| \| 0.0556 \| 5.0 \| 645 \| 0.0658 \| 0.9243 \| 0.9243 \| 0.9243 \| 0.9904 \| \| 0.0556 \| 6.0 \| 774 \| 0.0695 \| 0.944 \| 0.9402 \| 0.9421 \| 0.9921 \| \| 0.0556 \| 7.0 \| 903 \| 0.0731 \| 0.932 \| 0.9283 \| 0.9301 \| 0.9917 \| \| 0.0016 \| 8.0 \| 1032 \| 0.0750 \| 0.9283 \| 0.9283 \| 0.9283 \| 0.9917 \| \| 0.0016 \| 9.0 \| 1161 \| 0.0737 \| 0.944 \| 0.9402 \| 0.9421 \| 0.9921 \| \| 0.0016 \| 10.0 \| 1290 \| 0.0748 \| 0.944 \| 0.9402 \| 0.9421 \| 0.9921 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/small-mlm-imdb-target-imdb	muhtasham	2022-12-11T03:43:44Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T03:31:56Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: small-mlm-imdb-target-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.91736 - name: F1 type: f1 value: 0.9568990695539701 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # small-mlm-imdb-target-imdb This model is a fine-tuned version of [muhtasham/small-mlm-imdb](https://huggingface.co/muhtasham/small-mlm-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3145 - Accuracy: 0.9174 - F1: 0.9569 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.315 \| 0.64 \| 500 \| 0.1711 \| 0.9310 \| 0.9642 \| \| 0.2248 \| 1.28 \| 1000 \| 0.1385 \| 0.9471 \| 0.9728 \| \| 0.1824 \| 1.92 \| 1500 \| 0.1044 \| 0.9610 \| 0.9801 \| \| 0.1326 \| 2.56 \| 2000 \| 0.2382 \| 0.9294 \| 0.9634 \| \| 0.1056 \| 3.2 \| 2500 \| 0.5074 \| 0.8698 \| 0.9304 \| \| 0.0804 \| 3.84 \| 3000 \| 0.3145 \| 0.9174 \| 0.9569 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/tiny-mlm-imdb-target-imdb	muhtasham	2022-12-11T03:22:48Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T03:18:08Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: tiny-mlm-imdb-target-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.88952 - name: F1 type: f1 value: 0.9415301240526694 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny-mlm-imdb-target-imdb This model is a fine-tuned version of [muhtasham/tiny-mlm-imdb](https://huggingface.co/muhtasham/tiny-mlm-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2699 - Accuracy: 0.8895 - F1: 0.9415 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5432 \| 0.64 \| 500 \| 0.3567 \| 0.8578 \| 0.9235 \| \| 0.366 \| 1.28 \| 1000 \| 0.3687 \| 0.8414 \| 0.9138 \| \| 0.32 \| 1.92 \| 1500 \| 0.2648 \| 0.8922 \| 0.9430 \| \| 0.2868 \| 2.56 \| 2000 \| 0.3868 \| 0.8314 \| 0.9079 \| \| 0.2671 \| 3.2 \| 2500 \| 0.3092 \| 0.8774 \| 0.9347 \| \| 0.248 \| 3.84 \| 3000 \| 0.2699 \| 0.8895 \| 0.9415 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
sebrosen8/rose-shield-model	sebrosen8	2022-12-11T03:22:35Z	4	2	diffusers	[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-12-11T03:20:52Z	--- license: creativeml-openrail-m tags: - text-to-image widget: - text: dreamroseshield --- ### Rose Shield model Dreambooth model trained by sebrosen8 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the None base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: dreamroseshield (use that on your prompt) ![dreamroseshield 0](https://huggingface.co/sebrosen8/rose-shield-model/resolve/main/concept_images/dreamroseshield_%281%29.jpg)![dreamroseshield 1](https://huggingface.co/sebrosen8/rose-shield-model/resolve/main/concept_images/dreamroseshield_%282%29.jpg)![dreamroseshield 2](https://huggingface.co/sebrosen8/rose-shield-model/resolve/main/concept_images/dreamroseshield_%283%29.jpg)![dreamroseshield 3](https://huggingface.co/sebrosen8/rose-shield-model/resolve/main/concept_images/dreamroseshield_%284%29.jpg)![dreamroseshield 4](https://huggingface.co/sebrosen8/rose-shield-model/resolve/main/concept_images/dreamroseshield_%285%29.jpg)![dreamroseshield 5](https://huggingface.co/sebrosen8/rose-shield-model/resolve/main/concept_images/dreamroseshield_%286%29.jpg)![dreamroseshield 6](https://huggingface.co/sebrosen8/rose-shield-model/resolve/main/concept_images/dreamroseshield_%287%29.jpg)![dreamroseshield 7](https://huggingface.co/sebrosen8/rose-shield-model/resolve/main/concept_images/dreamroseshield_%288%29.jpg)
rymaju/KB13-t5-small-finetuned-en-to-regex	rymaju	2022-12-11T02:43:30Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-12-05T03:14:59Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: KB13-t5-small-finetuned-en-to-regex results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KB13-t5-small-finetuned-en-to-regex This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4028 - Semantic accuracy: 0.439 - Syntactic accuracy: 0.3659 - Gen Len: 15.3659 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Semantic accuracy \| Syntactic accuracy \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------------:\|:------------------:\|:-------:\| \| No log \| 1.0 \| 47 \| 0.9241 \| 0.0488 \| 0.0488 \| 15.1951 \| \| No log \| 2.0 \| 94 \| 0.6326 \| 0.3171 \| 0.2683 \| 14.6341 \| \| No log \| 3.0 \| 141 \| 0.5936 \| 0.2927 \| 0.2683 \| 15.1463 \| \| No log \| 4.0 \| 188 \| 0.5097 \| 0.3415 \| 0.3171 \| 15.5854 \| \| No log \| 5.0 \| 235 \| 0.4467 \| 0.3659 \| 0.3171 \| 15.7073 \| \| No log \| 6.0 \| 282 \| 0.3875 \| 0.3659 \| 0.3415 \| 15.4146 \| \| No log \| 7.0 \| 329 \| 0.4208 \| 0.3659 \| 0.3171 \| 15.5122 \| \| No log \| 8.0 \| 376 \| 0.3551 \| 0.3659 \| 0.3171 \| 15.3659 \| \| No log \| 9.0 \| 423 \| 0.2996 \| 0.3659 \| 0.3171 \| 15.3659 \| \| No log \| 10.0 \| 470 \| 0.3571 \| 0.3902 \| 0.3171 \| 15.2195 \| \| 0.7453 \| 11.0 \| 517 \| 0.3316 \| 0.4146 \| 0.3415 \| 15.3659 \| \| 0.7453 \| 12.0 \| 564 \| 0.3371 \| 0.4146 \| 0.3415 \| 15.439 \| \| 0.7453 \| 13.0 \| 611 \| 0.3488 \| 0.4146 \| 0.3415 \| 15.439 \| \| 0.7453 \| 14.0 \| 658 \| 0.3069 \| 0.439 \| 0.3659 \| 15.4146 \| \| 0.7453 \| 15.0 \| 705 \| 0.3289 \| 0.439 \| 0.3659 \| 15.1951 \| \| 0.7453 \| 16.0 \| 752 \| 0.3420 \| 0.3902 \| 0.3171 \| 15.0976 \| \| 0.7453 \| 17.0 \| 799 \| 0.3190 \| 0.4146 \| 0.3415 \| 15.1463 \| \| 0.7453 \| 18.0 \| 846 \| 0.3495 \| 0.439 \| 0.3659 \| 15.1463 \| \| 0.7453 \| 19.0 \| 893 \| 0.3588 \| 0.439 \| 0.3659 \| 15.3659 \| \| 0.7453 \| 20.0 \| 940 \| 0.3457 \| 0.439 \| 0.3659 \| 15.3659 \| \| 0.7453 \| 21.0 \| 987 \| 0.3662 \| 0.439 \| 0.3659 \| 15.3659 \| \| 0.1294 \| 22.0 \| 1034 \| 0.3533 \| 0.439 \| 0.3659 \| 15.3659 \| \| 0.1294 \| 23.0 \| 1081 \| 0.3872 \| 0.4146 \| 0.3415 \| 15.4146 \| \| 0.1294 \| 24.0 \| 1128 \| 0.3902 \| 0.4146 \| 0.3415 \| 15.3659 \| \| 0.1294 \| 25.0 \| 1175 \| 0.3802 \| 0.439 \| 0.3659 \| 15.3659 \| \| 0.1294 \| 26.0 \| 1222 \| 0.3893 \| 0.439 \| 0.3659 \| 15.4146 \| \| 0.1294 \| 27.0 \| 1269 \| 0.4035 \| 0.4146 \| 0.3415 \| 15.1951 \| \| 0.1294 \| 28.0 \| 1316 \| 0.4020 \| 0.4146 \| 0.3415 \| 15.3659 \| \| 0.1294 \| 29.0 \| 1363 \| 0.3983 \| 0.439 \| 0.3659 \| 15.3659 \| \| 0.1294 \| 30.0 \| 1410 \| 0.4028 \| 0.439 \| 0.3659 \| 15.3659 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/medium-vanilla-target-imdb	muhtasham	2022-12-11T02:36:24Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-11T02:20:08Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: medium-vanilla-target-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8964 - name: F1 type: f1 value: 0.945370175068551 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # medium-vanilla-target-imdb This model is a fine-tuned version of [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4330 - Accuracy: 0.8964 - F1: 0.9454 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.3068 \| 0.64 \| 500 \| 0.2373 \| 0.9061 \| 0.9507 \| \| 0.2143 \| 1.28 \| 1000 \| 0.1204 \| 0.9534 \| 0.9761 \| \| 0.1655 \| 1.92 \| 1500 \| 0.1557 \| 0.942 \| 0.9701 \| \| 0.1107 \| 2.56 \| 2000 \| 0.2791 \| 0.9268 \| 0.9620 \| \| 0.0905 \| 3.2 \| 2500 \| 0.4330 \| 0.8964 \| 0.9454 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
ScrappyCoco666/ppo-LunarLander-v2-5	ScrappyCoco666	2022-12-11T02:14:08Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-11T02:13:49Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 302.61 +/- 18.97 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
redevaaa/fin1	redevaaa	2022-12-11T02:12:04Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:fin", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-11T01:38:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - fin metrics: - precision - recall - f1 - accuracy model-index: - name: fin1 results: - task: name: Token Classification type: token-classification dataset: name: fin type: fin config: default split: train args: default metrics: - name: Precision type: precision value: 0.8315412186379928 - name: Recall type: recall value: 0.9243027888446215 - name: F1 type: f1 value: 0.8754716981132076 - name: Accuracy type: accuracy value: 0.985175455057234 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fin1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.0778 - Precision: 0.8315 - Recall: 0.9243 - F1: 0.8755 - Accuracy: 0.9852 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 129 \| 0.0860 \| 0.8535 \| 0.9283 \| 0.8893 \| 0.9904 \| \| No log \| 2.0 \| 258 \| 0.1513 \| 0.7993 \| 0.9203 \| 0.8556 \| 0.9799 \| \| No log \| 3.0 \| 387 \| 0.0977 \| 0.8221 \| 0.9203 \| 0.8684 \| 0.9831 \| \| 0.0017 \| 4.0 \| 516 \| 0.0783 \| 0.8286 \| 0.9243 \| 0.8738 \| 0.9848 \| \| 0.0017 \| 5.0 \| 645 \| 0.0778 \| 0.8315 \| 0.9243 \| 0.8755 \| 0.9852 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
sd-concepts-library/pokemon-rgby-sprite	sd-concepts-library	2022-12-11T02:10:06Z	0	7	null	[ "license:mit", "region:us" ]	null	2022-12-11T02:02:35Z	--- license: mit --- ### Pokemon RGBY sprite on Stable Diffusion Pokémon Red/Green/Blue/Yellow battle sprite concept (GameBoy 56x56 upscaled to 512x512) This is the `<pkmn-rgby>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<pkmn-rgby> 0](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/0.jpeg) ![<pkmn-rgby> 1](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/1.jpeg) ![<pkmn-rgby> 2](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/2.jpeg) ![<pkmn-rgby> 3](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/3.jpeg) ![<pkmn-rgby> 4](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/4.jpeg) ![<pkmn-rgby> 5](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/5.jpeg) ![<pkmn-rgby> 6](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/6.jpeg) ![<pkmn-rgby> 7](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/7.jpeg) ![<pkmn-rgby> 8](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/8.jpeg) ![<pkmn-rgby> 9](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/9.jpeg) ![<pkmn-rgby> 10](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/10.jpeg) ![<pkmn-rgby> 11](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/11.jpeg) ![<pkmn-rgby> 12](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/12.jpeg) ![<pkmn-rgby> 13](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/13.jpeg) ![<pkmn-rgby> 14](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/14.jpeg) ![<pkmn-rgby> 15](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/15.jpeg) ![<pkmn-rgby> 16](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/16.jpeg) ![<pkmn-rgby> 17](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/17.jpeg) ![<pkmn-rgby> 18](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/18.jpeg) ![<pkmn-rgby> 19](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/19.jpeg) ![<pkmn-rgby> 20](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/20.jpeg) ![<pkmn-rgby> 21](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/21.jpeg) ![<pkmn-rgby> 22](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/22.jpeg) ![<pkmn-rgby> 23](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/23.jpeg) ![<pkmn-rgby> 24](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/24.jpeg) ![<pkmn-rgby> 25](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/25.jpeg) ![<pkmn-rgby> 26](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/26.jpeg) ![<pkmn-rgby> 27](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/27.jpeg) ![<pkmn-rgby> 28](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/28.jpeg) ![<pkmn-rgby> 29](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/29.jpeg) ![<pkmn-rgby> 30](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/30.jpeg) ![<pkmn-rgby> 31](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/31.jpeg) ![<pkmn-rgby> 32](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/32.jpeg) ![<pkmn-rgby> 33](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/33.jpeg) ![<pkmn-rgby> 34](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/34.jpeg) ![<pkmn-rgby> 35](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/35.jpeg) ![<pkmn-rgby> 36](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/36.jpeg) ![<pkmn-rgby> 