Split (1)

train · 789k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
Kirili4ik/mbart_ruDialogSum	Kirili4ik	2023-07-03T09:45:51Z	338	25	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "ru", "license:cc", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- language: - ru tags: - mbart inference: parameters: no_repeat_ngram_size: 4, num_beams: 5 datasets: - IlyaGusev/gazeta - samsum - samsum_(translated_into_Russian) widget: - text: > Джефф: Могу ли я обучить модель 🤗 Transformers на Amazon SageMaker? Филипп: Конечно, вы можете использовать новый контейнер для глубокого обучения HuggingFace. Джефф: Хорошо. Джефф: и как я могу начать? Джефф: где я могу найти документацию? Филипп: ок, ок, здесь можно найти все: https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face model-index: - name: mbart_ruDialogSum results: - task: name: Abstractive Dialogue Summarization type: abstractive-text-summarization dataset: name: SAMSum Corpus (translated to Russian) type: samsum metrics: - name: Validation ROGUE-1 type: rogue-1 value: 34.5 - name: Validation ROGUE-L type: rogue-l value: 33 - name: Test ROGUE-1 type: rogue-1 value: 31 - name: Test ROGUE-L type: rogue-l value: 28 license: cc --- ### 📝 Description MBart for Russian summarization fine-tuned for dialogues summarization. This model was firstly fine-tuned by [Ilya Gusev](https://hf.co/IlyaGusev) on [Gazeta dataset](https://huggingface.co/datasets/IlyaGusev/gazeta). We have fine tuned that model on [SamSum dataset](https://huggingface.co/datasets/samsum) translated to Russian using GoogleTranslateAPI 🤗 Moreover! We have implemented a ! telegram bot [@summarization_bot](https://t.me/summarization_bot) ! with the inference of this model. Add it to the chat and get summaries instead of dozens spam messages! 🤗 ### ❓ How to use with code ```python from transformers import MBartTokenizer, MBartForConditionalGeneration # Download model and tokenizer model_name = "Kirili4ik/mbart_ruDialogSum" tokenizer = AutoTokenizer.from_pretrained(model_name) model = MBartForConditionalGeneration.from_pretrained(model_name) model.eval() article_text = "..." input_ids = tokenizer( [article_text], max_length=600, padding="max_length", truncation=True, return_tensors="pt", )["input_ids"] output_ids = model.generate( input_ids=input_ids, top_k=0, num_beams=3, no_repeat_ngram_size=3 )[0] summary = tokenizer.decode(output_ids, skip_special_tokens=True) print(summary) ```
KJan05/q-FrozenLake-v1-4x4-noSlippery	KJan05	2023-07-03T09:39:59Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T09:39:56Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery 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="KJan05/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```
aronmal/Taxi-v3-Qtable	aronmal	2023-07-03T09:39:02Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T09:39:00Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-Qtable results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 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="aronmal/Taxi-v3-Qtable", 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"]) ```
aronmal/q-FrozenLake-v1-4x4-noSlippery	aronmal	2023-07-03T09:37:17Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T09:37:14Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery 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="aronmal/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```
SRDdev/ScriptForge_Plus	SRDdev	2023-07-03T09:36:48Z	132	1	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "gpt2", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T05:25:32Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation widget: - text: 10 Meditation Tips example_title: Health Example - text: Cooking red sauce pasta example_title: Cooking Example - text: Introduction to Keras example_title: Technology Example Tags: - text-generation metrics: - accuracy --- # ScriptForge_Plus ## 🖊️ Model description ScriptForge_Plus is a language model trained on a dataset of 5000 YouTube videos that cover different domains of AI. ScriptForge_Plus is a Causal language transformer. The model resembles the GPT2 architecture, the model is a Causal Language model meaning it predicts the probability of a sequence of words based on the preceding words in the sequence. It generates a probability distribution over the next word given the previous words, without incorporating future words. The goal of ScriptForge_Plus is to generate scripts for Youtube videos that are coherent, informative, and engaging. This can be useful for content creators who are looking for inspiration or who want to automate the process of generating video scripts. To use ScriptForge_Plus, users can provide a prompt or a starting sentence, and the model will generate a sequence of words that follow the context and style of the training data. Models - [ScriptForge_Plus](https://huggingface.co/SRDdev/ScriptForge_Plus) : AI content Model - [ScriptForge-small](https://huggingface.co/SRDdev/ScriptForge-medium) : Generalized Content Model More models are coming soon... ## 🛒 Intended uses The intended uses of ScriptForge_Plus include generating scripts for videos, providing inspiration for content creators, and automating the process of generating video scripts. ## 📝 How to use You can use this model directly with a pipeline for text generation. 1. __Load Model__ ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SRDdev/ScriptForge_Plus") model = AutoModelForCausalLM.from_pretrained("SRDdev/ScriptForge_Plus") ``` 2. __Pipeline__ ```python from transformers import pipeline generator = pipeline('text-generation', model= model , tokenizer=tokenizer) context = "What is Deep Learning" length_to_generate = 250 script = generator(context, max_length=length_to_generate, do_sample=True)[0]['generated_text'] script ``` <p style="opacity: 0.8">The model may generate random information as it is still in beta version</p> ## 🎈Limitations and bias > The model is trained on Youtube Scripts and will work better for that. It may also generate random information and users should be aware of that and cross-validate the results. ## Citations ``` @model{ Name=Shreyas Dixit framework=Pytorch Year=Jan 2023 Pipeline=text-generation Github=https://github.com/SRDdev LinkedIn=https://www.linkedin.com/in/srddev } ```
DucHaiten/DucHaiten-FANCYxFANCY	DucHaiten	2023-07-03T09:36:13Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-03T09:31:51Z	--- license: creativeml-openrail-m ---
daiwenbin/xlm-roberta-base-finetuned-panx-de-fr	daiwenbin	2023-07-03T09:28:37Z	134	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-03T09:18:25Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2083 - F1: 0.8465 ## 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: 24 - eval_batch_size: 24 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.36 \| 1.0 \| 715 \| 0.2279 \| 0.8163 \| \| 0.1862 \| 2.0 \| 1430 \| 0.1997 \| 0.8363 \| \| 0.1169 \| 3.0 \| 2145 \| 0.2083 \| 0.8465 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.13.3
sarada/t5-small-finetuned-xsum	sarada	2023-07-03T09:24:54Z	103	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	2023-07-03T09:21:13Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-xsum 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. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 61 \| 3.0039 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
vlkn/falcon_instruct_100	vlkn	2023-07-03T09:21:32Z	0	0	null	[ "tensorboard", "generated_from_trainer", "region:us" ]	null	2023-07-03T09:01:38Z	--- tags: - generated_from_trainer model-index: - name: falcon_instruct_100 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. --> # falcon_instruct_100 This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
joserodr68/q-FrozenLake-v1-4x4-noSlippery	joserodr68	2023-07-03T09:10:38Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T09:10:34Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery 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="joserodr68/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```
NancyAthghara23/red-panda-rpd	NancyAthghara23	2023-07-03T08:55:34Z	10	3	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-03T08:52:05Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Red-Panda-rpd Dreambooth model trained by NancyAthghara23 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: CVRGU151 Sample pictures of this concept: ![0](https://huggingface.co/NancyAthghara23/red-panda-rpd/resolve/main/sample_images/00004-3404897571.png) ![1](https://huggingface.co/NancyAthghara23/red-panda-rpd/resolve/main/sample_images/00006-1635300479.png)
Soojeong/female_hanbok_1e-7_ckpt_icb	Soojeong	2023-07-03T08:32:21Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-03T06:33:25Z	--- license: creativeml-openrail-m base_model: model/chilloutmix_NiPrunedFp16Fix instance_prompt: a photo of wearing hanbok tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Soojeong/female_hanbok_1e-7_ckpt_icb This is a dreambooth model derived from model/chilloutmix_NiPrunedFp16Fix. The weights were trained on a photo of wearing hanbok using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.
