librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-13 06:28:01	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-13 06:25:04	card stringlengths 11 1.01M
prince99/results1	prince99	2023-09-18T10:40:17Z	0	0	null	[ "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:finetune:meta-llama/Llama-2-13b-chat-hf", "region:us" ]	null	2023-09-18T10:40:13Z	--- base_model: meta-llama/Llama-2-13b-chat-hf tags: - generated_from_trainer model-index: - name: results1 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. --> # results1 This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5723 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
dvrkdvys/Ted_Cruz_G_157500	dvrkdvys	2023-09-18T10:13:47Z	0	0	null	[ "natural language generation", "voice conversion", "adversarial learning", "license:openrail", "region:us" ]	null	2023-09-18T10:07:47Z	--- license: openrail tags: - natural language generation - voice conversion - adversarial learning ---
nxa277/falcon-7b_medichat_finetuned_final	nxa277	2023-09-18T10:12:35Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-20T17:08:19Z	--- library_name: peft --- # Fine-tuned Falcon-7B Model for Medical Diagnosis ## Model Details ### Model Description: This model is a fine-tuned version of the Falcon-7B model. This model was fine-tuned on Gretel.ai's "Symtoms to Diagnosis" dataset, found at the following link: https://huggingface.co/datasets/gretelai/symptom_to_diagnosis, in order to provide preliminary diagnoses based on the symptom descriptions it is prompted with. ### Baseline Model: For more details about the baseline Falcon-7B model, please see the following links: 1. https://huggingface.co/tiiuae/falcon-7b 2. https://huggingface.co/blog/falcon ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
thomas0104/whisper-large-v2-nan-tw-only-char	thomas0104	2023-09-18T09:58:56Z	27	1	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "zh", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-large-v2", "base_model:finetune:openai/whisper-large-v2", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-03-01T08:08:34Z	--- language: - zh license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer base_model: openai/whisper-large-v2 model-index: - name: Whisper large-v2 nan-tw only char results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: mozilla-foundation/common_voice_11_0 nan-tw type: mozilla-foundation/common_voice_11_0 config: nan-tw split: test args: nan-tw metrics: - type: wer value: 45.37404580152672 name: Wer --- <!-- 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-v2 nan-tw only char This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 nan-tw dataset. It achieves the following results on the evaluation set: - Loss: 1.0351 - Wer: 45.3740 - Cer: 45.4573 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 0.6011 \| 1.04 \| 1000 \| 1.1100 \| 55.0229 \| 55.2068 \| \| 0.1773 \| 2.08 \| 2000 \| 1.2055 \| 58.6565 \| 58.7685 \| \| 0.015 \| 3.13 \| 3000 \| 1.0932 \| 48.6412 \| 48.8077 \| \| 0.0131 \| 5.01 \| 4000 \| 1.0531 \| 45.7099 \| 45.8497 \| \| 0.0001 \| 6.05 \| 5000 \| 1.0351 \| 45.3740 \| 45.4573 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2
anggtpd/emotion_recognition	anggtpd	2023-09-18T09:58:50Z	9	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-14T12:40:05Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion_recognition results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.45625 --- <!-- 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. --> # emotion_recognition This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6139 - Accuracy: 0.4562 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 5 \| 1.9416 \| 0.3438 \| \| 1.8445 \| 2.0 \| 10 \| 1.8517 \| 0.3937 \| \| 1.8445 \| 3.0 \| 15 \| 1.7436 \| 0.3875 \| \| 1.6748 \| 4.0 \| 20 \| 1.6654 \| 0.475 \| \| 1.6748 \| 5.0 \| 25 \| 1.6098 \| 0.5062 \| \| 1.5405 \| 6.0 \| 30 \| 1.5734 \| 0.4875 \| \| 1.5405 \| 7.0 \| 35 \| 1.5446 \| 0.4938 \| \| 1.4603 \| 8.0 \| 40 \| 1.5415 \| 0.4938 \| \| 1.4603 \| 9.0 \| 45 \| 1.5173 \| 0.5062 \| \| 1.4154 \| 10.0 \| 50 \| 1.4983 \| 0.5062 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
bardsai/finance-sentiment-fr-base	bardsai	2023-09-18T09:54:48Z	728	5	transformers	[ "transformers", "pytorch", "camembert", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "fr", "dataset:datasets/financial_phrasebank", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T09:53:50Z	--- language: fr tags: - text-classification - financial-sentiment-analysis - sentiment-analysis datasets: - datasets/financial_phrasebank metrics: - f1 - accuracy - precision - recall widget: - text: "Le chiffre d'affaires net a augmenté de 30 % pour atteindre 36 millions d'euros." example_title: "Example 1" - text: "Coup d'envoi du vendredi fou. Liste des promotions en magasin." example_title: "Example 2" - text: "Les actions de CDPROJEKT ont enregistré la plus forte baisse parmi les entreprises cotées au WSE." example_title: "Example 3" --- # Finance Sentiment FR (base) Finance Sentiment FR (base) is a model based on [camembert-base](https://huggingface.co/camembert-base) for analyzing sentiment of French financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu. The model will give you a three labels: positive, negative and neutral. ## How to use You can use this model directly with a pipeline for sentiment-analysis: ```python from transformers import pipeline nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-fr-base") nlp("Le chiffre d'affaires net a augmenté de 30 % pour atteindre 36 millions d'euros.") ``` ```bash [{'label': 'positive', 'score': 0.9987998807375955}] ``` ## Performance \| Metric \| Value \| \| --- \| ----------- \| \| f1 macro \| 0.963 \| \| precision macro \| 0.959 \| \| recall macro \| 0.967 \| \| accuracy \| 0.971 \| \| samples per second \| 140.8 \| (The performance was evaluated on RTX 3090 gpu) ## Changelog - 2023-09-18: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/) Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]
Karsinogenic69/emotion_classification	Karsinogenic69	2023-09-18T09:53:45Z	200	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-18T09:50:26Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion_classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.5 --- <!-- 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. --> # emotion_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4512 - Accuracy: 0.5 ## 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: - 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 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 1.4449 \| 0.4688 \| \| No log \| 2.0 \| 80 \| 1.4457 \| 0.4938 \| \| No log \| 3.0 \| 120 \| 1.3813 \| 0.5563 \| \| No log \| 4.0 \| 160 \| 1.5903 \| 0.4313 \| \| No log \| 5.0 \| 200 \| 1.4512 \| 0.5 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/yusa_kozue_idolmastercinderellagirls	CyberHarem	2023-09-18T09:40:26Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/yusa_kozue_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-18T09:22:35Z	--- license: mit datasets: - CyberHarem/yusa_kozue_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of yusa_kozue_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 7800, you need to download `7800/yusa_kozue_idolmastercinderellagirls.pt` as the embedding and `7800/yusa_kozue_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 7800, with the score of 0.924. The trigger words are: 1. `yusa_kozue_idolmastercinderellagirls` 