37](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/37.jpeg) ![<pkmn-rgby> 38](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/38.jpeg) ![<pkmn-rgby> 39](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/39.jpeg) ![<pkmn-rgby> 40](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/40.jpeg) ![<pkmn-rgby> 41](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/41.jpeg) ![<pkmn-rgby> 42](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/42.jpeg) ![<pkmn-rgby> 43](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/43.jpeg) ![<pkmn-rgby> 44](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/44.jpeg) ![<pkmn-rgby> 45](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/45.jpeg) ![<pkmn-rgby> 46](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/46.jpeg) ![<pkmn-rgby> 47](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/47.jpeg) ![<pkmn-rgby> 48](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/48.jpeg) ![<pkmn-rgby> 49](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/49.jpeg) ![<pkmn-rgby> 50](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/50.jpeg) ![<pkmn-rgby> 51](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/51.jpeg) ![<pkmn-rgby> 52](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/52.jpeg) ![<pkmn-rgby> 53](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/53.jpeg) ![<pkmn-rgby> 54](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/54.jpeg) ![<pkmn-rgby> 55](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/55.jpeg) ![<pkmn-rgby> 56](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/56.jpeg) ![<pkmn-rgby> 57](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/57.jpeg) ![<pkmn-rgby> 58](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/58.jpeg) ![<pkmn-rgby> 59](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/59.jpeg) ![<pkmn-rgby> 60](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/60.jpeg) ![<pkmn-rgby> 61](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/61.jpeg) ![<pkmn-rgby> 62](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/62.jpeg) ![<pkmn-rgby> 63](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/63.jpeg) ![<pkmn-rgby> 64](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/64.jpeg) ![<pkmn-rgby> 65](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/65.jpeg) ![<pkmn-rgby> 66](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/66.jpeg) ![<pkmn-rgby> 67](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/67.jpeg) ![<pkmn-rgby> 68](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/68.jpeg) ![<pkmn-rgby> 69](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/69.jpeg) ![<pkmn-rgby> 70](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/70.jpeg) ![<pkmn-rgby> 71](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/71.jpeg) ![<pkmn-rgby> 72](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/72.jpeg) ![<pkmn-rgby> 73](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/73.jpeg) ![<pkmn-rgby> 74](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/74.jpeg) ![<pkmn-rgby> 75](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/75.jpeg) ![<pkmn-rgby> 76](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/76.jpeg) ![<pkmn-rgby> 77](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/77.jpeg) ![<pkmn-rgby> 78](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/78.jpeg) ![<pkmn-rgby> 79](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/79.jpeg) ![<pkmn-rgby> 80](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/80.jpeg) ![<pkmn-rgby> 81](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/81.jpeg) ![<pkmn-rgby> 82](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/82.jpeg) ![<pkmn-rgby> 83](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/83.jpeg) ![<pkmn-rgby> 84](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/84.jpeg) ![<pkmn-rgby> 85](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/85.jpeg) ![<pkmn-rgby> 86](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/86.jpeg) ![<pkmn-rgby> 87](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/87.jpeg) ![<pkmn-rgby> 88](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/88.jpeg) ![<pkmn-rgby> 89](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/89.jpeg) ![<pkmn-rgby> 90](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/90.jpeg) ![<pkmn-rgby> 91](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/91.jpeg) ![<pkmn-rgby> 92](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/92.jpeg) ![<pkmn-rgby> 93](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/93.jpeg) ![<pkmn-rgby> 94](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/94.jpeg) ![<pkmn-rgby> 95](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/95.jpeg) ![<pkmn-rgby> 96](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/96.jpeg) ![<pkmn-rgby> 97](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/97.jpeg) ![<pkmn-rgby> 98](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/98.jpeg) ![<pkmn-rgby> 99](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/99.jpeg) ![<pkmn-rgby> 100](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/100.jpeg) ![<pkmn-rgby> 101](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/101.jpeg) ![<pkmn-rgby> 102](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/102.jpeg) ![<pkmn-rgby> 103](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/103.jpeg) ![<pkmn-rgby> 104](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/104.jpeg) ![<pkmn-rgby> 105](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/105.jpeg) ![<pkmn-rgby> 106](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/106.jpeg) ![<pkmn-rgby> 107](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/107.jpeg) ![<pkmn-rgby> 108](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/108.jpeg) ![<pkmn-rgby> 109](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/109.jpeg) ![<pkmn-rgby> 110](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/110.jpeg) ![<pkmn-rgby> 111](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/111.jpeg) ![<pkmn-rgby> 112](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/112.jpeg) ![<pkmn-rgby> 113](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/113.jpeg) ![<pkmn-rgby> 