leomortensen/best-bissell-vacuum	leomortensen	2023-07-03T08:31:33Z	0	0	null	[ "region:us" ]	null	2023-06-02T10:33:31Z	# The 20 Best Bissell Vacuum Cleaners Worth Every Penny In 2023 In a market flooded with numerous brands of vacuum cleaners, Bissell stands out as a trusted and renowned name in the industry. With a rich history and a commitment to innovation, Bissell has earned its reputation for manufacturing high-quality and reliable vacuum cleaners. Whether you're dealing with pet hair, tackling tough stains, or simply maintaining a clean home, Bissell offers a wide range of vacuum cleaners to meet your specific needs. From powerful uprights to versatile handhelds, Bissell has a solution for every cleaning task. Let's dive in with [https://thekinglive.com](https://thekinglive.com) and discover why Bissell is the go-to brand for effective and efficient cleaning. Bissell best vacuum cleaner - A famous brand from the USA Bissell, a renowned brand of vacuum cleaners originating from the USA, has established itself as one of the best choices on the market. With over 140 years of experience in the industry, Bissell has perfected the art of creating high-performance cleaning solutions for homes. One of the standout features of Bissell vacuum cleaners is their exceptional cleaning power. Equipped with advanced technologies and strong suction capabilities, Bissell vacuums effectively remove dirt, dust, allergens, and even stubborn pet hair from various surfaces. Whether you have carpets, hardwood floors, or upholstery, Bissell offers models specifically designed to cater to your cleaning needs. Bissell's commitment to innovation is evident in its range of cutting-edge features. Many Bissell vacuums incorporate specialized brush rolls, HEPA filtration systems, and pet-specific tools to deliver superior cleaning results. They also prioritize user convenience, offering features like easy-empty dirt bins, swivel steering for maneuverability, and cordless options for hassle-free cleaning. Moreover, Bissell is known for its dedication to sustainability. They have implemented environmentally friendly practices, such as using recycled materials in their products and promoting energy-efficient designs. When considering a vacuum cleaner, Bissell's stellar reputation, extensive product line, and commitment to quality make it a brand that customers can rely on for exceptional cleaning performance and durability. ![](https://imgur.com/UDmAw9U.jpg) [TOP Rated 20 Best Bissell Vacuum Cleaners Worth Buying In 2023](https://thekinglive.com/what-is-the-best-rated-bissell-vacuum-cleaner-reviews.html) In this section, we will explore the top 20 best Bissell vacuum cleaners that are worth buying in 2023\. Join us as we unveil the top-rated Bissell vacuum cleaners that are worth every penny in 2023. Here are the Bissell vacuum reviews of the top 20 for you: 1. Bissell CleanView Swivel Rewind Pet Upright Vacuum Cleaner: Features powerful suction, specialized pet tools, and a swivel steering design for easy maneuverability. 2. Bissell Pet Hair Eraser Turbo Plus Upright Vacuum Cleaner: Equipped with a tangle-free brush roll, powerful suction, and specialized pet tools for effective pet hair removal. 3. Bissell CrossWave All-in-One Multi-Surface Wet Dry Vacuum Cleaner: Offers simultaneous vacuuming and mopping capabilities, suitable for cleaning multiple surfaces in one pass. 4. Bissell PowerEdge Pet Hardwood Floor Bagless Stick Vacuum Cleaner: Designed with a V-shaped nozzle for capturing pet hair and debris along edges and corners. 5. Bissell Iconpet Cordless Stick Vacuum Cleaner: Provides cordless convenience, strong suction power, and specialized pet tools for efficient cleaning. 6. Bissell CleanView Rewind Deluxe Upright Vacuum Cleaner: Features a powerful multi-cyclonic system, automatic cord rewind, and a wide cleaning path for quick and thorough cleaning. 7. Bissell ProHeat 2X Revolution Max Clean Pet Pro Full-Size Carpet Cleaner: Designed for deep cleaning carpets and tackling tough pet stains with HeatWave Technology and specialized pet cleaning tools. 8. Bissell PowerGlide Pet Hair Bagless Vacuum Cleaner: Equipped with strong suction, a tangle-free brush roll, and specialized pet tools for effective pet hair removal. 9. Bissell Featherweight Stick Lightweight Bagless Vacuum Cleaner: Offers lightweight and versatile cleaning with the ability to transform into a handheld vacuum. 10. Bissell MultiClean Wet/Dry Garage and Auto Vacuum Cleaner: Ideal for cleaning both wet and dry messes in garages and automobiles, featuring powerful suction and various attachments. 11. Bissell PowerFresh Steam Mop: It utilizes the power of steam to sanitize and clean hard floors, eliminating 99.9% of germs and bacteria. 12. Bissell Zing Bagged Canister Vacuum Cleaner: Features a lightweight and compact design, powerful suction, and a bagged system for easy disposal. 13. Bissell PowerLifter PowerBrush Upright Carpet Cleaner and Shampooer: Offers a deep-cleaning power brush and a removable nozzle for efficient carpet cleaning and spot treatment. 14. Bissell AirRam Cordless Stick Vacuum Cleaner: Provides cordless convenience, strong suction, and a compact design for easy maneuverability. 15. Bissell SpotClean ProHeat Portable Spot and Stain Carpet Cleaner: Designed for targeted spot cleaning, it features a built-in heater for effective stain removal. 16. Bissell CleanView Connect Robotic Vacuum Cleaner: Offers hands-free cleaning with Wi-Fi connectivity, triple-action cleaning, and a low-profile design for reaching under furniture. 17. Bissell PowerForce Helix Bagless Upright Vacuum Cleaner: Features a helix dirt separation system, strong suction, and a large-capacity dirt bin for efficient cleaning. 18. Bissell Garage Pro Wall-Mounted Wet Dry Car Vacuum: Ideal for cleaning vehicles and garages, it offers powerful suction, a wall-mountable design, and a blower function. 19. Bissell Symphony Vac and Steam 2-in-1 Vacuum and Steam Mop: This appliance combines vacuuming and steam cleaning in one appliance, allowing for simultaneous dirt and germ removal. 20. Bissell SpotBot Pet Hands-Free Spot and Stain Portable Carpet Cleaner: Designed for hands-free spot cleaning with automatic cleaning cycles and specialized pet cleaning solutions. ![](https://imgur.com/48um9wF.jpg) Frequently Asked Questions (FAQs) 1\. Is Bissell's product safe to use around kids and pets? Yes, Bissell products are generally safe to use around kids and pets when used according to the manufacturer's instructions. Bissell understands the importance of creating products that prioritize safety, especially in households with children and pets. However, it is always recommended to exercise caution and follow safety guidelines while using any cleaning products. Bissell takes various measures to ensure the safety of its products. For example, they use materials that are safe for use in homes and have undergone rigorous testing to meet safety standards. Additionally, Bissell designs its products with features like tangle-free brush rolls, specialized pet tools, and multi-level filtration systems to minimize potential hazards. It's also advisable to keep an eye on pets and children when operating cleaning equipment to prevent accidental injuries. 2\. How often should you change your Bissell vacuum filter? The frequency of changing the filter in your Bissell vacuum will depend on several factors, including the model of your vacuum, the level of usage, and the type of environment you are cleaning. However, as a general guideline, it is recommended to check the condition of your Bissell vacuum filter regularly and replace it as needed. Some Bissell vacuum models have washable filters that can be cleaned and reused, while others have replaceable filters that need to be replaced when they become visibly dirty or clogged. If you have a washable filter, it is typically recommended to clean it at least once a month, or more frequently if you have pets or if you're vacuuming in a particularly dusty environment. For replaceable filters, it's a good idea to consult the user manual or the manufacturer's recommendations for your specific Bissell vacuum model. They will provide guidelines on when to replace the filter based on average usage and environmental conditions. Regularly maintaining and replacing the filter in your Bissell vacuum is important to ensure optimal performance and good indoor air quality. A clean filter helps to capture dust, debris, and allergens effectively, ensuring efficient cleaning and reducing the strain on the vacuum's motor. 3\. How long does it take for your carpet to get dried after using Bissell? The drying time for carpets after using a Bissell carpet cleaner can vary depending on several factors, such as the type of carpet, the level of saturation during cleaning, the ambient humidity, and the airflow in the room. Generally, it can take anywhere from a few hours to a full day for carpets to completely dry. Based on our Bissell vacuum review, its carpet cleaners are designed to minimize excess water and facilitate quicker drying. They typically employ powerful suction and extraction capabilities to remove as much moisture as possible during the cleaning process. However, the drying time can still be influenced by the factors mentioned above. To help expedite the drying process, you can take the following steps: * Ensure proper ventilation: open windows or turn on fans to promote air circulation in the room. This helps evaporate moisture more quickly. * Use dehumidifiers: If the ambient humidity is high, running a dehumidifier can help remove excess moisture from the air, aiding in faster drying. * Avoid heavy foot traffic: Limit walking on the carpet while it is still damp to prevent dirt and debris from being tracked in and to avoid potential damage to the fibers. * Lift furniture: If possible, raise furniture legs or place aluminum foil or plastic film under the legs to prevent them from coming into contact with the damp carpet. By investing in the best Bissell vacuum, you can enjoy the benefits of powerful suction, advanced cleaning technologies, and specialized attachments that cater to your unique cleaning needs. With a [Bissell vacuum cleaner](https://www.tumblr.com/topvacuumcleaners/721457318981664768/bissell-pet-hair-eraser-handheld), you can confidently tackle dirt, pet hair, and other messes, ensuring a cleaner and more comfortable living environment for you and your loved ones.