2. `blonde_hair, ahoge, blush, green_eyes, twintails, low_twintails, long_hair, open_mouth` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:--------------------------------------------------------------\|:----------------------------------------------------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------\|:-------------------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 7800 \| 0.924 \| [Download](7800/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](7800/previews/pattern_1.png) \| [<NSFW, click to see>](7800/previews/pattern_2.png) \| ![pattern_3-7800](7800/previews/pattern_3.png) \| ![pattern_4-7800](7800/previews/pattern_4.png) \| ![pattern_5-7800](7800/previews/pattern_5.png) \| ![pattern_6-7800](7800/previews/pattern_6.png) \| ![pattern_7-7800](7800/previews/pattern_7.png) \| ![pattern_8-7800](7800/previews/pattern_8.png) \| ![pattern_9-7800](7800/previews/pattern_9.png) \| ![pattern_10-7800](7800/previews/pattern_10.png) \| ![pattern_11-7800](7800/previews/pattern_11.png) \| [<NSFW, click to see>](7800/previews/pattern_12.png) \| [<NSFW, click to see>](7800/previews/bikini.png) \| [<NSFW, click to see>](7800/previews/bondage.png) \| ![free-7800](7800/previews/free.png) \| ![maid-7800](7800/previews/maid.png) \| ![miko-7800](7800/previews/miko.png) \| [<NSFW, click to see>](7800/previews/nude.png) \| [<NSFW, click to see>](7800/previews/nude2.png) \| ![suit-7800](7800/previews/suit.png) \| ![yukata-7800](7800/previews/yukata.png) \| \| 7280 \| 0.909 \| [Download](7280/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](7280/previews/pattern_1.png) \| [<NSFW, click to see>](7280/previews/pattern_2.png) \| ![pattern_3-7280](7280/previews/pattern_3.png) \| ![pattern_4-7280](7280/previews/pattern_4.png) \| ![pattern_5-7280](7280/previews/pattern_5.png) \| ![pattern_6-7280](7280/previews/pattern_6.png) \| ![pattern_7-7280](7280/previews/pattern_7.png) \| ![pattern_8-7280](7280/previews/pattern_8.png) \| ![pattern_9-7280](7280/previews/pattern_9.png) \| ![pattern_10-7280](7280/previews/pattern_10.png) \| ![pattern_11-7280](7280/previews/pattern_11.png) \| [<NSFW, click to see>](7280/previews/pattern_12.png) \| [<NSFW, click to see>](7280/previews/bikini.png) \| [<NSFW, click to see>](7280/previews/bondage.png) \| ![free-7280](7280/previews/free.png) \| ![maid-7280](7280/previews/maid.png) \| ![miko-7280](7280/previews/miko.png) \| [<NSFW, click to see>](7280/previews/nude.png) \| [<NSFW, click to see>](7280/previews/nude2.png) \| ![suit-7280](7280/previews/suit.png) \| ![yukata-7280](7280/previews/yukata.png) \| \| 6760 \| 0.853 \| [Download](6760/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](6760/previews/pattern_1.png) \| [<NSFW, click to see>](6760/previews/pattern_2.png) \| ![pattern_3-6760](6760/previews/pattern_3.png) \| ![pattern_4-6760](6760/previews/pattern_4.png) \| ![pattern_5-6760](6760/previews/pattern_5.png) \| ![pattern_6-6760](6760/previews/pattern_6.png) \| ![pattern_7-6760](6760/previews/pattern_7.png) \| ![pattern_8-6760](6760/previews/pattern_8.png) \| ![pattern_9-6760](6760/previews/pattern_9.png) \| ![pattern_10-6760](6760/previews/pattern_10.png) \| ![pattern_11-6760](6760/previews/pattern_11.png) \| [<NSFW, click to see>](6760/previews/pattern_12.png) \| [<NSFW, click to see>](6760/previews/bikini.png) \| [<NSFW, click to see>](6760/previews/bondage.png) \| ![free-6760](6760/previews/free.png) \| ![maid-6760](6760/previews/maid.png) \| ![miko-6760](6760/previews/miko.png) \| [<NSFW, click to see>](6760/previews/nude.png) \| [<NSFW, click to see>](6760/previews/nude2.png) \| ![suit-6760](6760/previews/suit.png) \| ![yukata-6760](6760/previews/yukata.png) \| \| 6240 \| 0.920 \| [Download](6240/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](6240/previews/pattern_1.png) \| [<NSFW, click to see>](6240/previews/pattern_2.png) \| ![pattern_3-6240](6240/previews/pattern_3.png) \| ![pattern_4-6240](6240/previews/pattern_4.png) \| ![pattern_5-6240](6240/previews/pattern_5.png) \| ![pattern_6-6240](6240/previews/pattern_6.png) \| ![pattern_7-6240](6240/previews/pattern_7.png) \| ![pattern_8-6240](6240/previews/pattern_8.png) \| ![pattern_9-6240](6240/previews/pattern_9.png) \| ![pattern_10-6240](6240/previews/pattern_10.png) \| ![pattern_11-6240](6240/previews/pattern_11.png) \| [<NSFW, click to see>](6240/previews/pattern_12.png) \| [<NSFW, click to see>](6240/previews/bikini.png) \| [<NSFW, click to see>](6240/previews/bondage.png) \| ![free-6240](6240/previews/free.png) \| ![maid-6240](6240/previews/maid.png) \| ![miko-6240](6240/previews/miko.png) \| [<NSFW, click to see>](6240/previews/nude.png) \| [<NSFW, click to see>](6240/previews/nude2.png) \| ![suit-6240](6240/previews/suit.png) \| ![yukata-6240](6240/previews/yukata.png) \| \| 5720 \| 0.862 \| [Download](5720/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](5720/previews/pattern_1.png) \| [<NSFW, click to see>](5720/previews/pattern_2.png) \| ![pattern_3-5720](5720/previews/pattern_3.png) \| ![pattern_4-5720](5720/previews/pattern_4.png) \| ![pattern_5-5720](5720/previews/pattern_5.png) \| ![pattern_6-5720](5720/previews/pattern_6.png) \| ![pattern_7-5720](5720/previews/pattern_7.png) \| ![pattern_8-5720](5720/previews/pattern_8.png) \| ![pattern_9-5720](5720/previews/pattern_9.png) \| ![pattern_10-5720](5720/previews/pattern_10.png) \| ![pattern_11-5720](5720/previews/pattern_11.png) \| [<NSFW, click to see>](5720/previews/pattern_12.png) \| [<NSFW, click to see>](5720/previews/bikini.png) \| [<NSFW, click to see>](5720/previews/bondage.png) \| ![free-5720](5720/previews/free.png) \| ![maid-5720](5720/previews/maid.png) \| ![miko-5720](5720/previews/miko.png) \| [<NSFW, click to see>](5720/previews/nude.png) \| [<NSFW, click to see>](5720/previews/nude2.png) \| ![suit-5720](5720/previews/suit.png) \| ![yukata-5720](5720/previews/yukata.png) \| \| 5200 \| 0.784 \| [Download](5200/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](5200/previews/pattern_1.png) \| [<NSFW, click to see>](5200/previews/pattern_2.png) \| ![pattern_3-5200](5200/previews/pattern_3.png) \| ![pattern_4-5200](5200/previews/pattern_4.png) \| ![pattern_5-5200](5200/previews/pattern_5.png) \| ![pattern_6-5200](5200/previews/pattern_6.png) \| ![pattern_7-5200](5200/previews/pattern_7.png) \| ![pattern_8-5200](5200/previews/pattern_8.png) \| ![pattern_9-5200](5200/previews/pattern_9.png) \| ![pattern_10-5200](5200/previews/pattern_10.png) \| ![pattern_11-5200](5200/previews/pattern_11.png) \| [<NSFW, click to see>](5200/previews/pattern_12.png) \| [<NSFW, click to see>](5200/previews/bikini.png) \| [<NSFW, click to see>](5200/previews/bondage.png) \| ![free-5200](5200/previews/free.png) \| ![maid-5200](5200/previews/maid.png) \| ![miko-5200](5200/previews/miko.png) \| [<NSFW, click to see>](5200/previews/nude.png) \| [<NSFW, click to see>](5200/previews/nude2.png) \| ![suit-5200](5200/previews/suit.png) \| ![yukata-5200](5200/previews/yukata.png) \| \| 4680 \| 0.770 \| [Download](4680/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](4680/previews/pattern_1.png) \| [<NSFW, click to see>](4680/previews/pattern_2.png) \| ![pattern_3-4680](4680/previews/pattern_3.png) \| ![pattern_4-4680](4680/previews/pattern_4.png) \| ![pattern_5-4680](4680/previews/pattern_5.png) \| ![pattern_6-4680](4680/previews/pattern_6.png) \| ![pattern_7-4680](4680/previews/pattern_7.png) \| ![pattern_8-4680](4680/previews/pattern_8.png) \| ![pattern_9-4680](4680/previews/pattern_9.png) \| ![pattern_10-4680](4680/previews/pattern_10.png) \| ![pattern_11-4680](4680/previews/pattern_11.png) \| [<NSFW, click to see>](4680/previews/pattern_12.png) \| [<NSFW, click to see>](4680/previews/bikini.png) \| [<NSFW, click to see>](4680/previews/bondage.png) \| ![free-4680](4680/previews/free.png) \| ![maid-4680](4680/previews/maid.png) \| ![miko-4680](4680/previews/miko.png) \| [<NSFW, click