114](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/114.jpeg) ![<pkmn-rgby> 115](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/115.jpeg) ![<pkmn-rgby> 116](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/116.jpeg) ![<pkmn-rgby> 117](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/117.jpeg) ![<pkmn-rgby> 118](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/118.jpeg) ![<pkmn-rgby> 119](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/119.jpeg) ![<pkmn-rgby> 120](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/120.jpeg) ![<pkmn-rgby> 121](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/121.jpeg) ![<pkmn-rgby> 122](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/122.jpeg) ![<pkmn-rgby> 123](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/123.jpeg) ![<pkmn-rgby> 124](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/124.jpeg) ![<pkmn-rgby> 125](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/125.jpeg) ![<pkmn-rgby> 126](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/126.jpeg) ![<pkmn-rgby> 127](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/127.jpeg) ![<pkmn-rgby> 128](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/128.jpeg) ![<pkmn-rgby> 129](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/129.jpeg) ![<pkmn-rgby> 130](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/130.jpeg) ![<pkmn-rgby> 131](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/131.jpeg) ![<pkmn-rgby> 132](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/132.jpeg) ![<pkmn-rgby> 133](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/133.jpeg) ![<pkmn-rgby> 134](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/134.jpeg) ![<pkmn-rgby> 135](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/135.jpeg) ![<pkmn-rgby> 136](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/136.jpeg) ![<pkmn-rgby> 137](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/137.jpeg) ![<pkmn-rgby> 138](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/138.jpeg) ![<pkmn-rgby> 139](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/139.jpeg) ![<pkmn-rgby> 140](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/140.jpeg) ![<pkmn-rgby> 141](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/141.jpeg) ![<pkmn-rgby> 142](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/142.jpeg) ![<pkmn-rgby> 143](https://huggingface.co/sd-concepts-library/pokemon-rgby-sprite/resolve/main/concept_images/143.jpeg) ![<pkmn-rgby> 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ScrappyCoco666/ppo-LunarLander-v2-2	ScrappyCoco666	2022-12-11T01:46:52Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-11T01:46:24Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 289.76 +/- 15.08 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ScrappyCoco666/ppo-LunarLander-v2-3	ScrappyCoco666	2022-12-11T01:38:52Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-11T01:38:31Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 292.80 +/- 19.87 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
panopstor/finetunedump	panopstor	2022-12-11T01:37:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-12-11T01:37:20Z	--- license: creativeml-openrail-m ---
eublefar/bigbird-dialogue-score	eublefar	2022-12-11T01:18:15Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "big_bird", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-10T13:26:00Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bigbird-dialogue-score results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bigbird-dialogue-score This model is a fine-tuned version of [google/bigbird-roberta-large](https://huggingface.co/google/bigbird-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2129 - eval_f1: 0.9290 - eval_precision: 0.9173 - eval_recall: 0.9410 - eval_runtime: 311.0516 - eval_samples_per_second: 49.304 - eval_steps_per_second: 6.163 - epoch: 1.0 - step: 5432 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 6 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
anuragshas/whisper-small-ur	anuragshas	2022-12-11T00:37:51Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ur", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-10T19:59:32Z	--- language: - ur license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Urdu results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 ur type: mozilla-foundation/common_voice_11_0 config: ur split: test args: ur metrics: - name: Wer type: wer value: 32.68135868933731 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Urdu This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ur dataset. It achieves the following results on the evaluation set: - Loss: 0.7803 - Wer: 32.6814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2634 \| 3.85 \| 200 \| 0.5562 \| 43.3518 \| \| 0.0592 \| 7.69 \| 400 \| 0.6271 \| 40.8807 \| \| 0.0121 \| 11.54 \| 600 \| 0.7298 \| 35.4506 \| \| 0.0048 \| 15.38 \| 800 \| 0.7803 \| 32.6814 \| \| 0.0039 \| 19.23 \| 1000 \| 0.7940 \| 33.3243 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
zates/distilbert-base-uncased-finetuned-squad-seed-420	zates	2022-12-11T00:20:35Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-12-10T21:34:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-squad-seed-420 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad-seed-420 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.9590 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.4491 \| 1.0 \| 8248 \| 2.1014 \| \| 2.1388 \| 2.0 \| 16496 \| 1.9590 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/small-mlm-imdb	muhtasham	2022-12-10T23:57:28Z	3	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-12-10T23:17:54Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: small-mlm-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # small-mlm-imdb This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3673 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.7542 \| 0.16 \| 500 \| 2.5445 \| \| 2.6734 \| 0.32 \| 1000 \| 2.5191 \| \| 2.6552 \| 0.48 \| 1500 \| 2.4976 \| \| 2.6481 \| 0.64 \| 2000 \| 2.4866 \| \| 2.6291 \| 0.8 \| 2500 \| 2.4599 \| \| 2.6134 \| 0.96 \| 3000 \| 2.4585 \| \| 2.5627 \| 1.12 \| 3500 \| 2.4476 \| \| 2.5564 \| 1.28 \| 4000 \| 2.4340 \| \| 2.5493 \| 1.44 \| 4500 \| 2.4354 \| \| 2.5435 \| 1.6 \| 5000 \| 2.4307 \| \| 2.5352 \| 1.76 \| 5500 \| 2.4224 \| \| 2.5445 \| 1.92 \| 6000 \| 2.4167 \| \| 2.5191 \| 2.08 \| 6500 \| 2.4175 \| \| 2.5143 \| 2.24 \| 7000 \| 2.4149 \| \| 2.5059 \| 2.4 \| 7500 \| 2.4117 \| \| 2.4865 \| 2.56 \| 8000 \| 2.4063 \| \| 2.5113 \| 2.72 \| 8500 \| 2.3976 \| \| 2.5115 \| 2.88 \| 9000 \| 2.3959 \| \| 2.485 \| 3.04 \| 9500 \| 2.3917 \| \| 2.4652 \| 3.2 \| 10000 \| 2.3908 \| \| 2.4569 \| 3.36 \| 10500 \| 2.3877 \| \| 2.4706 \| 3.52 \| 11000 \| 2.3836 \| \| 2.4375 \| 3.68 \| 11500 \| 2.3870 \| \| 2.4556 \| 3.84 \| 12000 \| 2.3819 \| \| 2.4487 \| 4.0 \| 12500 \| 2.3842 \| \| 2.4233 \| 4.16 \| 13000 \| 2.3731 \| \| 2.4238 \| 4.32 \| 13500 \| 2.3801 \| \| 2.4051 \| 4.48 \| 14000 \| 2.3809 \| \| 2.432 \| 4.64 \| 14500 \| 2.3641 \| \| 2.428 \| 4.8 \| 15000 \| 2.3686 \| \| 2.4248 \| 4.96 \| 15500 \| 2.3741 \| \| 2.4109 \| 5.12 \| 16000 \| 2.3673 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