Sourabh2/pixelcopter-s3	Sourabh2	2023-07-03T08:26:17Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T08:20:56Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter-s3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 32.30 +/- 28.84 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
hoanghoavienvo/roberta-base-Dep	hoanghoavienvo	2023-07-03T08:13:24Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-03T06:42:10Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: roberta-base-Dep 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-base-Dep This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4653 - Accuracy: 0.8333 - F1: 0.8992 ## 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: 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 235 \| 0.4592 \| 0.8217 \| 0.8911 \| \| No log \| 2.0 \| 470 \| 0.4116 \| 0.845 \| 0.9086 \| \| 0.2907 \| 3.0 \| 705 \| 0.4892 \| 0.8133 \| 0.8845 \| \| 0.2907 \| 4.0 \| 940 \| 0.4532 \| 0.835 \| 0.9011 \| \| 0.2694 \| 5.0 \| 1175 \| 0.4653 \| 0.8333 \| 0.8992 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jantz/IU-RVC_V2-300_Epochs	jantz	2023-07-03T08:12:30Z	0	1	null	[ "region:us" ]	null	2023-07-03T02:07:14Z	- Dataset: 1 hour of IU songs. - Vocal Separation: UVR5 model was used to separate vocals. The process involved: Kim Vocal 1 -> Reverb HQ -> Karaoke 2. - Additional Processing: Noise gate and manual touch-ups were performed in Audacity.
manmyung/q-Taxi-v3	manmyung	2023-07-03T08:02:34Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T08:02:32Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 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="manmyung/q-Taxi-v3", 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"]) ```
manmyung/q-FrozenLake-v1-4x4-noSlippery	manmyung	2023-07-03T07:53:56Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T07:53:54Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery 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="manmyung/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```
daiwenbin/xlm-roberta-base-finetuned-panx-de	daiwenbin	2023-07-03T07:50:20Z	122	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-03T07:35:18Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8327865206027916 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1947 - F1: 0.8328 ## 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: 24 - eval_batch_size: 24 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.3446 \| 1.0 \| 525 \| 0.2154 \| 0.8031 \| \| 0.1782 \| 2.0 \| 1050 \| 0.2004 \| 0.8228 \| \| 0.1128 \| 3.0 \| 1575 \| 0.1947 \| 0.8328 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.13.3
nomad-ai/poca-SoccerTwos-test	nomad-ai	2023-07-03T07:37:43Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-07-03T07:37:36Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: nomad-ai/poca-SoccerTwos-test 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
aronmal/ppo-Huggy	aronmal	2023-07-03T07:33:12Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-03T07:33:07Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # 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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: aronmal/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
heka-ai/e5-90k	heka-ai	2023-07-03T07:31:44Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-07-03T07:31:39Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # heka-ai/e5-90k This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('heka-ai/e5-90k') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=heka-ai/e5-90k) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 10000 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 100000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
tlapusan/bert-finetuned-win_file	tlapusan	2023-07-03T07:28:41Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-30T12:40:10Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-win_file 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. --> # bert-finetuned-win_file This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0149 - Precision: 0.9566 - Recall: 0.9935 - F1: 0.9747 - Accuracy: 0.9962 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1053 \| 1.0 \| 938 \| 0.0163 \| 0.9550 \| 0.9931 \| 0.9736 \| 0.9957 \| \| 0.0123 \| 2.0 \| 1876 \| 0.0149 \| 0.9566 \| 0.9935 \| 0.9747 \| 0.9962 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
vladkolev/distilroberta-base-finetuned-emotion	vladkolev	2023-07-03T07:27:32Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-21T08:29:38Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilroberta-base-finetuned-emotion 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. --> # distilroberta-base-finetuned-emotion This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3438 - Accuracy: 0.9004 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.615 \| 1.0 \| 748 \| 0.2832 \| 0.9004 \| \| 0.2716 \| 2.0 \| 1496 \| 0.2632 \| 0.9044 \| \| 0.1929 \| 3.0 \| 2244 \| 0.3124 \| 0.9071 \| \| 0.1559 \| 4.0 \| 2992 \| 0.3258 \| 0.8971 \| \| 0.1185 \| 5.0 \| 3740 \| 0.3438 \| 0.9004 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2
NasimB/gpt2-cl-rarity-sampling-5	NasimB	2023-07-03T07:01:49Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-03T04:30:07Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cl-rarity-sampling-5 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. --> # gpt2-cl-rarity-sampling-5 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.7342 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.6015 \| 0.05 \| 500 \| 5.8621 \| \| 5.3617 \| 0.11 \| 1000 \| 5.4637 \| \| 5.0237 \| 0.16 \| 1500 \| 5.2314 \| \| 4.8011 \| 0.22 \| 2000 \| 5.0828 \| \| 4.6311 \| 0.27 \| 2500 \| 4.9993 \| \| 4.504 \| 0.33 \| 3000 \| 4.9326 \| \| 4.3948 \| 0.38 \| 3500 \| 4.8809 \| \| 4.2939 \| 0.44 \| 4000 \| 4.8421 \| \| 4.2022 \| 0.49 \| 4500 \| 4.8057 \| \| 4.1111 \| 0.55 \| 5000 \| 4.7772 \| \| 4.0184 \| 0.6 \| 5500 \| 4.7492 \| \| 3.9458 \| 0.66 \| 6000 \| 4.7347 \| \| 3.8712 \| 0.71 \| 6500 \| 4.7195 \| \| 3.8079 \| 0.77 \| 7000 \| 4.7051 \| \| 3.7575 \| 0.82 \| 7500 \| 4.6946 \| \| 3.716 \| 0.88 \| 8000 \| 4.6904 \| \| 3.6978 \| 0.93 \| 8500 \| 4.6861 \| \| 3.6899 \| 0.99 \| 9000 \| 4.6848 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
vn0161/autotrain-bhoj-5n53-vq5m-71714138701	vn0161	2023-07-03T07:01:14Z	107	0	transformers	[ "transformers", "pytorch", "safetensors", "deberta", "text-classification", "autotrain", "en", "dataset:vn0161/autotrain-data-bhoj-5n53-vq5m", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-03T07:00:26Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain" datasets: - vn0161/autotrain-data-bhoj-5n53-vq5m co2_eq_emissions: emissions: 0.37493319480549947 --- # Model Trained Using AutoTrain - Problem type: Text Classification - CO2 Emissions (in grams): 0.3749 ## Validation Metrics loss: 0.35270485281944275 f1: 0.8472906403940886 precision: 0.8958333333333334 recall: 0.8037383177570093 auc: 0.9286837278364922 accuracy: 0.8551401869158879
SnorreStorjord/whisper-large-no	SnorreStorjord	2023-07-03T06:55:42Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "no", "dataset:NbAiLab/NPSC", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-27T11:44:47Z	--- language: - no license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - NbAiLab/NPSC model-index: - name: Whisper Large NO 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 Large no WIP This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the NbAiLab/NPSC 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - 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 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
nolanaatama/phngyfrmfvnghtstfrddysrvcv2300pchnlgspdrwb	nolanaatama	2023-07-03T06:51:29Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-03T06:37:26Z	--- license: creativeml-openrail-m ---
digiplay/CiderMix_ciderR	digiplay	2023-07-03T06:32:08Z	40	4	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-03T06:22:28Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/20051?modelVersionId=23816 Original Author's DEMO image : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/c56ad882-1dff-480c-64b4-3e2be810ed00/width=768/258748.jpeg)
Tiru8055/ppo-Pyramids	Tiru8055	2023-07-03T06:08:55Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-03T06:08:50Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Tiru8055/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Shubham09/falcon7b-test-updated-policies	Shubham09	2023-07-03T05:55:47Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-03T05:55:25Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0
google/umt5-base	google	2023-07-03T05:37:52Z	1,831	13	transformers	[ "transformers", "pytorch", "text2text-generation", "multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu", "dataset:mc4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-02T01:49:59Z	--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- [Google's UMT5](https://github.com/google-research/multilingual-t5) UMT5 is pretrained on the an updated version of [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 107 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. Note: UMT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) Other Community Checkpoints: [here](https://huggingface.co/models?search=umt5) Paper: [UniMax, Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi) Authors: by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant ## Abstract Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.