to see>](4680/previews/nude.png) \| [<NSFW, click to see>](4680/previews/nude2.png) \| ![suit-4680](4680/previews/suit.png) \| ![yukata-4680](4680/previews/yukata.png) \| \| 4160 \| 0.749 \| [Download](4160/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](4160/previews/pattern_1.png) \| [<NSFW, click to see>](4160/previews/pattern_2.png) \| ![pattern_3-4160](4160/previews/pattern_3.png) \| ![pattern_4-4160](4160/previews/pattern_4.png) \| ![pattern_5-4160](4160/previews/pattern_5.png) \| ![pattern_6-4160](4160/previews/pattern_6.png) \| ![pattern_7-4160](4160/previews/pattern_7.png) \| ![pattern_8-4160](4160/previews/pattern_8.png) \| ![pattern_9-4160](4160/previews/pattern_9.png) \| ![pattern_10-4160](4160/previews/pattern_10.png) \| ![pattern_11-4160](4160/previews/pattern_11.png) \| [<NSFW, click to see>](4160/previews/pattern_12.png) \| [<NSFW, click to see>](4160/previews/bikini.png) \| [<NSFW, click to see>](4160/previews/bondage.png) \| ![free-4160](4160/previews/free.png) \| ![maid-4160](4160/previews/maid.png) \| ![miko-4160](4160/previews/miko.png) \| [<NSFW, click to see>](4160/previews/nude.png) \| [<NSFW, click to see>](4160/previews/nude2.png) \| ![suit-4160](4160/previews/suit.png) \| ![yukata-4160](4160/previews/yukata.png) \| \| 3640 \| 0.700 \| [Download](3640/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](3640/previews/pattern_1.png) \| [<NSFW, click to see>](3640/previews/pattern_2.png) \| ![pattern_3-3640](3640/previews/pattern_3.png) \| ![pattern_4-3640](3640/previews/pattern_4.png) \| ![pattern_5-3640](3640/previews/pattern_5.png) \| ![pattern_6-3640](3640/previews/pattern_6.png) \| ![pattern_7-3640](3640/previews/pattern_7.png) \| ![pattern_8-3640](3640/previews/pattern_8.png) \| ![pattern_9-3640](3640/previews/pattern_9.png) \| ![pattern_10-3640](3640/previews/pattern_10.png) \| ![pattern_11-3640](3640/previews/pattern_11.png) \| [<NSFW, click to see>](3640/previews/pattern_12.png) \| [<NSFW, click to see>](3640/previews/bikini.png) \| [<NSFW, click to see>](3640/previews/bondage.png) \| ![free-3640](3640/previews/free.png) \| ![maid-3640](3640/previews/maid.png) \| ![miko-3640](3640/previews/miko.png) \| [<NSFW, click to see>](3640/previews/nude.png) \| [<NSFW, click to see>](3640/previews/nude2.png) \| ![suit-3640](3640/previews/suit.png) \| ![yukata-3640](3640/previews/yukata.png) \| \| 3120 \| 0.802 \| [Download](3120/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](3120/previews/pattern_1.png) \| [<NSFW, click to see>](3120/previews/pattern_2.png) \| ![pattern_3-3120](3120/previews/pattern_3.png) \| ![pattern_4-3120](3120/previews/pattern_4.png) \| ![pattern_5-3120](3120/previews/pattern_5.png) \| ![pattern_6-3120](3120/previews/pattern_6.png) \| ![pattern_7-3120](3120/previews/pattern_7.png) \| ![pattern_8-3120](3120/previews/pattern_8.png) \| ![pattern_9-3120](3120/previews/pattern_9.png) \| ![pattern_10-3120](3120/previews/pattern_10.png) \| ![pattern_11-3120](3120/previews/pattern_11.png) \| [<NSFW, click to see>](3120/previews/pattern_12.png) \| [<NSFW, click to see>](3120/previews/bikini.png) \| [<NSFW, click to see>](3120/previews/bondage.png) \| ![free-3120](3120/previews/free.png) \| ![maid-3120](3120/previews/maid.png) \| ![miko-3120](3120/previews/miko.png) \| [<NSFW, click to see>](3120/previews/nude.png) \| [<NSFW, click to see>](3120/previews/nude2.png) \| ![suit-3120](3120/previews/suit.png) \| ![yukata-3120](3120/previews/yukata.png) \| \| 2600 \| 0.677 \| [Download](2600/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](2600/previews/pattern_1.png) \| [<NSFW, click to see>](2600/previews/pattern_2.png) \| ![pattern_3-2600](2600/previews/pattern_3.png) \| ![pattern_4-2600](2600/previews/pattern_4.png) \| ![pattern_5-2600](2600/previews/pattern_5.png) \| ![pattern_6-2600](2600/previews/pattern_6.png) \| ![pattern_7-2600](2600/previews/pattern_7.png) \| ![pattern_8-2600](2600/previews/pattern_8.png) \| ![pattern_9-2600](2600/previews/pattern_9.png) \| ![pattern_10-2600](2600/previews/pattern_10.png) \| ![pattern_11-2600](2600/previews/pattern_11.png) \| [<NSFW, click to see>](2600/previews/pattern_12.png) \| [<NSFW, click to see>](2600/previews/bikini.png) \| [<NSFW, click to see>](2600/previews/bondage.png) \| ![free-2600](2600/previews/free.png) \| ![maid-2600](2600/previews/maid.png) \| ![miko-2600](2600/previews/miko.png) \| [<NSFW, click to see>](2600/previews/nude.png) \| [<NSFW, click to see>](2600/previews/nude2.png) \| ![suit-2600](2600/previews/suit.png) \| ![yukata-2600](2600/previews/yukata.png) \| \| 2080 \| 0.705 \| [Download](2080/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](2080/previews/pattern_1.png) \| [<NSFW, click to see>](2080/previews/pattern_2.png) \| ![pattern_3-2080](2080/previews/pattern_3.png) \| ![pattern_4-2080](2080/previews/pattern_4.png) \| ![pattern_5-2080](2080/previews/pattern_5.png) \| ![pattern_6-2080](2080/previews/pattern_6.png) \| ![pattern_7-2080](2080/previews/pattern_7.png) \| ![pattern_8-2080](2080/previews/pattern_8.png) \| ![pattern_9-2080](2080/previews/pattern_9.png) \| ![pattern_10-2080](2080/previews/pattern_10.png) \| ![pattern_11-2080](2080/previews/pattern_11.png) \| [<NSFW, click to see>](2080/previews/pattern_12.png) \| [<NSFW, click to see>](2080/previews/bikini.png) \| [<NSFW, click to see>](2080/previews/bondage.png) \| ![free-2080](2080/previews/free.png) \| ![maid-2080](2080/previews/maid.png) \| ![miko-2080](2080/previews/miko.png) \| [<NSFW, click to see>](2080/previews/nude.png) \| [<NSFW, click to see>](2080/previews/nude2.png) \| ![suit-2080](2080/previews/suit.png) \| ![yukata-2080](2080/previews/yukata.png) \| \| 1560 \| 0.478 \| [Download](1560/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](1560/previews/pattern_1.png) \| [<NSFW, click to see>](1560/previews/pattern_2.png) \| ![pattern_3-1560](1560/previews/pattern_3.png) \| ![pattern_4-1560](1560/previews/pattern_4.png) \| ![pattern_5-1560](1560/previews/pattern_5.png) \| ![pattern_6-1560](1560/previews/pattern_6.png) \| ![pattern_7-1560](1560/previews/pattern_7.png) \| ![pattern_8-1560](1560/previews/pattern_8.png) \| ![pattern_9-1560](1560/previews/pattern_9.png) \| ![pattern_10-1560](1560/previews/pattern_10.png) \| ![pattern_11-1560](1560/previews/pattern_11.png) \| [<NSFW, click to see>](1560/previews/pattern_12.png) \| [<NSFW, click to see>](1560/previews/bikini.png) \| [<NSFW, click to see>](1560/previews/bondage.png) \| ![free-1560](1560/previews/free.png) \| ![maid-1560](1560/previews/maid.png) \| ![miko-1560](1560/previews/miko.png) \| [<NSFW, click to see>](1560/previews/nude.png) \| [<NSFW, click to see>](1560/previews/nude2.png) \| ![suit-1560](1560/previews/suit.png) \| ![yukata-1560](1560/previews/yukata.png) \| \| 1040 \| 0.492 \| [Download](1040/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](1040/previews/pattern_1.png) \| [<NSFW, click to see>](1040/previews/pattern_2.png) \| ![pattern_3-1040](1040/previews/pattern_3.png) \| ![pattern_4-1040](1040/previews/pattern_4.png) \| ![pattern_5-1040](1040/previews/pattern_5.png) \| ![pattern_6-1040](1040/previews/pattern_6.png) \| ![pattern_7-1040](1040/previews/pattern_7.png) \| ![pattern_8-1040](1040/previews/pattern_8.png) \| ![pattern_9-1040](1040/previews/pattern_9.png) \| ![pattern_10-1040](1040/previews/pattern_10.png) \| ![pattern_11-1040](1040/previews/pattern_11.png) \| [<NSFW, click to see>](1040/previews/pattern_12.png) \| [<NSFW, click to see>](1040/previews/bikini.png) \| [<NSFW, click to see>](1040/previews/bondage.png) \| ![free-1040](1040/previews/free.png) \| ![maid-1040](1040/previews/maid.png) \| ![miko-1040](1040/previews/miko.png) \| [<NSFW, click