Michunie/ppo-LunarLander-v2	Michunie	2022-12-10T23:39:04Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-10T19:31:41Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 284.30 +/- 17.28 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Vasi001/whisper-small	Vasi001	2022-12-10T23:32:04Z	4	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-10T21:57:53Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Swedish results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/medium-mlm-tweet	muhtasham	2022-12-10T23:13:39Z	3	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-12-10T22:56:21Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: medium-mlm-tweet results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # medium-mlm-tweet This model is a fine-tuned version of [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3983 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.1681 \| 11.11 \| 500 \| 3.2485 \| \| 2.6193 \| 22.22 \| 1000 \| 3.2971 \| \| 2.286 \| 33.33 \| 1500 \| 3.5000 \| \| 1.9916 \| 44.44 \| 2000 \| 3.3983 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/small-mlm-tweet	muhtasham	2022-12-10T22:55:44Z	3	1	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-12-10T22:41:42Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: small-mlm-tweet results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # small-mlm-tweet This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8171 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.4028 \| 11.11 \| 500 \| 3.4323 \| \| 2.8952 \| 22.22 \| 1000 \| 3.4180 \| \| 2.6035 \| 33.33 \| 1500 \| 3.6851 \| \| 2.3349 \| 44.44 \| 2000 \| 3.4708 \| \| 2.1048 \| 55.56 \| 2500 \| 3.8171 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
sanchit-gandhi/whisper-small-en-1k-steps	sanchit-gandhi	2022-12-10T22:41:09Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-10T18:20:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: openai/whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: en split: test args: en metrics: - name: Wer type: wer value: 14.805770651929443 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # openai/whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3747 - Wer: 14.8058 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2803 \| 1.0 \| 1000 \| 0.3747 \| 14.8058 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221210+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
muhtasham/mini-mlm-tweet	muhtasham	2022-12-10T22:41:06Z	4	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-12-10T22:31:51Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: mini-mlm-tweet results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mini-mlm-tweet This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1171 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.9227 \| 11.11 \| 500 \| 3.8377 \| \| 3.4825 \| 22.22 \| 1000 \| 3.7411 \| \| 3.2903 \| 33.33 \| 1500 \| 3.8864 \| \| 3.1026 \| 44.44 \| 2000 \| 3.6987 \| \| 2.9438 \| 55.56 \| 2500 \| 3.9807 \| \| 2.8075 \| 66.67 \| 3000 \| 3.8835 \| \| 2.6951 \| 77.78 \| 3500 \| 4.1171 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
alanrice/wav2vec2-large-xls-r-1b-irish-colab	alanrice	2022-12-10T22:39:04Z	7	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "ga", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-10T10:21:23Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer language: - ga model-index: - name: wav2vec2-large-xls-r-1b-irish-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 type: mozilla-foundation/common_voice_11_0 config: ga-IE split: train+validation args: ga-IE metrics: - name: Wer type: wer value: 46.911764705882353 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-1b-irish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.0795 - Wer: 46.91 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.6902 \| 12.12 \| 400 \| 1.1158 \| 0.5959 \| \| 0.2988 \| 24.24 \| 800 \| 1.1375 \| 0.5094 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.10.0+cu113 - Datasets 2.0.0 - Tokenizers 0.13.2
alanrice/wav2vec2-large-xls-r-300m-irish-colab	alanrice	2022-12-10T22:38:40Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "ga", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-08T22:35:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer language: - ga model-index: - name: wav2vec2-large-xls-r-300m-irish-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 type: mozilla-foundation/common_voice_11_0 config: ga-IE split: train+validation args: ga-IE metrics: - name: Wer type: wer value: 52.44117647058824 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-irish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.148 - Wer: 52.4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 4.6516 \| 12.12 \| 400 \| 1.2867 \| 0.7653 \| \| 0.4188 \| 24.24 \| 800 \| 1.1262 \| 0.5509 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.10.0+cu113 - Datasets 2.0.0 - Tokenizers 0.13.2
Leilab/hair_lenght	Leilab	2022-12-10T22:31:40Z	28	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-12-10T22:31:29Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: hair_lenght results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8888888955116272 --- # hair_lenght Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### long hair ![long hair](images/long_hair.jpg) #### short human hair ![short human hair](images/short_human_hair.jpg)