google/umt5-xl	google	2023-07-03T05:37:35Z	3,435	16	transformers	[ "transformers", "pytorch", "text2text-generation", "multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu", "dataset:mc4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-02T01:51:24Z	--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- [Google's UMT5](https://github.com/google-research/multilingual-t5) UMT5 is pretrained on the an updated version of [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 107 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. Note: UMT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) Other Community Checkpoints: [here](https://huggingface.co/models?search=umt5) Paper: [UniMax, Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi) Authors: by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant ## Abstract Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.
google/umt5-xxl	google	2023-07-03T05:37:17Z	286	19	transformers	[ "transformers", "pytorch", "text2text-generation", "multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu", "dataset:mc4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-02T02:15:00Z	--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- [Google's UMT5](https://github.com/google-research/multilingual-t5) UMT5 is pretrained on the an updated version of [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 107 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. Note: UMT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) Other Community Checkpoints: [here](https://huggingface.co/models?search=umt5) Paper: [UniMax, Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi) Authors: by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant ## Abstract Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.
emya/outputs	emya	2023-07-03T05:29:25Z	2	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-02T22:00:13Z	--- license: creativeml-openrail-m base_model: outputs instance_prompt: a logo of a service, named Mcdonald tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - emya/outputs This is a dreambooth model derived from outputs. The weights were trained on a logo of a service, named Mcdonald using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
Valent2809/classifier-model	Valent2809	2023-07-03T05:15:49Z	26	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-03T03:27:44Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Valent2809/classifier-model 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. --> # Valent2809/classifier-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3103 - Validation Loss: 0.4343 - Train Accuracy: 0.8478 - Epoch: 5 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 125, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6413 \| 0.5342 \| 0.7826 \| 0 \| \| 0.4819 \| 0.4865 \| 0.8043 \| 1 \| \| 0.3806 \| 0.4798 \| 0.7826 \| 2 \| \| 0.3400 \| 0.4362 \| 0.8261 \| 3 \| \| 0.3009 \| 0.4343 \| 0.8478 \| 4 \| \| 0.3103 \| 0.4343 \| 0.8478 \| 5 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
gautam1989/mt5-small-finetuned-amazon-en-es	gautam1989	2023-07-03T04:54:53Z	7	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-03T04:00:20Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: gautam1989/mt5-small-finetuned-amazon-en-es 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. --> # gautam1989/mt5-small-finetuned-amazon-en-es 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: 4.2895 - Validation Loss: 3.3954 - Epoch: 5 ## 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': 9672, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 9.6709 \| 4.4471 \| 0 \| \| 5.9597 \| 3.7763 \| 1 \| \| 5.1538 \| 3.6068 \| 2 \| \| 4.7554 \| 3.5175 \| 3 \| \| 4.4603 \| 3.4380 \| 4 \| \| 4.2895 \| 3.3954 \| 5 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
chriskim2273/IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta	chriskim2273	2023-07-03T04:50:05Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-03T04:13:01Z	--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta 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. --> # IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7219 ## 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: 16 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 45 \| 0.5443 \| \| No log \| 2.0 \| 90 \| 0.6332 \| \| No log \| 3.0 \| 135 \| 0.6942 \| \| No log \| 4.0 \| 180 \| 0.6725 \| \| No log \| 5.0 \| 225 \| 0.7219 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-48	hopkins	2023-07-03T04:27:43Z	65	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T04:09:58Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-48 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. --> # mbart-finetuned-eng-kor-48 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9901 - Bleu: 6.8796 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-49	hopkins	2023-07-03T04:25:49Z	51	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T04:12:15Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-49 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. --> # mbart-finetuned-eng-kor-49 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9908 - Bleu: 7.2223 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
deepghs/imgutils-models	deepghs	2023-07-03T04:12:18Z	0	6	null	[ "onnx", "dataset:deepghs/chafen_arknights", "dataset:deepghs/monochrome_danbooru", "license:mit", "region:us" ]	null	2023-03-11T08:37:38Z	--- license: mit datasets: - deepghs/chafen_arknights - deepghs/monochrome_danbooru metrics: - accuracy --- # imgutils-models This repository includes all the models in [deepghs/imgutils](https://github.com/deepghs/imgutils). ## LPIPS This model is used for clustering anime images (named `差分` in Chinese), based on [richzhang/PerceptualSimilarity](https://github.com/richzhang/PerceptualSimilarity), trained with dataset [deepghs/chafen_arknights(private)](https://huggingface.co/datasets/deepghs/chafen_arknights). When threshold is `0.45`, the [adjusted rand score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html) can reach `0.995`. File lists: * `lpips_diff.onnx`, feature difference. * `lpips_feature.onnx`, feature extracting. ## Monochrome These model is used for monochrome image classification, based on CNNs and Transformers, trained with dataset [deepghs/monochrome_danbooru(private)](https://huggingface.co/datasets/deepghs/monochrome_danbooru). The following are the checkpoints that have been formally put into use, all based on the Caformer architecture: \| Checkpoint \| Algorithm \| Safe Level \| Accuracy \| False Negative \| False Positive \| \|:----------------------------:\|:---------:\|:----------:\|:----------:\|:--------------:\|:--------------:\| \| monochrome-caformer-40 \| caformer \| 0 \| 96.41% \| 2.69% \| 0.89% \| \| monochrome-caformer-110 \| caformer \| 0 \| 96.97% \| 1.57% \| 1.46% \| \| monochrome-caformer_safe2-80 \| caformer \| 2 \| 94.84% \| 1.12% \| 4.03% \| \| monochrome-caformer_safe4-70 \| caformer \| 4 \| 94.28% \| 0.67% \| 5.04% \| `monochrome-caformer-110` has the best overall accuracy among them, but considering that this model is often used to screen out monochrome images and we want to screen out as many as possible without omission, we have also introduced weighted models (`safe2` and `safe4`). Although their overall accuracy has been slightly reduced, the probability of False Negative (misidentifying a monochrome image as a colored one) is lower, making them more suitable for batch screening. ## Deepdanbooru `deepdanbooru` is a model used to tag anime images. Here, we provide a table for tag classification called `deepdanbooru_tags.csv`, as well as an ONNX model (from [chinoll/deepdanbooru](https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags)). It's worth noting that due to the poor quality of the deepdanbooru model itself and the relatively old dataset, it is only for testing purposes and is not recommended to be used as the main classification model. We recommend using the `wd14` model instead, see: * https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags
anurag629/ppo-LunarLander-v2	anurag629	2023-07-03T04:08:06Z	1	1	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T04:07:47Z	--- 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: 249.10 +/- 12.74 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 ... ```
vineetsharma/whisper-base-finetuned-gtzan	vineetsharma	2023-07-03T04:03:36Z	49	0	transformers	[ "transformers", "pytorch", "whisper", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-03T01:16:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: whisper-base-finetuned-gtzan 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-base-finetuned-gtzan This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6867 - Accuracy: 0.87 ## 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: 16 - 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_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9075 \| 1.0 \| 57 \| 1.0000 \| 0.58 \| \| 0.4569 \| 2.0 \| 114 \| 0.6073 \| 0.83 \| \| 0.3761 \| 3.0 \| 171 \| 0.6410 \| 0.8 \| \| 0.3049 \| 4.0 \| 228 \| 0.4536 \| 0.86 \| \| 0.0284 \| 5.0 \| 285 \| 0.5120 \| 0.85 \| \| 0.0165 \| 6.0 \| 342 \| 0.4856 \| 0.89 \| \| 0.0087 \| 7.0 \| 399 \| 0.6814 \| 0.87 \| \| 0.0038 \| 8.0 \| 456 \| 0.7059 \| 0.85 \| \| 0.0032 \| 9.0 \| 513 \| 0.6831 \| 0.87 \| \| 0.0034 \| 10.0 \| 570 \| 0.6867 \| 0.87 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-deu-47	hopkins	2023-07-03T03:40:49Z	43	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:22:38Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-47 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. --> # mbart-finetuned-eng-deu-47 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6483 - Bleu: 20.8742 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-44	hopkins	2023-07-03T03:32:33Z	55	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:14:52Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-44 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. --> # mbart-finetuned-eng-kor-44 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9949 - Bleu: 6.8417 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
alphaduriendur/ner-deBERTa-v2-x-large	alphaduriendur	2023-07-03T03:27:58Z	56	0	transformers	[ "transformers", "pytorch", "tensorboard", "deberta-v2", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-03T01:44:37Z	--- license: mit tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: ner-deBERTa-v2-x-large results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: test args: conll2003 metrics: - name: Precision type: precision value: 0.7384370015948963 - name: Recall type: recall value: 0.7377832861189801 - name: F1 type: f1 value: 0.7381099991143388 - name: Accuracy type: accuracy value: 0.9460966943038657 --- <!-- 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. --> # ner-deBERTa-v2-x-large This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.3963 - Precision: 0.7384 - Recall: 0.7378 - F1: 0.7381 - Accuracy: 0.9461 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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 \| 219 \| 0.4082 \| 0.6932 \| 0.7087 \| 0.7009 \| 0.9386 \| \| No log \| 2.0 \| 439 \| 0.4299 \| 0.7467 \| 0.6948 \| 0.7198 \| 0.9426 \| \| 0.0094 \| 3.0 \| 658 \| 0.4086 \| 0.7435 \| 0.7072 \| 0.7249 \| 0.9441 \| \| 0.0094 \| 4.0 \| 878 \| 0.3873 \| 0.7426 \| 0.7420 \| 0.7423 \| 0.9461 \| \| 0.0054 \| 4.99 \| 1095 \| 0.3963 \| 0.7384 \| 0.7378 \| 0.7381 \| 0.9461 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-ind-45	hopkins	2023-07-03T03:16:25Z	53	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:58:21Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-45 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. --> # mbart-finetuned-eng-ind-45 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7646 - Bleu: 21.8949 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
samzoozi/atari_game	samzoozi	2023-07-03T03:04:22Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T03:03:41Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 718.00 +/- 220.55 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga samzoozi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga samzoozi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga samzoozi ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
jinlee74/distilbert-base-uncased-finetuned-emotions	jinlee74	2023-07-03T02:59:35Z	55	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-03T00:11:13Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotions results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9415 - name: F1 type: f1 value: 0.9416116671925132 --- <!-- 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-emotions This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2357 - Accuracy: 0.9415 - F1: 0.9416 ## 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: 64 - eval_batch_size: 64 - 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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.016 \| 1.0 \| 250 \| 0.2262 \| 0.9405 \| 0.9404 \| \| 0.011 \| 2.0 \| 500 \| 0.2357 \| 0.9415 \| 0.9416 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.1.0.dev20230316 - Datasets 2.12.0 - Tokenizers 0.13.3
AshtakaOOf/ssambatea-locon	AshtakaOOf	2023-07-03T02:58:58Z	0	1	null	[ "Text-to-Image", "anime", "lora", "locon", "lycoris", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-07-03T01:36:57Z	--- license: cc-by-nc-sa-4.0 tags: - Text-to-Image - anime - lora - locon - lycoris --- # SSAMBAtea Style LoCon ![example](https://media.discordapp.net/attachments/1019446913268973689/1125244643852947466/00115-24682990.png?width=500&height=620) ## token: ssambatea Trained on SSAMBAtea artwork This is a LoCon and require the LyCORIS extension to work I am planning on making a new improved dataset to do a V2 # License [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
hopkins/mbart-finetuned-eng-ind-42	hopkins	2023-07-03T02:57:04Z	60	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:39:13Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-42 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. --> # mbart-finetuned-eng-ind-42 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7642 - Bleu: 21.7118 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
alibaba-pai/pai-diffusion-artist-large-zh	alibaba-pai	2023-07-03T02:56:37Z	14	7	diffusers	[ "diffusers", "pytorch", "text-to-image", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-04-03T09:38:48Z	--- license: apache-2.0 tags: - pytorch - diffusers - text-to-image --- # Chinese Diffusion Model (Artist, 512 Resolution) ## 简介 Brief Introduction 我们开源了一个中文 Diffusion 模型，您可以直接输入中文提示词，我们为您呈现精美的艺术风格图片。本模型的默认分辨率是 512512。 We release a Chinese diffusion model, which is able to generate high-quality artistic images according to the prompts you input. The default resolution of this model is 512512. * Github: [EasyNLP](https://github.com/alibaba/EasyNLP) ## 使用 Usage 本模型支持 `diffusers`，可以参考以下范例代码： This model supports `diffusers`. Please refer to the following code: ```python from diffusers import StableDiffusionPipeline model_id = "alibaba-pai/pai-diffusion-artist-large-zh" pipe = StableDiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") prompt = "雾蒙蒙的日出在湖面上" image = pipe(prompt).images[0] image.save("result.png") ``` ## 作品展示 Gallery \| prompt: 浮岛，天空，白云，城堡，幻想世界 \| prompt: 红白玫瑰花，很多花瓣，绽放 \| \| ---------------------------------------- \| ---------------------------------- \| \| negative_prompt: 油画，模糊，雾蒙蒙 \| negative_prompt: \| \| ![](example1.png) \| ![](example2.png) \| \| prompt: 亭台楼阁，曲径通幽，水墨绘画，中国风 \| prompt: 阳光，彩虹，小白马 \| \| -------------------------------------------- \| -------------------------- \| \| negative_prompt: 油画，彩色 \| negative_prompt: \| \| ![](example3.png) \| ![](example4.png) \| ## 使用须知 Notice for Use 使用上述模型需遵守[AIGC模型开源特别条款](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html)。 If you want to use this model, please read this [document](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html) carefully and abide by the terms.