to see>](1040/previews/nude.png) \| [<NSFW, click to see>](1040/previews/nude2.png) \| ![suit-1040](1040/previews/suit.png) \| ![yukata-1040](1040/previews/yukata.png) \| \| 520 \| 0.554 \| [Download](520/yusa_kozue_idolmastercinderellagirls.zip) \| [<NSFW, click to see>](520/previews/pattern_1.png) \| [<NSFW, click to see>](520/previews/pattern_2.png) \| ![pattern_3-520](520/previews/pattern_3.png) \| ![pattern_4-520](520/previews/pattern_4.png) \| ![pattern_5-520](520/previews/pattern_5.png) \| ![pattern_6-520](520/previews/pattern_6.png) \| ![pattern_7-520](520/previews/pattern_7.png) \| ![pattern_8-520](520/previews/pattern_8.png) \| ![pattern_9-520](520/previews/pattern_9.png) \| ![pattern_10-520](520/previews/pattern_10.png) \| ![pattern_11-520](520/previews/pattern_11.png) \| [<NSFW, click to see>](520/previews/pattern_12.png) \| [<NSFW, click to see>](520/previews/bikini.png) \| [<NSFW, click to see>](520/previews/bondage.png) \| ![free-520](520/previews/free.png) \| ![maid-520](520/previews/maid.png) \| ![miko-520](520/previews/miko.png) \| [<NSFW, click to see>](520/previews/nude.png) \| [<NSFW, click to see>](520/previews/nude2.png) \| ![suit-520](520/previews/suit.png) \| ![yukata-520](520/previews/yukata.png) \|
kbbabu/flanT5_grammerly_ft	kbbabu	2023-09-18T09:36:15Z	4	0	transformers	[ "transformers", "generated_from_trainer", "dataset:grammarly/coedit", "base_model:google/flan-t5-large", "base_model:finetune:google/flan-t5-large", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2023-09-15T10:57:08Z	--- license: apache-2.0 base_model: google/flan-t5-large tags: - generated_from_trainer model-index: - name: coedit-finetuned results: [] datasets: - grammarly/coedit --- <!-- 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. --> # coedit-finetuned This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an CoEdit 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: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Vishal24/Llama-2-7b-chat-hf-fine-tuned-adapters	Vishal24	2023-09-18T09:35:24Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-18T08:11:24Z	--- 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: bfloat16 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: bfloat16 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0
dai152/1	dai152	2023-09-18T09:31:54Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2023-09-18T09:31:54Z	--- license: bigcode-openrail-m ---
junaid20/llama-fine-tuned-qa	junaid20	2023-09-18T09:26:36Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:finetune:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2023-09-18T09:18:02Z	--- base_model: NousResearch/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: llama-fine-tuned-qa 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. --> # llama-fine-tuned-qa This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
matelorg/q-FrozenLake-v1-4x4-noSlippery	matelorg	2023-09-18T09:25:08Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-18T09:25:06Z	--- 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="matelorg/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"]) ```
Naveen2910/Taxi-V3	Naveen2910	2023-09-18T09:14:34Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-18T09:14:33Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-V3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="Naveen2910/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"]) ```
Lamurias/ppo-Pyramids	Lamurias	2023-09-18T08:59:02Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-09-15T16:59:26Z	--- 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: Lamurias/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Charishma13/my_awesome_model	Charishma13	2023-09-18T08:48:22Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T07:35:11Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: my_awesome_model 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. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Cherishh/whisper-slu-1	Cherishh	2023-09-18T08:42:48Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-18T08:42:44Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - 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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
pmarar96/ddpm-celebahq-finetuned-butterflies-2epochs	pmarar96	2023-09-18T08:30:39Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-09-18T08:30:17Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('pmarar96/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```
nickprock/xlm-roberta-base-banking77-classification	nickprock	2023-09-18T08:30:35Z	120	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:banking77", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-16T11:02:45Z	--- license: mit tags: - generated_from_trainer datasets: - banking77 metrics: - accuracy widget: - text: 'Can I track the card you sent to me? ' example_title: Card Arrival Example - English - text: 'Posso tracciare la carta che mi avete spedito? ' example_title: Card Arrival Example - Italian - text: Can you explain your exchange rate policy to me? example_title: Exchange Rate Example - English - text: Potete spiegarmi la vostra politica dei tassi di cambio? example_title: Exchange Rate Example - Italian - text: I can't pay by my credit card example_title: Card Not Working Example - English - text: Non riesco a pagare con la mia carta di credito example_title: Card Not Working Example - Italian base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-banking77-classification results: - task: type: text-classification name: Text Classification dataset: name: banking77 type: banking77 config: default split: train args: default metrics: - type: accuracy value: 0.9321428571428572 name: Accuracy - task: type: text-classification name: Text Classification dataset: name: banking77 type: banking77 config: default split: test metrics: - type: accuracy value: 0.9321428571428572 name: Accuracy verified: true - type: precision value: 0.9339627666926148 name: Precision Macro verified: true - type: precision value: 0.9321428571428572 name: Precision Micro verified: true - type: precision value: 0.9339627666926148 name: Precision Weighted verified: true - type: recall value: 0.9321428571428572 name: Recall Macro verified: true - type: recall value: 0.9321428571428572 name: Recall Micro verified: true - type: recall value: 0.9321428571428572 name: Recall Weighted verified: true - type: f1 value: 0.9320514513719953 name: F1 Macro verified: true - type: f1 value: 0.9321428571428572 name: F1 Micro verified: true - type: f1 value: 0.9320514513719956 name: F1 Weighted verified: true - type: loss value: 0.30337899923324585 name: loss verified: true --- <!