osanseviero/q-Taxi-v3-nice	osanseviero	2022-12-10T22:16:59Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-10T22:16:53Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-nice results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="osanseviero/q-Taxi-v3-nice", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
fcakyon/timesformer-large-finetuned-ssv2	fcakyon	2022-12-10T22:16:57Z	4	0	transformers	[ "transformers", "pytorch", "timesformer", "video-classification", "vision", "arxiv:2102.05095", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2022-12-10T21:37:16Z	--- license: "cc-by-nc-4.0" tags: - vision - video-classification --- # TimeSformer (large-sized model, fine-tuned on Something Something v2) TimeSformer model pre-trained on [Something Something v2](https://developer.qualcomm.com/software/ai-datasets/something-something). It was introduced in the paper [TimeSformer: Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Tong et al. and first released in [this repository](https://github.com/facebookresearch/TimeSformer). Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by [fcakyon](https://github.com/fcakyon). ## Intended uses & limitations You can use the raw model for video classification into one of the 174 possible Something Something v2 labels. ### How to use Here is how to use this model to classify a video: ```python from transformers import AutoImageProcessor, TimesformerForVideoClassification import numpy as np import torch video = list(np.random.randn(64, 3, 448, 448)) processor = AutoImageProcessor.from_pretrained("fcakyon/timesformer-large-finetuned-ssv2") model = TimesformerForVideoClassification.from_pretrained("fcakyon/timesformer-large-finetuned-ssv2") inputs = feature_extractor(images=video, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/timesformer.html#). ### BibTeX entry and citation info ```bibtex @inproceedings{bertasius2021space, title={Is Space-Time Attention All You Need for Video Understanding?}, author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo}, booktitle={International Conference on Machine Learning}, pages={813--824}, year={2021}, organization={PMLR} } ```
fcakyon/timesformer-base-finetuned-k600	fcakyon	2022-12-10T22:09:46Z	2	0	transformers	[ "transformers", "pytorch", "timesformer", "video-classification", "vision", "arxiv:2102.05095", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2022-12-10T21:53:59Z	--- license: "cc-by-nc-4.0" tags: - vision - video-classification --- # TimeSformer (base-sized model, fine-tuned on Kinetics-600) TimeSformer model pre-trained on [Kinetics-600](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [TimeSformer: Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Tong et al. and first released in [this repository](https://github.com/facebookresearch/TimeSformer). Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by [fcakyon](https://github.com/fcakyon). ## Intended uses & limitations You can use the raw model for video classification into one of the 600 possible Kinetics-600 labels. ### How to use Here is how to use this model to classify a video: ```python from transformers import AutoImageProcessor, TimesformerForVideoClassification import numpy as np import torch video = list(np.random.randn(8, 3, 224, 224)) processor = AutoImageProcessor.from_pretrained("fcakyon/timesformer-base-finetuned-k600") model = TimesformerForVideoClassification.from_pretrained("fcakyon/timesformer-base-finetuned-k600") inputs = processor(images=video, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/timesformer.html#). ### BibTeX entry and citation info ```bibtex @inproceedings{bertasius2021space, title={Is Space-Time Attention All You Need for Video Understanding?}, author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo}, booktitle={International Conference on Machine Learning}, pages={813--824}, year={2021}, organization={PMLR} } ```
osanseviero/q-FrozenLake-v1-4x4-noSlippery-test4	osanseviero	2022-12-10T22:08:08Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-10T22:07:59Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery-test4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="osanseviero/q-FrozenLake-v1-4x4-noSlippery-test4", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
osanseviero/q-FrozenLake-v1-4x4-noSlippery-test3	osanseviero	2022-12-10T22:02:25Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-10T21:58:46Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery-test3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="osanseviero/q-FrozenLake-v1-4x4-noSlippery-test3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
osanseviero/q-FrozenLake-v1-4x4-noSlippery-test	osanseviero	2022-12-10T21:58:02Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-10T21:46:27Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery-test results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="osanseviero/q-FrozenLake-v1-4x4-noSlippery-test", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
xpariz10/ast-finetuned-audioset-10-10-0.4593-finetuning-ESC-50	xpariz10	2022-12-10T21:55:51Z	38	1	transformers	[ "transformers", "pytorch", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	audio-classification	2022-12-07T17:18:03Z	--- license: bsd-3-clause tags: - generated_from_trainer metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuning-ESC-50 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ast-finetuned-audioset-10-10-0.4593-finetuning-ESC-50 This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the ESC-50 dataset. It achieves the following results on the evaluation set: - Loss: 0.3356 - Accuracy: 0.9464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0621 \| 1.0 \| 28 \| 0.4656 \| 0.875 \| \| 0.0694 \| 2.0 \| 56 \| 0.3050 \| 0.9107 \| \| 0.0157 \| 3.0 \| 84 \| 0.3356 \| 0.9464 \| \| 0.0038 \| 4.0 \| 112 \| 0.3175 \| 0.9286 \| \| 0.0011 \| 5.0 \| 140 \| 0.2579 \| 0.9286 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
Stxlla/fine-finetuned	Stxlla	2022-12-10T21:37:31Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-12-10T16:13:44Z	--- license: mit tags: - generated_from_trainer model-index: - name: fine-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fine-finetuned This model is a fine-tuned version of [Stxlla/ko-en-following](https://huggingface.co/Stxlla/ko-en-following) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1211 - eval_bleu: 61.2672 - eval_gen_len: 11.3556 - eval_runtime: 2042.0344 - eval_samples_per_second: 16.208 - eval_steps_per_second: 1.013 - epoch: 2.0 - step: 33098 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