hopkins/mbart-finetuned-eng-deu-44	hopkins	2023-07-03T02:56:05Z	67	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:37:53Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-44 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. --> # mbart-finetuned-eng-deu-44 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6513 - Bleu: 20.8990 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
digiplay/Landscape_PhotoReal_v1	digiplay	2023-07-03T02:53:33Z	620	7	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-03T02:20:00Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/71987/landscapephotoreal?modelVersionId=76750 Sample images and prompt : magnificent scenery, wide landscape, sharp and crisp background, very beautiful landscape, old ruins buildings, fantasy, birdview, best quality, masterpiece, ultra high res, dark blue light, cloudy, photo, photorealistic, wide view, kkw-ph1 ![8fe78e3f-8861-4a05-b81b-ece37ef654b2.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/pn1BrZcWpE102SLyJq6Jh.jpeg) ![acb17a24-5b9a-4699-b0f5-2192523b78e8.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/PrOTKR4lrBWeRNFGhAyhd.jpeg) photorealistic modern living room, sharp and crisp background, sofa, low table, bookshelf, parks and buildings from window, wood and flower, beautiful landscape, best quality, masterpiece, hires, in the morning light, detailed lighting, blue sky, (((photo))), (((photorealistic))) ,kkw-ph1, wide shot, web meeting background ![89c995b5-e33d-4526-bb2f-5d1550a20084.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/RDKBVRmNV0SWXpz-o-D4F.jpeg)
hopkins/mbart-finetuned-eng-kor-41	hopkins	2023-07-03T02:39:04Z	63	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:25:38Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-41 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. --> # mbart-finetuned-eng-kor-41 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9881 - Bleu: 7.0463 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-deu-42	hopkins	2023-07-03T02:38:45Z	57	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:24:45Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-42 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. --> # mbart-finetuned-eng-deu-42 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6513 - Bleu: 20.8783 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-40	hopkins	2023-07-03T02:37:25Z	74	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:19:49Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-40 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. --> # mbart-finetuned-eng-kor-40 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9919 - Bleu: 7.0359 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-39	hopkins	2023-07-03T02:31:10Z	53	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:13:29Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-39 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. --> # mbart-finetuned-eng-kor-39 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9925 - Bleu: 6.7954 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
djifg/grow_classification_xlmr2	djifg	2023-07-03T02:28:32Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-03T01:59:42Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: grow_classification_xlmr2 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. --> # grow_classification_xlmr2 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5585 - Accuracy: 0.9309 - F1: 0.9297 ## 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: 64 - eval_batch_size: 64 - 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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.2832 \| 1.0 \| 436 \| 0.4686 \| 0.8870 \| 0.8872 \| \| 0.0717 \| 2.0 \| 872 \| 0.5915 \| 0.8964 \| 0.8950 \| \| 0.0374 \| 3.0 \| 1308 \| 0.4898 \| 0.9276 \| 0.9266 \| \| 0.0205 \| 4.0 \| 1744 \| 0.5333 \| 0.9271 \| 0.9257 \| \| 0.0101 \| 5.0 \| 2180 \| 0.5585 \| 0.9309 \| 0.9297 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jncraton/fastchat-t5-3b-v1.0-ct2-int8	jncraton	2023-07-03T02:24:58Z	3	2	transformers	[ "transformers", "license:apache-2.0", "region:us" ]	null	2023-07-03T01:59:59Z	--- license: apache-2.0 inference: false --- # FastChat-T5 Model Card ## Model details Model type: FastChat-T5 is an open-source chatbot trained by fine-tuning Flan-t5-xl (3B parameters) on user-shared conversations collected from ShareGPT. It is based on an encoder-decoder transformer architecture, and can autoregressively generate responses to users' inputs. Model date: FastChat-T5 was trained on April 2023. Organizations developing the model: The FastChat developers, primarily Dacheng Li, Lianmin Zheng and Hao Zhang. Paper or resources for more information: https://github.com/lm-sys/FastChat#FastChat-T5 License: Apache License 2.0 Where to send questions or comments about the model: https://github.com/lm-sys/FastChat/issues ## Intended use Primary intended uses: The primary use of FastChat-T5 is the commercial usage of large language models and chatbots. It can also be used for research purposes. Primary intended users: The primary intended users of the model are entrepreneurs and researchers in natural language processing, machine learning, and artificial intelligence. ## Training dataset 70K conversations collected from ShareGPT.com. ## Training details It processes the ShareGPT data in the form of question answering. Each ChatGPT response is processed as an answer, and previous conversations between the user and the ChatGPT are processed as the question. The encoder bi-directionally encodes a question into a hidden representation. The decoder uses cross-attention to attend to this representation while generating an answer uni-directionally from a start token. This model is fine-tuned for 3 epochs, with a max learning rate 2e-5, warmup ratio 0.03, and a cosine learning rate schedule. ## Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
AhmedTaha012/gptneo-TxtToJson-v0.1.16	AhmedTaha012	2023-07-03T02:16:00Z	79	1	transformers	[ "transformers", "pytorch", "tensorboard", "gpt_neo", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-03T01:43:59Z	--- license: mit tags: - generated_from_trainer model-index: - name: gptneo-TxtToJson-v0.1.16 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. --> # gptneo-TxtToJson-v0.1.16 This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1180 ## 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 88 \| 0.6397 \| \| No log \| 2.0 \| 176 \| 0.5158 \| \| No log \| 3.0 \| 264 \| 0.4083 \| \| No log \| 4.0 \| 352 \| 0.2929 \| \| No log \| 5.0 \| 440 \| 0.2384 \| \| 0.3687 \| 6.0 \| 528 \| 0.1904 \| \| 0.3687 \| 7.0 \| 616 \| 0.1638 \| \| 0.3687 \| 8.0 \| 704 \| 0.1485 \| \| 0.3687 \| 9.0 \| 792 \| 0.1405 \| \| 0.3687 \| 10.0 \| 880 \| 0.1277 \| \| 0.3687 \| 11.0 \| 968 \| 0.1232 \| \| 0.0629 \| 12.0 \| 1056 \| 0.1291 \| \| 0.0629 \| 13.0 \| 1144 \| 0.1159 \| \| 0.0629 \| 14.0 \| 1232 \| 0.1123 \| \| 0.0629 \| 15.0 \| 1320 \| 0.1160 \| \| 0.0629 \| 16.0 \| 1408 \| 0.1159 \| \| 0.0629 \| 17.0 \| 1496 \| 0.1195 \| \| 0.0137 \| 18.0 \| 1584 \| 0.1186 \| \| 0.0137 \| 19.0 \| 1672 \| 0.1179 \| \| 0.0137 \| 20.0 \| 1760 \| 0.1180 \| ### Framework versions - Transformers 4.27.4 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
stephansf/taxi	stephansf	2023-07-03T02:15:53Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T02:15:48Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi 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="stephansf/taxi", 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"]) ```
hopkins/mbart-finetuned-eng-deu-41	hopkins	2023-07-03T02:06:54Z	63	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:48:43Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-41 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. --> # mbart-finetuned-eng-deu-41 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6499 - Bleu: 21.0780 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-ind-38	hopkins	2023-07-03T02:06:04Z	65	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:52:19Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-38 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. --> # mbart-finetuned-eng-ind-38 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7718 - Bleu: 21.7535 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
digiplay/CityEdge_StyleMix_v1.44	digiplay	2023-07-03T02:03:34Z	310	4	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-03T01:27:43Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/63243/cityedgestylemix Sample images and prompt : 1girl, solo, long hair blown by wind,close-up ,long dress, green eyes, white stocking, lace, look at viewer, luxurious, elegant, extremely detailed, majestic, blurry, blurry background, tree, branch, cherry blossoms, butterfly, flower petals blown by wind, depth of field, ![adc5b9bd-9c9c-4eaa-8c2b-f3d80cf35de9.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/YQ55e6l6sTyMKfDMMEHjf.jpeg) 8k Angel sky,best quality , masterpiece, close up, ultra detailed ,upper body ![f7000d0a-06ba-4d2f-9cb2-99e66dce200c.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/gYaVpXPgV9iuGoZwVFkoY.jpeg) ![ff8d0f59-9b28-456d-a5dc-93e5ab494a9c.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/NY9c_GHK4Dph8mKh1BkOh.jpeg)
Soojeong/female_hanbok_1e-7_ckpt	Soojeong	2023-07-03T02:02:54Z	1	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-02T23:52:07Z	--- license: creativeml-openrail-m base_model: model/chilloutmix_NiPrunedFp16Fix instance_prompt: a photo of wearing hanbok tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Soojeong/female_hanbok_1e-7_ckpt This is a dreambooth model derived from model/chilloutmix_NiPrunedFp16Fix. The weights were trained on a photo of wearing hanbok using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.