-- 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-banking77-classification This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.3034 - Accuracy: 0.9321 - F1 Score: 0.9321 ## Model description Experiment on a cross-language model to assess how accurate the classification is by using for fine tuning an English dataset but later querying the model in Italian. ## Intended uses & limitations The model can be used on text classification. In particular is fine tuned on banking domain for multilingual task. ## Training and evaluation data The dataset used is [banking77](https://huggingface.co/datasets/banking77) The 77 labels are: \|label\|intent\| \|:---:\|:----:\| \|0\|activate_my_card\| \|1\|age_limit\| \|2\|apple_pay_or_google_pay\| \|3\|atm_support\| \|4\|automatic_top_up\| \|5\|balance_not_updated_after_bank_transfer\| \|6\|balance_not_updated_after_cheque_or_cash_deposit\| \|7\|beneficiary_not_allowed\| \|8\|cancel_transfer\| \|9\|card_about_to_expire\| \|10\|card_acceptance\| \|11\|card_arrival\| \|12\|card_delivery_estimate\| \|13\|card_linking\| \|14\|card_not_working\| \|15\|card_payment_fee_charged\| \|16\|card_payment_not_recognised\| \|17\|card_payment_wrong_exchange_rate\| \|18\|card_swallowed\| \|19\|cash_withdrawal_charge\| \|20\|cash_withdrawal_not_recognised\| \|21\|change_pin\| \|22\|compromised_card\| \|23\|contactless_not_working\| \|24\|country_support\| \|25\|declined_card_payment\| \|26\|declined_cash_withdrawal\| \|27\|declined_transfer\| \|28\|direct_debit_payment_not_recognised\| \|29\|disposable_card_limits\| \|30\|edit_personal_details\| \|31\|exchange_charge\| \|32\|exchange_rate\| \|33\|exchange_via_app\| \|34\|extra_charge_on_statement\| \|35\|failed_transfer\| \|36\|fiat_currency_support\| \|37\|get_disposable_virtual_card\| \|38\|get_physical_card\| \|39\|getting_spare_card\| \|40\|getting_virtual_card\| \|41\|lost_or_stolen_card\| \|42\|lost_or_stolen_phone\| \|43\|order_physical_card\| \|44\|passcode_forgotten\| \|45\|pending_card_payment\| \|46\|pending_cash_withdrawal\| \|47\|pending_top_up\| \|48\|pending_transfer\| \|49\|pin_blocked\| \|50\|receiving_money\| \|51\|Refund_not_showing_up\| \|52\|request_refund\| \|53\|reverted_card_payment?\| \|54\|supported_cards_and_currencies\| \|55\|terminate_account\| \|56\|top_up_by_bank_transfer_charge\| \|57\|top_up_by_card_charge\| \|58\|top_up_by_cash_or_cheque\| \|59\|top_up_failed\| \|60\|top_up_limits\| \|61\|top_up_reverted\| \|62\|topping_up_by_card\| \|63\|transaction_charged_twice\| \|64\|transfer_fee_charged\| \|65\|transfer_into_account\| \|66\|transfer_not_received_by_recipient\| \|67\|transfer_timing\| \|68\|unable_to_verify_identity\| \|69\|verify_my_identity\| \|70\|verify_source_of_funds\| \|71\|verify_top_up\| \|72\|virtual_card_not_working\| \|73\|visa_or_mastercard\| \|74\|why_verify_identity\| \|75\|wrong_amount_of_cash_received\| \|76\|wrong_exchange_rate_for_cash_withdrawal\| ## Training procedure ``` from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/xlm-roberta-base-banking77-classification") pipe("Non riesco a pagare con la carta di credito") ``` ### 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\| \| 3.8002 \| 1.0 \| 157 \| 2.7771 \| 0.5159 \| 0.4483 \| \| 2.4006 \| 2.0 \| 314 \| 1.6937 \| 0.7140 \| 0.6720 \| \| 1.4633 \| 3.0 \| 471 \| 1.0385 \| 0.8308 \| 0.8153 \| \| 0.9234 \| 4.0 \| 628 \| 0.7008 \| 0.8789 \| 0.8761 \| \| 0.6163 \| 5.0 \| 785 \| 0.5029 \| 0.9068 \| 0.9063 \| \| 0.4282 \| 6.0 \| 942 \| 0.4084 \| 0.9123 \| 0.9125 \| \| 0.3203 \| 7.0 \| 1099 \| 0.3515 \| 0.9253 \| 0.9253 \| \| 0.245 \| 8.0 \| 1256 \| 0.3295 \| 0.9227 \| 0.9225 \| \| 0.1863 \| 9.0 \| 1413 \| 0.3092 \| 0.9269 \| 0.9269 \| \| 0.1518 \| 10.0 \| 1570 \| 0.2901 \| 0.9338 \| 0.9338 \| \| 0.1179 \| 11.0 \| 1727 \| 0.2938 \| 0.9318 \| 0.9319 \| \| 0.0969 \| 12.0 \| 1884 \| 0.2906 \| 0.9328 \| 0.9328 \| \| 0.0805 \| 13.0 \| 2041 \| 0.2963 \| 0.9295 \| 0.9295 \| \| 0.063 \| 14.0 \| 2198 \| 0.2998 \| 0.9289 \| 0.9288 \| \| 0.0554 \| 15.0 \| 2355 \| 0.2933 \| 0.9351 \| 0.9349 \| \| 0.046 \| 16.0 \| 2512 \| 0.2960 \| 0.9328 \| 0.9326 \| \| 0.04 \| 17.0 \| 2669 \| 0.3032 \| 0.9318 \| 0.9318 \| \| 0.035 \| 18.0 \| 2826 \| 0.3061 \| 0.9312 \| 0.9312 \| \| 0.0317 \| 19.0 \| 2983 \| 0.3030 \| 0.9331 \| 0.9330 \| \| 0.0315 \| 20.0 \| 3140 \| 0.3034 \| 0.9321 \| 0.9321 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Vicky0522/RSFNet-models	Vicky0522	2023-09-18T08:28:16Z	0	0	null	[ "arxiv:2303.08682", "region:us" ]	null	2023-09-12T14:21:04Z	Pretrained models for RSFNet Paper: https://arxiv.org/abs/2303.08682 Code: https://github.com/Vicky0522/RSFNet If our work is helpful for your research, please consider citing: ``` @article{oywq2023rsfnet, title={RSFNet: A white-Box image retouching approach using region-specific color filters}, author={Wenqi Ouyang and Yi Dong and Xiaoyang Kang and Peiran Ren and Xin Xu and Xuansong Xie}, journal={https://arxiv.org/abs/2303.08682}, year={2023} } ```
CyberHarem/mukai_takumi_idolmastercinderellagirls	CyberHarem	2023-09-18T08:27:50Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/mukai_takumi_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-18T08:03:55Z	--- license: mit datasets: - CyberHarem/mukai_takumi_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of mukai_takumi_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 6480, you need to download `6480/mukai_takumi_idolmastercinderellagirls.pt` as the embedding and `6480/mukai_takumi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 6480, with the score of 0.806. The trigger words are: 1. `mukai_takumi_idolmastercinderellagirls` 2. `long_hair, breasts, blush, black_hair, large_breasts, brown_hair, cleavage, bangs, collarbone, green_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:----------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 8100 \| 0.797 \| [Download](8100/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-8100](8100/previews/pattern_1.png) \| ![pattern_2-8100](8100/previews/pattern_2.png) \| [<NSFW, click to see>](8100/previews/pattern_3.png) \| ![pattern_4-8100](8100/previews/pattern_4.png) \| ![pattern_5-8100](8100/previews/pattern_5.png) \| [<NSFW, click to see>](8100/previews/pattern_6.png) \| ![pattern_7-8100](8100/previews/pattern_7.png) \| ![pattern_8-8100](8100/previews/pattern_8.png) \| ![pattern_9-8100](8100/previews/pattern_9.png) \| ![pattern_10-8100](8100/previews/pattern_10.png) \| ![pattern_11-8100](8100/previews/pattern_11.png) \| ![pattern_12-8100](8100/previews/pattern_12.png) \| ![bikini-8100](8100/previews/bikini.png) \| [<NSFW, click to see>](8100/previews/bondage.png) \| ![free-8100](8100/previews/free.png) \| ![maid-8100](8100/previews/maid.png) \| ![miko-8100](8100/previews/miko.png) \| [<NSFW, click to see>](8100/previews/nude.png) \| [<NSFW, click to see>](8100/previews/nude2.png) \| ![suit-8100](8100/previews/suit.png) \| ![yukata-8100](8100/previews/yukata.png) \| \| 7560 \| 0.783 \| [Download](7560/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-7560](7560/previews/pattern_1.png) \| ![pattern_2-7560](7560/previews/pattern_2.png) \| [<NSFW, click to see>](7560/previews/pattern_3.png) \| ![pattern_4-7560](7560/previews/pattern_4.png) \| ![pattern_5-7560](7560/previews/pattern_5.png) \| [<NSFW, click to see>](7560/previews/pattern_6.png) \| ![pattern_7-7560](7560/previews/pattern_7.png) \| ![pattern_8-7560](7560/previews/pattern_8.png) \| ![pattern_9-7560](7560/previews/pattern_9.png) \| ![pattern_10-7560](7560/previews/pattern_10.png) \| ![pattern_11-7560](7560/previews/pattern_11.png) \| ![pattern_12-7560](7560/previews/pattern_12.png) \| ![bikini-7560](7560/previews/bikini.png) \| [<NSFW, click to see>](7560/previews/bondage.png) \| ![free-7560](7560/previews/free.png) \| ![maid-7560](7560/previews/maid.png) \| ![miko-7560](7560/previews/miko.png) \| [<NSFW, click to see>](7560/previews/nude.png) \| [<NSFW, click to see>](7560/previews/nude2.png) \| ![suit-7560](7560/previews/suit.png) \| ![yukata-7560](7560/previews/yukata.png) \| \| 7020 \| 0.736 \| [Download](7020/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-7020](7020/previews/pattern_1.png) \| ![pattern_2-7020](7020/previews/pattern_2.png) \| [<NSFW, click to see>](7020/previews/pattern_3.png) \| ![pattern_4-7020](7020/previews/pattern_4.png) \| ![pattern_5-7020](7020/previews/pattern_5.png) \| [<NSFW, click to