joelkoch/ppo-Huggy	joelkoch	2022-12-10T21:31:18Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-10T21:31:11Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: joelkoch/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Leilab/gender_class	Leilab	2022-12-10T21:18:02Z	1,020	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-12-10T21:17:51Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: gender_class results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9555555582046509 --- # gender_class Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### men ![men](images/men.jpg) #### women ![women](images/women.jpg)
TimothyKassis/ppo-LunarLander-v2	TimothyKassis	2022-12-10T20:09:11Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-10T20:08:44Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.85 +/- 16.91 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
michellejieli/inappropriate_text_classifier	michellejieli	2022-12-10T20:08:21Z	1,298	10	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "distilroberta", "sentiment", "NSFW", "inappropriate", "spam", "twitter", "reddit", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-10T20:00:03Z	--- license: creativeml-openrail-m language: "en" tags: - distilroberta - sentiment - NSFW - inappropriate - spam - twitter - reddit widget: - text: "I like you. You remind me of me when I was young and stupid." - text: "I see you’ve set aside this special time to humiliate yourself in public." - text: "Have a great weekend! See you next week!" --- # Fine-tuned DistilBERT for NSFW Inappropriate Text Classification # Model Description DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert). It was fine-tuned on 19604 Reddit posts pulled from the [Comprehensive Abusiveness Detection Dataset](https://aclanthology.org/2021.conll-1.43/). # How to Use ```python from transformers import pipeline classifier = pipeline("sentiment-analysis", model="michellejieli/inappropriate_text_classifier") classifier("I see you’ve set aside this special time to humiliate yourself in public.") ``` ```python Output: [{'label': 'NSFW', 'score': 0.9684491753578186}] ``` # Contact Please reach out to [[email protected]](mailto:[email protected]) if you have any questions or feedback. # Reference ``` Hoyun Song, Soo Hyun Ryu, Huije Lee, and Jong Park. 2021. A Large-scale Comprehensive Abusiveness Detection Dataset with Multifaceted Labels from Reddit. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 552–561, Online. Association for Computational Linguistics. ``` ---
RamonAnkersmit/ppo-LunarLander-v2	RamonAnkersmit	2022-12-10T20:08:18Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-09T17:59:48Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 273.85 +/- 20.21 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
michellejieli/NSFW_text_classifier	michellejieli	2022-12-10T19:59:37Z	149,407	95	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "distilroberta", "sentiment", "NSFW", "inappropriate", "spam", "twitter", "reddit", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-10T01:42:56Z	--- language: "en" tags: - distilroberta - sentiment - NSFW - inappropriate - spam - twitter - reddit widget: - text: "I like you. You remind me of me when I was young and stupid." - text: "I see you’ve set aside this special time to humiliate yourself in public." - text: "Have a great weekend! See you next week!" --- # Fine-tuned DistilRoBERTa-base for NSFW Classification # Model Description DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert). It was fine-tuned on 14317 Reddit posts pulled from the (Reddit API) [https://praw.readthedocs.io/en/stable/]. # How to Use ```python from transformers import pipeline classifier = pipeline("sentiment-analysis", model="michellejieli/NSFW_text_classification") classifier("I see you’ve set aside this special time to humiliate yourself in public.") ``` ```python Output: [{'label': 'NSFW', 'score': 0.998853325843811}] ``` # Contact Please reach out to [[email protected]](mailto:[email protected]) if you have any questions or feedback. ---
Wurzeldieb/painted_abstract	Wurzeldieb	2022-12-10T19:54:40Z	0	8	null	[ "license:openrail", "region:us" ]	null	2022-12-10T17:10:58Z	--- license: openrail --- This is a a Textual Inversion Embedding to create an abstract style with a lot of detail, but still recognizable content. Works with the 768x768 versions of Stable Diffusion 2.0 and 2.1 To use it put the painted_abstract.pt file in in your embeddings folder and use painted_abstract as promt I recommend a cfg below 10 and maybe even a bit lower for 2.1, it gets more blocky the higher the cfg usually I use 7 for 2.0 and 5 for 2.1 I also recommend using an anime upscaler like RealESRGAN_x4plus_anime_6B For the examples I used different samplers and both 2.0 and 2.1, generated at 768x768 and upscaeld x4 with RealESRGAN_x4plus_anime_6B, otherwise untouched ![09994-3570179826-(Horrifying Digital Artwork_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692802633-63126010c7577b68d90ac441.jpeg) ![00009.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692797495-63126010c7577b68d90ac441.jpeg) ![11085-1189634371-(Vector image_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692797848-63126010c7577b68d90ac441.jpeg) ![00012.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692802782-63126010c7577b68d90ac441.jpeg) ![09993-3530690305-(Horrifying Digital Artwork_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692794311-63126010c7577b68d90ac441.jpeg) ![11057-956511450-(Drawing_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692800015-63126010c7577b68d90ac441.jpeg) ![10017-1188724224-(Horrifying Digital Artwork_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692789138-63126010c7577b68d90ac441.jpeg) ![10214-2903346083-(Magical Painting_1.3) of (Illustration_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692801653-63126010c7577b68d90ac441.jpeg) ![10316-2083980865-(Drawing_1.2) of (Detailed illustration_1.2),(Evil_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692803130-63126010c7577b68d90ac441.jpeg) ![10512-1292097722-(Detailed illustration_1.2),(Evil_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692789024-63126010c7577b68d90ac441.jpeg) ![00007.