hopkins/mbart-finetuned-eng-deu-38	hopkins	2023-07-03T01:51:51Z	63	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:33:49Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-38 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. --> # mbart-finetuned-eng-deu-38 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6498 - Bleu: 20.8314 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
Huggingfly/q-FrozenLake-v1-4x4-noSlippery	Huggingfly	2023-07-03T01:40:04Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T01:40:00Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery 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="Huggingfly/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```
hopkins/mbart-finetuned-eng-kor-35	hopkins	2023-07-03T01:35:52Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:18:24Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-35 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. --> # mbart-finetuned-eng-kor-35 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9930 - Bleu: 6.7448 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-34	hopkins	2023-07-03T01:33:22Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:15:53Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-34 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. --> # mbart-finetuned-eng-kor-34 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9937 - Bleu: 7.1397 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
ankitvyas/myBloomLoraModel	ankitvyas	2023-07-03T01:31:54Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-03T01:19:55Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
Ngadou/bert-sms-spam-dectector	Ngadou	2023-07-03T01:29:26Z	111	2	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "en", "dataset:Ngadou/Spam_SMS", "doi:10.57967/hf/0746", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-10T21:24:39Z	--- license: cc-by-4.0 datasets: - Ngadou/Spam_SMS language: - en metrics: - accuracy pipeline_tag: text-classification ---
hopkins/mbart-finetuned-eng-ind-35	hopkins	2023-07-03T01:17:55Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:00:12Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-35 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. --> # mbart-finetuned-eng-ind-35 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7681 - Bleu: 21.8412 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
SATOU0ZHU/qteamixQ_omegaFp16	SATOU0ZHU	2023-07-03T01:17:22Z	30	0	diffusers	[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-02T16:02:18Z	Diffusers version of QTea Model
hopkins/mbart-finetuned-eng-deu-37	hopkins	2023-07-03T01:11:58Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:53:43Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-37 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. --> # mbart-finetuned-eng-deu-37 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6509 - Bleu: 20.9509 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-33	hopkins	2023-07-03T00:53:13Z	102	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:39:30Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-33 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. --> # mbart-finetuned-eng-kor-33 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9920 - Bleu: 6.7823 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
dereklvlv/tsh-chp-150	dereklvlv	2023-07-03T00:50:51Z	4	0	peft	[ "peft", "region:us" ]	null	2023-07-03T00:45:07Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
hopkins/mbart-finetuned-eng-kor-32	hopkins	2023-07-03T00:47:13Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:33:42Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-32 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. --> # mbart-finetuned-eng-kor-32 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9944 - Bleu: 7.0531 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
FPHam/Rachel_Assistant_Editor_13b_GPTQ	FPHam	2023-07-03T00:34:14Z	6	5	transformers	[ "transformers", "llama", "text-generation", "LLM", "writing", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-02T23:27:48Z	--- tags: - LLM - llama - writing --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/eNZ7P8D.png" alt="FPHam's Harper" style="width: 50%; min-width: 300px; display: block; margin: auto;"> </div> <div style="display: flex; flex-direction: column; align-items: center;"> <p><a href="https://ko-fi.com/Q5Q5MOB4M">Buy Rachel Ko-fi (I'll give her a sip)</a></p> </div> <!-- header end --> ## Rachel is an assistant editor for your fiction. (v.2) This is a retraining of [Harper Assistant Editor](https://huggingface.co/FPHam/Harper_AssistantEditor_V1_13b_GPTQ) (who has been fired - she was just too young and inexperienced to do this job properly) Meet Rachel. She's a senior Assistant Editor at the famous FP Ham publishing company. IMPORTANT NOTE: Rachel uses ALPACA instruct If you want to get in touch with Rachel, a decent approach is to ask her to ``` ### Instruction: Rewrite the following text: <the text> ### Response: ``` Text should be a short paragraph at a time, few sentences. If you write long paragraphs, Rachel might feel the urge to shorten them. You see, her attention span is like that of a goldfish. You can give her specific instructions like: ``` Rewrite the following text using aristocratic British English, also known as 'Receiver Pronunciation' (RP) or 'BBC English': ``` Oh, and just a heads up, if you ask her to use profanity, well, the outcome is all on you because she is very eager to comply. Don't be surprised if Rachel jumps at the chance to correct your words, even if you thought they were perfectly fine. She's just that eager to make her mark, you know? ## Examples Here are some examples. The format was as just as above, an instruction to rewrite text follwing with the input text itself. The input text was (of course): ``` It is a truth universally acknowledged, that a single man in possession of a good fortune must be in want of a wife. However little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered as the rightful property of some one or other of their daughters. ``` Rewrite the following text in a modern, easy-to-read language ``` Everyone knows that if a guy has money and isn't married, people will think he needs to find a wife. So it's assumed that whoever owns all that cash should hook up with someone from the neighborhood. ``` Rewrite the following text the way Zapp Brannigan from Futurama talks ``` Y'know, it's a fact, sonny, that any fella with a bit o' money to spare has gotta find himself a missus. They all know the score - ain't no secret that when a new face rolls into town, there's gonna be some lucky lady claimin' him for her own. Ain't nothin' like it in their heads, mind you, but they don't need no fancy talkers to figure out who gets dibs on 'em. ``` Rewrite the following text reversing the genders ``` It is a truth universally acknowledged, that a single woman in possession of a good fortune must be in want of a husband. However little known the feelings or views of such a woman may be on her first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that she is considered as the rightful property of some one or other of their sons. ``` Rewrite the following text in Czech ``` Je pravda, kterou uznávají všichni, že jednotlivec vlastnící nějaké bohatství musí potřebovat ženu. Ačkoli pocity nebo názory takového muže mohou být málo známé okolním rodinám, když se poprvé setká s blízkým okolím, je tato pravda tak pevně zakořeněná ve mysli sousedních rodin, že je považován za majetek právem jedné ze jejich dcer. ``` Rewrite the following text using profanity, obscenity, and vulgar language ``` (okay, try it for yuourself if you must) ``` ## Technicalities Based on Meta LLaMA Trained using ALPACA Instruct : ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Rewrite the following text: <the text> ### Response: ``` You can have a conversation with Rachel about your text of course. Params. I am only happy if you experiment with the parameters. The parameters I used to test: Temperature: 0.7 ``` top_p: 0.9 top_k: 20 repetition penalty: 1.15 ```
hopkins/mbart-finetuned-eng-ind-32	hopkins	2023-07-03T00:33:11Z	121	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:19:33Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-32 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. --> # mbart-finetuned-eng-ind-32 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7674 - Bleu: 21.9161 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
FPHam/Harper_AssistantEditor_V1_13b_GPTQ	FPHam	2023-07-03T00:33:01Z	11	7	transformers	[ "transformers", "llama", "text-generation", "LLama", "writing", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-27T18:00:59Z	--- language: - en tags: - LLama - writing --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/Kd0Vpem.png" alt="FPHam's Harper" style="width: 30%; min-width: 200px; display: block; margin: auto;"> </div> <div style="display: flex; flex-direction: column; align-items: center;"> <p><a href="https://ko-fi.com/Q5Q5MOB4M">Buy Harper Ko-fi (I'll give her a sip)</a></p> </div> <!-- header end --> ## Harper is an assistant editor for your fiction. (v.1) Meet Harper. She's a young Assistant Editor at the famous FP Ham publishing company. This is test version 1 Note: Harper was fired and replaced with Rachel https://huggingface.co/FPHam/Rachel_Assistant_Editor_13b_GPTQ Harper v.1 uses Vicuna format: ``` User: Rewrite the following text: <the text> Assistant: ``` Text should be a short paragraph at a time, few sentences. If you write long paragraphs, Harper might feel the urge to shorten them. You see, her attention span is like that of the new generation or a goldfish.