see>](7020/previews/pattern_6.png) \| ![pattern_7-7020](7020/previews/pattern_7.png) \| ![pattern_8-7020](7020/previews/pattern_8.png) \| ![pattern_9-7020](7020/previews/pattern_9.png) \| ![pattern_10-7020](7020/previews/pattern_10.png) \| ![pattern_11-7020](7020/previews/pattern_11.png) \| ![pattern_12-7020](7020/previews/pattern_12.png) \| ![bikini-7020](7020/previews/bikini.png) \| [<NSFW, click to see>](7020/previews/bondage.png) \| ![free-7020](7020/previews/free.png) \| ![maid-7020](7020/previews/maid.png) \| ![miko-7020](7020/previews/miko.png) \| [<NSFW, click to see>](7020/previews/nude.png) \| [<NSFW, click to see>](7020/previews/nude2.png) \| ![suit-7020](7020/previews/suit.png) \| ![yukata-7020](7020/previews/yukata.png) \| \| 6480 \| 0.806 \| [Download](6480/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-6480](6480/previews/pattern_1.png) \| ![pattern_2-6480](6480/previews/pattern_2.png) \| [<NSFW, click to see>](6480/previews/pattern_3.png) \| ![pattern_4-6480](6480/previews/pattern_4.png) \| ![pattern_5-6480](6480/previews/pattern_5.png) \| [<NSFW, click to see>](6480/previews/pattern_6.png) \| ![pattern_7-6480](6480/previews/pattern_7.png) \| ![pattern_8-6480](6480/previews/pattern_8.png) \| ![pattern_9-6480](6480/previews/pattern_9.png) \| ![pattern_10-6480](6480/previews/pattern_10.png) \| ![pattern_11-6480](6480/previews/pattern_11.png) \| ![pattern_12-6480](6480/previews/pattern_12.png) \| ![bikini-6480](6480/previews/bikini.png) \| [<NSFW, click to see>](6480/previews/bondage.png) \| ![free-6480](6480/previews/free.png) \| ![maid-6480](6480/previews/maid.png) \| ![miko-6480](6480/previews/miko.png) \| [<NSFW, click to see>](6480/previews/nude.png) \| [<NSFW, click to see>](6480/previews/nude2.png) \| ![suit-6480](6480/previews/suit.png) \| ![yukata-6480](6480/previews/yukata.png) \| \| 5940 \| 0.766 \| [Download](5940/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-5940](5940/previews/pattern_1.png) \| ![pattern_2-5940](5940/previews/pattern_2.png) \| [<NSFW, click to see>](5940/previews/pattern_3.png) \| ![pattern_4-5940](5940/previews/pattern_4.png) \| ![pattern_5-5940](5940/previews/pattern_5.png) \| [<NSFW, click to see>](5940/previews/pattern_6.png) \| ![pattern_7-5940](5940/previews/pattern_7.png) \| ![pattern_8-5940](5940/previews/pattern_8.png) \| ![pattern_9-5940](5940/previews/pattern_9.png) \| ![pattern_10-5940](5940/previews/pattern_10.png) \| ![pattern_11-5940](5940/previews/pattern_11.png) \| ![pattern_12-5940](5940/previews/pattern_12.png) \| ![bikini-5940](5940/previews/bikini.png) \| [<NSFW, click to see>](5940/previews/bondage.png) \| ![free-5940](5940/previews/free.png) \| ![maid-5940](5940/previews/maid.png) \| ![miko-5940](5940/previews/miko.png) \| [<NSFW, click to see>](5940/previews/nude.png) \| [<NSFW, click to see>](5940/previews/nude2.png) \| ![suit-5940](5940/previews/suit.png) \| ![yukata-5940](5940/previews/yukata.png) \| \| 5400 \| 0.792 \| [Download](5400/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-5400](5400/previews/pattern_1.png) \| ![pattern_2-5400](5400/previews/pattern_2.png) \| [<NSFW, click to see>](5400/previews/pattern_3.png) \| ![pattern_4-5400](5400/previews/pattern_4.png) \| ![pattern_5-5400](5400/previews/pattern_5.png) \| [<NSFW, click to see>](5400/previews/pattern_6.png) \| ![pattern_7-5400](5400/previews/pattern_7.png) \| ![pattern_8-5400](5400/previews/pattern_8.png) \| ![pattern_9-5400](5400/previews/pattern_9.png) \| ![pattern_10-5400](5400/previews/pattern_10.png) \| ![pattern_11-5400](5400/previews/pattern_11.png) \| ![pattern_12-5400](5400/previews/pattern_12.png) \| ![bikini-5400](5400/previews/bikini.png) \| [<NSFW, click to see>](5400/previews/bondage.png) \| ![free-5400](5400/previews/free.png) \| ![maid-5400](5400/previews/maid.png) \| ![miko-5400](5400/previews/miko.png) \| [<NSFW, click to see>](5400/previews/nude.png) \| [<NSFW, click to see>](5400/previews/nude2.png) \| ![suit-5400](5400/previews/suit.png) \| ![yukata-5400](5400/previews/yukata.png) \| \| 4860 \| 0.794 \| [Download](4860/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-4860](4860/previews/pattern_1.png) \| ![pattern_2-4860](4860/previews/pattern_2.png) \| [<NSFW, click to see>](4860/previews/pattern_3.png) \| ![pattern_4-4860](4860/previews/pattern_4.png) \| ![pattern_5-4860](4860/previews/pattern_5.png) \| [<NSFW, click to see>](4860/previews/pattern_6.png) \| ![pattern_7-4860](4860/previews/pattern_7.png) \| ![pattern_8-4860](4860/previews/pattern_8.png) \| ![pattern_9-4860](4860/previews/pattern_9.png) \| ![pattern_10-4860](4860/previews/pattern_10.png) \| ![pattern_11-4860](4860/previews/pattern_11.png) \| ![pattern_12-4860](4860/previews/pattern_12.png) \| ![bikini-4860](4860/previews/bikini.png) \| [<NSFW, click to see>](4860/previews/bondage.png) \| ![free-4860](4860/previews/free.png) \| ![maid-4860](4860/previews/maid.png) \| ![miko-4860](4860/previews/miko.png) \| [<NSFW, click to see>](4860/previews/nude.png) \| [<NSFW, click to see>](4860/previews/nude2.png) \| ![suit-4860](4860/previews/suit.png) \| ![yukata-4860](4860/previews/yukata.png) \| \| 4320 \| 0.747 \| [Download](4320/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-4320](4320/previews/pattern_1.png) \| ![pattern_2-4320](4320/previews/pattern_2.png) \| [<NSFW, click to see>](4320/previews/pattern_3.png) \| ![pattern_4-4320](4320/previews/pattern_4.png) \| ![pattern_5-4320](4320/previews/pattern_5.png) \| [<NSFW, click to see>](4320/previews/pattern_6.png) \| ![pattern_7-4320](4320/previews/pattern_7.png) \| ![pattern_8-4320](4320/previews/pattern_8.png) \| ![pattern_9-4320](4320/previews/pattern_9.png) \| ![pattern_10-4320](4320/previews/pattern_10.png) \| ![pattern_11-4320](4320/previews/pattern_11.png) \| ![pattern_12-4320](4320/previews/pattern_12.png) \| ![bikini-4320](4320/previews/bikini.png) \| [<NSFW, click to see>](4320/previews/bondage.png) \| ![free-4320](4320/previews/free.png) \| ![maid-4320](4320/previews/maid.png) \| ![miko-4320](4320/previews/miko.png) \| [<NSFW, click to see>](4320/previews/nude.png) \| [<NSFW, click to see>](4320/previews/nude2.png) \| ![suit-4320](4320/previews/suit.png) \| ![yukata-4320](4320/previews/yukata.png) \| \| 3780 \| 0.761 \| [Download](3780/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-3780](3780/previews/pattern_1.png) \| ![pattern_2-3780](3780/previews/pattern_2.png) \| [<NSFW, click to see>](3780/previews/pattern_3.png) \| ![pattern_4-3780](3780/previews/pattern_4.png) \| ![pattern_5-3780](3780/previews/pattern_5.png) \| [<NSFW, click to see>](3780/previews/pattern_6.png) \| ![pattern_7-3780](3780/previews/pattern_7.png) \| ![pattern_8-3780](3780/previews/pattern_8.png) \| ![pattern_9-3780](3780/previews/pattern_9.png) \| ![pattern_10-3780](3780/previews/pattern_10.png) \| ![pattern_11-3780](3780/previews/pattern_11.png) \| ![pattern_12-3780](3780/previews/pattern_12.png) \| ![bikini-3780](3780/previews/bikini.png) \| [<NSFW, click to see>](3780/previews/bondage.png) \| ![free-3780](3780/previews/free.png) \| ![maid-3780](3780/previews/maid.png) \| ![miko-3780](3780/previews/miko.png) \| [<NSFW, click to see>](3780/previews/nude.png) \| [<NSFW, click to see>](3780/previews/nude2.png) \| ![suit-3780](3780/previews/suit.png) \| ![yukata-3780](3780/previews/yukata.png) \| \| 3240 \| 0.769 \| [Download](3240/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-3240](3240/previews/pattern_1.png) \| ![pattern_2-3240](3240/previews/pattern_2.png) \| [<NSFW, click to see>](3240/previews/pattern_3.png) \| ![pattern_4-3240](3240/previews/pattern_4.png) \| ![pattern_5-3240](3240/previews/pattern_5.png) \| [<NSFW, click to see>](3240/previews/pattern_6.png) \| ![pattern_7-3240](3240/previews/pattern_7.png) \| ![pattern_8-3240](3240/previews/pattern_8.png) \| ![pattern_9-3240](3240/previews/pattern_9.png) \| ![pattern_10-3240](3240/previews/pattern_10.png) \| ![pattern_11-3240](3240/previews/pattern_11.png) \| ![pattern_12-3240](3240/previews/pattern_12.png) \| ![bikini-3240](3240/previews/bikini.png) \| [<NSFW, click