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692798706-63126010c7577b68d90ac441.jpeg) ![10577-3820618625-(Magical Painting_1.2) of (Visual novel_1.2),(Energetic_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692794517-63126010c7577b68d90ac441.jpeg) ![10774-1832682202-(Drawing_1.2) of (Sketched_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692802642-63126010c7577b68d90ac441.jpeg) ![10917-114459484-(Digital Artwork_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692797288-63126010c7577b68d90ac441.jpeg) ![11032-4026688857-(Drawing_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692797738-63126010c7577b68d90ac441.jpeg) ![00004.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692798753-63126010c7577b68d90ac441.jpeg) ![11062-3333540938-(Professional 3D rendering_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692793538-63126010c7577b68d90ac441.jpeg) ![11121-837666352-(Painting_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692796391-63126010c7577b68d90ac441.jpeg) ![11186-442099512-(Painting_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692788702-63126010c7577b68d90ac441.jpeg) ![11187-1398713543-(Professional 3D rendering_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692799804-63126010c7577b68d90ac441.jpeg) ![11188-1848306607-(Digital Artwork_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692775074-63126010c7577b68d90ac441.jpeg) ![11196-3753636034-(Photo_1-0000.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692798340-63126010c7577b68d90ac441.jpeg) ![00010.jpg](https://s3.amazonaws.com/moonup/production/uploads/1670692797359-63126010c7577b68d90ac441.jpeg)
eduyio/ppo-LunarLander-v2	eduyio	2022-12-10T19:07:49Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-10T19:07:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 259.90 +/- 18.83 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
admarcosai/ppo-Huggy	admarcosai	2022-12-10T19:03:05Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-10T19:02:57Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: dmarcos/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
uzn/ddpm-trucks	uzn	2022-12-10T18:50:41Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:uzn/truck", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-12-10T13:15:02Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: uzn/truck metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-trucks ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `uzn/truck` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/uzn/ddpm-trucks/tensorboard?#scalars)
alaaawad/sd-class-butterflies-64	alaaawad	2022-12-10T18:42:06Z	3	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-10T18:41:21Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('alaaawad/sd-class-butterflies-64') image = pipeline().images[0] image ```
Lilya/distilbert-base-uncased-finetuned-ner-invoiceSenderName	Lilya	2022-12-10T18:39:24Z	13	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-09T14:43:41Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner-invoiceSenderName results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner-invoiceSenderName This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0254 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9924 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| 0.0306 \| 1.0 \| 1956 \| 0.0273 \| 0.0 \| 0.0 \| 0.0 \| 0.9901 \| \| 0.0195 \| 2.0 \| 3912 \| 0.0240 \| 0.0 \| 0.0 \| 0.0 \| 0.9914 \| \| 0.0143 \| 3.0 \| 5868 \| 0.0251 \| 0.0 \| 0.0 \| 0.0 \| 0.9921 \| \| 0.0107 \| 4.0 \| 7824 \| 0.0254 \| 0.0 \| 0.0 \| 0.0 \| 0.9924 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1 - Datasets 2.3.2 - Tokenizers 0.10.3
alaaawad/sd-class-butterflies-32	alaaawad	2022-12-10T18:03:24Z	0	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-10T18:01:49Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('alaaawad/sd-class-butterflies-32') image = pipeline().images[0] image ```
austinzheng/ppo-Huggy	austinzheng	2022-12-10T18:01:49Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-10T18:01:26Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: austinzheng/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
bitsanlp/roberta-retrained-250k	bitsanlp	2022-12-10T17:18:00Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-12-10T15:35:24Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-retrained-250k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-retrained-250k This model is a fine-tuned version of [bitsanlp/roberta-retrained_100k](https://huggingface.co/bitsanlp/roberta-retrained_100k) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
yonas/stt_rw_conformer_ctc_large	yonas	2022-12-10T17:16:27Z	12	0	nemo	[ "nemo", "automatic-speech-recognition", "speech", "Kinyarwanda", "audio", "CTC", "Conformer", "Transformer", "NeMo", "pytorch", "rw", "dataset:mozilla-foundation/common_voice_11_0", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2022-12-02T13:08:08Z	--- language: - rw license: cc-by-4.0 library_name: nemo datasets: - mozilla-foundation/common_voice_11_0 thumbnail: null tags: - automatic-speech-recognition - speech - Kinyarwanda - audio - CTC - Conformer - Transformer - NeMo - pytorch model-index: - name: stt_rw_conformer_ctc_large results: [] --- ## Model Overview <DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("yonas/stt_rw_conformer_ctc_large") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="yonas/stt_rw_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 KHz Mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture <ADD SOME INFORMATION ABOUT THE ARCHITECTURE> ## Training <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC> ### Datasets <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)> ## Performance <LIST THE SCORES OF THE MODEL - OR USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS> ## Limitations <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL> Eg: Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## References <ADD ANY REFERENCES HERE AS NEEDED> [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

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