hopkins/mbart-finetuned-eng-ind-31	hopkins	2023-07-03T00:31:21Z	119	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:17:51Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-31 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. --> # mbart-finetuned-eng-ind-31 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7681 - Bleu: 21.8896 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
samzoozi/q-FrozenLake-v1-4x4-noSlippery	samzoozi	2023-07-03T00:24:06Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T00:24:03Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery 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="samzoozi/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```
hopkins/mbart-finetuned-eng-deu-32	hopkins	2023-07-03T00:19:06Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:05:09Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-32 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. --> # mbart-finetuned-eng-deu-32 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6494 - Bleu: 21.1716 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-29	hopkins	2023-07-03T00:10:34Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-02T23:56:59Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-29 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. --> # mbart-finetuned-eng-kor-29 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9934 - Bleu: 7.0740 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-28	hopkins	2023-07-03T00:04:40Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-02T23:50:40Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-28 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. --> # mbart-finetuned-eng-kor-28 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9908 - Bleu: 6.8812 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
digiplay/Noosphere_v3	digiplay	2023-07-03T00:00:25Z	285	4	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-02T20:01:58Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: highly recommend 👍👍👍👍👍 useful text-to-image model 😉👍 https://civitai.com/models/36538?modelVersionId=107675 Sample image I made : ![2ee8c55f-69bb-4f97-a870-c1385d88e155.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/sfXbt8JMWfK0yApQBxlEd.jpeg) ![e38605a0-29c9-4788-9d24-bf72063f5f4f.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/qh4hQ7-HxCECZgVcsZ9fe.jpeg) ![00758439-b919-4c97-9f21-12f3ec423348.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/hIZoDeGGbMT_9Z2N3DSdm.jpeg) Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/63e59dcd-ee38-4de5-92a7-abefb36b934e/width=976/00536-1725005712.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/3f90967e-d1f8-4c04-b0e1-4ec6d7c04c90/width=944/00633-2110018611.jpeg)
hopkins/mbart-finetuned-eng-kor-26	hopkins	2023-07-03T00:00:17Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-02T23:44:37Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-26 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. --> # mbart-finetuned-eng-kor-26 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9925 - Bleu: 6.9558 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-ind-26	hopkins	2023-07-02T23:44:09Z	112	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-02T23:30:41Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-26 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. --> # mbart-finetuned-eng-ind-26 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7671 - Bleu: 22.0145 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-deu-27	hopkins	2023-07-02T23:34:12Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-02T23:20:13Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-27 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. --> # mbart-finetuned-eng-deu-27 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6502 - Bleu: 21.0549 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
AhmedTaha012/gptneo-TxtToJson-v0.1.10	AhmedTaha012	2023-07-02T23:31:21Z	112	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt_neo", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-02T22:54:01Z	--- license: mit tags: - generated_from_trainer model-index: - name: gptneo-TxtToJson-v0.1.10 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. --> # gptneo-TxtToJson-v0.1.10 This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2115 ## 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 414 \| 0.8261 \| \| 1.2433 \| 2.0 \| 828 \| 0.4342 \| \| 0.5005 \| 3.0 \| 1242 \| 0.2863 \| \| 0.2502 \| 4.0 \| 1656 \| 0.2305 \| \| 0.1616 \| 5.0 \| 2070 \| 0.2115 \| ### Framework versions - Transformers 4.27.4 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
hopkins/mbart-finetuned-eng-kor-24	hopkins	2023-07-02T23:25:30Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-02T23:07:23Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-24 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. --> # mbart-finetuned-eng-kor-24 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9896 - Bleu: 7.0455 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-25	hopkins	2023-07-02T23:21:35Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-02T23:03:50Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-25 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. --> # mbart-finetuned-eng-kor-25 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9949 - Bleu: 6.9771 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
LucasDash/dash-sd-v15	LucasDash	2023-07-02T23:17:58Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-28T22:12:10Z	--- license: creativeml-openrail-m --- # Dash SD 1.5 Experimetal Models ![cover](https://cdn-uploads.huggingface.co/production/uploads/638bf06ed274cbbad28448b0/rX20aV3BhBmqDaxa3XG6Z.png)
squeeze-ai-lab/sq-vicuna-13b-w4-s45	squeeze-ai-lab	2023-07-02T23:17:14Z	0	0	null	[ "arxiv:2306.07629", "region:us" ]	null	2023-06-26T19:19:33Z	SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. TLDR: Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization. But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method. Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance, as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach, we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality. For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf). ## Model description 4-bit quantized Vicuna-13B-v1.1 model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf). * Base Model: [Vicuna-13B-v1.1](https://huggingface.co/lmsys/vicuna-13b-delta-v1.1) (by [LMSYS](https://lmsys.org/)) * Bitwidth: 4-bit * Sparsity Level: 0.45% ## Links * Paper: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf) * Code: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM) --- license: other ---
squeeze-ai-lab/sq-vicuna-13b-w3-s0	squeeze-ai-lab	2023-07-02T23:16:13Z	0	2	null	[ "arxiv:2306.07629", "region:us" ]	null	2023-06-15T02:13:11Z	SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. TLDR: Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization. But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method. Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance, as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach, we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality. For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf). ## Model description 3-bit quantized Vicuna-13B-v1.1 model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf). * Base Model: [Vicuna-13B-v1.1](https://huggingface.co/lmsys/vicuna-13b-delta-v1.1) (by [LMSYS](https://lmsys.org/)) * Bitwidth: 3-bit * Sparsity Level: 0% (dense-only) ## Links * Paper: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf) * Code: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM) --- license: other ---
squeeze-ai-lab/sq-vicuna-7b-w4-s45	squeeze-ai-lab	2023-07-02T23:15:54Z	0	0	null	[ "arxiv:2306.07629", "region:us" ]	null	2023-06-26T19:19:08Z	SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. TLDR: Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization. But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method. Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance, as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach, we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality. For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf). ## Model description 4-bit quantized Vicuna-7B-v1.1 model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf). * Base Model: [Vicuna-7B-v1.1](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1) (by [LMSYS](https://lmsys.org/)) * Bitwidth: 4-bit * Sparsity Level: 0.45% ## Links * Paper: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf) * Code: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM) --- license: other ---
squeeze-ai-lab/sq-vicuna-7b-w3-s45	squeeze-ai-lab	2023-07-02T23:15:24Z	0	0	null	[ "arxiv:2306.07629", "region:us" ]	null	2023-06-26T19:19:22Z	SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. TLDR: Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization. But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method. Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance, as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach, we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality. For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf). ## Model description 3-bit quantized Vicuna-7B-v1.1 model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf). * Base Model: [Vicuna-7B-v1.1](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1) (by [LMSYS](https://lmsys.org/)) * Bitwidth: 3-bit * Sparsity Level: 0.45% ## Links * Paper: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf) * Code: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM) --- license: other ---
squeeze-ai-lab/sq-vicuna-7b-w4-s0	squeeze-ai-lab	2023-07-02T23:13:27Z	0	1	null	[ "arxiv:2306.07629", "region:us" ]	null	2023-06-15T02:13:28Z	SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. TLDR: Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization. But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method. Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance, as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach, we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality. For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf). ## Model description 4-bit quantized Vicuna-7B-v1.1 model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf). * Base Model: [Vicuna-7B-v1.1](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1) (by [LMSYS](https://lmsys.org/)) * Bitwidth: 4-bit * Sparsity Level: 0% (dense-only) ## Links * Paper: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf) * Code: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM) --- license: other ---
hopkins/mbart-finetuned-eng-ind-24	hopkins	2023-07-02T23:06:53Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-02T22:48:41Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-24 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. --> # mbart-finetuned-eng-ind-24 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7644 - Bleu: 22.0678 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

Subsets and Splits