to see>](3240/previews/bondage.png) \| ![free-3240](3240/previews/free.png) \| ![maid-3240](3240/previews/maid.png) \| ![miko-3240](3240/previews/miko.png) \| [<NSFW, click to see>](3240/previews/nude.png) \| [<NSFW, click to see>](3240/previews/nude2.png) \| ![suit-3240](3240/previews/suit.png) \| ![yukata-3240](3240/previews/yukata.png) \| \| 2700 \| 0.693 \| [Download](2700/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-2700](2700/previews/pattern_1.png) \| ![pattern_2-2700](2700/previews/pattern_2.png) \| [<NSFW, click to see>](2700/previews/pattern_3.png) \| ![pattern_4-2700](2700/previews/pattern_4.png) \| ![pattern_5-2700](2700/previews/pattern_5.png) \| [<NSFW, click to see>](2700/previews/pattern_6.png) \| ![pattern_7-2700](2700/previews/pattern_7.png) \| ![pattern_8-2700](2700/previews/pattern_8.png) \| ![pattern_9-2700](2700/previews/pattern_9.png) \| ![pattern_10-2700](2700/previews/pattern_10.png) \| ![pattern_11-2700](2700/previews/pattern_11.png) \| ![pattern_12-2700](2700/previews/pattern_12.png) \| ![bikini-2700](2700/previews/bikini.png) \| [<NSFW, click to see>](2700/previews/bondage.png) \| ![free-2700](2700/previews/free.png) \| ![maid-2700](2700/previews/maid.png) \| ![miko-2700](2700/previews/miko.png) \| [<NSFW, click to see>](2700/previews/nude.png) \| [<NSFW, click to see>](2700/previews/nude2.png) \| ![suit-2700](2700/previews/suit.png) \| ![yukata-2700](2700/previews/yukata.png) \| \| 2160 \| 0.810 \| [Download](2160/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-2160](2160/previews/pattern_1.png) \| ![pattern_2-2160](2160/previews/pattern_2.png) \| [<NSFW, click to see>](2160/previews/pattern_3.png) \| ![pattern_4-2160](2160/previews/pattern_4.png) \| ![pattern_5-2160](2160/previews/pattern_5.png) \| [<NSFW, click to see>](2160/previews/pattern_6.png) \| ![pattern_7-2160](2160/previews/pattern_7.png) \| ![pattern_8-2160](2160/previews/pattern_8.png) \| ![pattern_9-2160](2160/previews/pattern_9.png) \| ![pattern_10-2160](2160/previews/pattern_10.png) \| ![pattern_11-2160](2160/previews/pattern_11.png) \| ![pattern_12-2160](2160/previews/pattern_12.png) \| ![bikini-2160](2160/previews/bikini.png) \| [<NSFW, click to see>](2160/previews/bondage.png) \| ![free-2160](2160/previews/free.png) \| ![maid-2160](2160/previews/maid.png) \| ![miko-2160](2160/previews/miko.png) \| [<NSFW, click to see>](2160/previews/nude.png) \| [<NSFW, click to see>](2160/previews/nude2.png) \| ![suit-2160](2160/previews/suit.png) \| ![yukata-2160](2160/previews/yukata.png) \| \| 1620 \| 0.718 \| [Download](1620/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-1620](1620/previews/pattern_1.png) \| ![pattern_2-1620](1620/previews/pattern_2.png) \| [<NSFW, click to see>](1620/previews/pattern_3.png) \| ![pattern_4-1620](1620/previews/pattern_4.png) \| ![pattern_5-1620](1620/previews/pattern_5.png) \| [<NSFW, click to see>](1620/previews/pattern_6.png) \| ![pattern_7-1620](1620/previews/pattern_7.png) \| ![pattern_8-1620](1620/previews/pattern_8.png) \| ![pattern_9-1620](1620/previews/pattern_9.png) \| ![pattern_10-1620](1620/previews/pattern_10.png) \| ![pattern_11-1620](1620/previews/pattern_11.png) \| ![pattern_12-1620](1620/previews/pattern_12.png) \| ![bikini-1620](1620/previews/bikini.png) \| [<NSFW, click to see>](1620/previews/bondage.png) \| ![free-1620](1620/previews/free.png) \| ![maid-1620](1620/previews/maid.png) \| ![miko-1620](1620/previews/miko.png) \| [<NSFW, click to see>](1620/previews/nude.png) \| [<NSFW, click to see>](1620/previews/nude2.png) \| ![suit-1620](1620/previews/suit.png) \| ![yukata-1620](1620/previews/yukata.png) \| \| 1080 \| 0.703 \| [Download](1080/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-1080](1080/previews/pattern_1.png) \| ![pattern_2-1080](1080/previews/pattern_2.png) \| [<NSFW, click to see>](1080/previews/pattern_3.png) \| ![pattern_4-1080](1080/previews/pattern_4.png) \| ![pattern_5-1080](1080/previews/pattern_5.png) \| [<NSFW, click to see>](1080/previews/pattern_6.png) \| ![pattern_7-1080](1080/previews/pattern_7.png) \| ![pattern_8-1080](1080/previews/pattern_8.png) \| ![pattern_9-1080](1080/previews/pattern_9.png) \| ![pattern_10-1080](1080/previews/pattern_10.png) \| ![pattern_11-1080](1080/previews/pattern_11.png) \| ![pattern_12-1080](1080/previews/pattern_12.png) \| ![bikini-1080](1080/previews/bikini.png) \| [<NSFW, click to see>](1080/previews/bondage.png) \| ![free-1080](1080/previews/free.png) \| ![maid-1080](1080/previews/maid.png) \| ![miko-1080](1080/previews/miko.png) \| [<NSFW, click to see>](1080/previews/nude.png) \| [<NSFW, click to see>](1080/previews/nude2.png) \| ![suit-1080](1080/previews/suit.png) \| ![yukata-1080](1080/previews/yukata.png) \| \| 540 \| 0.610 \| [Download](540/mukai_takumi_idolmastercinderellagirls.zip) \| ![pattern_1-540](540/previews/pattern_1.png) \| ![pattern_2-540](540/previews/pattern_2.png) \| [<NSFW, click to see>](540/previews/pattern_3.png) \| ![pattern_4-540](540/previews/pattern_4.png) \| ![pattern_5-540](540/previews/pattern_5.png) \| [<NSFW, click to see>](540/previews/pattern_6.png) \| ![pattern_7-540](540/previews/pattern_7.png) \| ![pattern_8-540](540/previews/pattern_8.png) \| ![pattern_9-540](540/previews/pattern_9.png) \| ![pattern_10-540](540/previews/pattern_10.png) \| ![pattern_11-540](540/previews/pattern_11.png) \| ![pattern_12-540](540/previews/pattern_12.png) \| ![bikini-540](540/previews/bikini.png) \| [<NSFW, click to see>](540/previews/bondage.png) \| ![free-540](540/previews/free.png) \| ![maid-540](540/previews/maid.png) \| ![miko-540](540/previews/miko.png) \| [<NSFW, click to see>](540/previews/nude.png) \| [<NSFW, click to see>](540/previews/nude2.png) \| ![suit-540](540/previews/suit.png) \| ![yukata-540](540/previews/yukata.png) \|
BAAI/Aquila-7B	BAAI	2023-09-18T08:26:37Z	1,824	17	transformers	[ "transformers", "pytorch", "aquila", "custom_code", "license:other", "endpoints_compatible", "region:us" ]	null	2023-06-08T07:25:29Z	--- license: other --- ![Aquila_logo](./log.jpeg) <h4 align="center"> <p> <b>English</b> \| <a href="https://huggingface.co/BAAI/Aquila-7B/blob/main/README_zh.md">简体中文</a> \| <p> </h4> Aquila Language Model is the first open source language model that supports both Chinese and English knowledge, commercial license agreements, and compliance with domestic data regulations. - 🌟 Supports open source commercial licenses. The source code of the Aquila series models is based on the [Apache 2.0 agreement](https://www.apache.org/licenses/LICENSE-2.0), while the model weight is based on the [BAAI Aquila Model License Agreement](https://huggingface.co/BAAI/Aquila-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf). Users can use it for commercial purposes as long as they meet the licensing restrictions. - ✍️ Possesses Chinese and English knowledge. The Aquila series model is trained from scratch on a high-quality corpus of Chinese and English languages, with Chinese corpora accounting for about 40%, ensuring that the model accumulates native Chinese world knowledge during the pre-training phase, rather than translated knowledge. - 👮‍♀️ Complies with domestic data regulations. The Chinese corpora of the Aquila series models come from Intelligence Source's accumulated Chinese datasets over the years, including Chinese internet data from over 10,000 sources (more than 99% of which are domestic sources), as well as high-quality Chinese literature and book data supported by authoritative domestic organizations. We will continue to accumulate high-quality and diverse datasets and incorporate them into the subsequent training of the Aquila base models. - 🎯 Continuous improvements and open sourcing. We will continue to improve training data, optimize training methods, and enhance model performance, cultivate a flourishing "model tree" on a better base model foundation, and continuously update open-source versions. The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels, including the [FlagAI GitHub repository](https://github.com/FlagAI-Open/FlagAI/), [FlagAI's Zhihu account](https://www.zhihu.com/people/95-22-20-18) and [FlagAI's official technical communication group](https://github.com/FlagAI-Open/FlagAI/blob/master/wechat-qrcode.jpg). \| Model \| Model Type \| Description \| Status \| GPUs Used \| \| :----------------- \| :----------------------- \| :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ \| :--------------\| :----------- \| \| Aquila-7B \| Base model, 7 billion parameters \| Aquila Base Model inherits the architectural design advantages of GPT-3 and LLaMA. It replaces a batch of more efficient underlying operator implementations, redesigns the implementation of bilingual tokenizer, upgrades BMTrain parallel training method, and achieves nearly 8 times the training efficiency of Magtron+DeepSpeed ZeRO-2. \| Released \| Nvidia-A100 \| \| Aquila-33B \| Base model, 33 billion parameters \| Same as above \| Coming soon \| Nvidia-A100 \| \| AquilaChat-7B \| SFT model, fine-tuned and RL based on Aquila-7B \| AquilaChat Dialog Model supports fluent text dialogue and multiple language generation tasks, and realizes the call of AquilaChat to other models and tools by defining an expandable special instruction specification, which is easy to extend. For example, calling the open source [AltDiffusion](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion-m18) multimodal language image generation model of Flagship Intelligence achieved smooth image generation capability. Together with Flagship Intelligence's InstructFace multi-step controllable text-picture model, it is easy to achieve multi-step controllable editing of human face images. \| Released \| Nvidia-A100 \| \| AquilaChat-33B \| SFT model, fine-tuned and RL based on Aquila-33B \| Same as above \| Coming soon \| Nvidia-A100 \| \| AquilaCode-7B-NV \| Base model, "text-code" generation model, further pre-trained based on Aquila-7B, trained on Nvidia \| AquilaCode-7B achieves high performance with small data sets and parameters, and is currently the best open source code model that supports both Chinese and English, trained using training code data with compliant open source licenses after high-quality filtering. AquilaCode-7B has been trained on both Nvidia and domestic chips for code models. \| Released on GitHub \| Nvidia-A100 \| \| AquilaCode-7B-TS \| Base model, "text-code" generation model, further pre-trained based on Aquila-7B, trained on Horizon Robotics chips \| Same as above \| Released on GitHub \| Tianshu-BI-V100 \| We will continue to release improved versions of Aquila model as open source. - 2023/08/15 ：release v0.10 - Aquila-7B-01 md5: 4279db72e68df1a0705ecc8d4c7be3db - Aquila-7B-02 md5: 621f8ce4c8deebe1635f5a09aa4b80f2 - AquilaChat-7B-01 md5: 22b22ffaed51388ce23f8e328a9b6a18 - AquilaChat-7B-02 md5: 6e84423fe2837c79c0ced6817c316bd4 Aquila-7B has shown improvements in the FlagEval large model evaluation ("Objective") compared to last version. It achieved improvements of approximately 9.09% on TruthfulQA datasets. For detailed evaluation results, please refer to the website http://flageval.baai.ac.cn. For detailed version change history, see [Change Log](https://huggingface.co/BAAI/Aquila-7B/blob/main/change_log.log). ## Quick Start Aquila-7B ### 1. Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_info = "BAAI/Aquila-7B" tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True) model.eval() model.to("cuda:0") text = "汽车EDR是什么" tokens = tokenizer.encode_plus(text)['input_ids'][:-1] tokens = torch.tensor(tokens)[None,].to("cuda:0") with torch.no_grad(): out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007)[0] out = tokenizer.decode(out.cpu().numpy().tolist()) print(out) ``` ## License Aquila-7B and AquilaChat-33B open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/Aquila-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf)
bardsai/twitter-emotion-pl-base	bardsai	2023-09-18T08:23:29Z	898	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "twitter", "pl", "dataset:datasets/tweet_eval", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-19T10:55:07Z	--- language: pl tags: - text-classification - twitter datasets: - datasets/tweet_eval metrics: - f1 - accuracy - precision - recall widget: - text: "Nigdy przegrana nie sprawiła mi takiej radości. Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl" example_title: "Example 1" - text: "Osoby z Ukrainy zapłacą za życie w centrach pomocy? Sprzeczne prawem UE, niehumanitarne, okrutne." example_title: "Example 2" --- # Twitter emotion PL (base) Twitter emotion PL (base) is a model based on [herbert-base](https://huggingface.co/allegro/herbert-base-cased) for analyzing emotion of Polish twitter posts. It was trained on the translated version of [TweetEval](https://www.researchgate.net/publication/347233661_TweetEval_Unified_Benchmark_and_Comparative_Evaluation_for_Tweet_Classification) by Barbieri et al., 2020 for 10 epochs on single RTX3090 gpu. The model will give you a four labels: joy, optimism, sadness and anger. ## How to use You can use this model directly with a pipeline for text classification: ```python from transformers import pipeline nlp = pipeline("text-classification", model="bardsai/twitter-emotion-pl-base") nlp("Nigdy przegrana nie sprawiła mi takiej radości. Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl") ``` ```bash [{'label': 'joy', 'score': 0.5163766145706177}] ``` ## Performance \| Metric \| Value \| \| --- \| ----------- \| \| f1 macro \| 0.756 \| \| precision macro \| 0.767 \| \| recall macro \| 0.750 \| \| accuracy \| 0.789 \| \| samples per second \| 131.6 \| (The performance was evaluated on RTX 3090 gpu) ## Changelog - 2023-07-19: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/) Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]
octava/audio_classification	octava	2023-09-18T08:23:27Z	161	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-09-11T13:23:00Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: audio_classification results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.09734513274336283 --- <!-- 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. --> # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6471 - Accuracy: 0.0973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 15 \| 2.6423 \| 0.0531 \| \| No log \| 2.0 \| 30 \| 2.6471 \| 0.0973 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
AlexanderBond/distilbert-base-uncased-finetuned-emotion	AlexanderBond	2023-09-18T08:12:36Z	102	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T06:01:24Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion 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: 0.922 - name: F1 type: f1 value: 0.9218912616592688 --- <!-- 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-emotion 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: 0.2165 - Accuracy: 0.922 - F1: 0.9219 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8214 \| 1.0 \| 250 \| 0.3159 \| 0.909 \| 0.9085 \| \| 0.2497 \| 2.0 \| 500 \| 0.2165 \| 0.922 \| 0.9219 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
vdivya/dummy-model	vdivya	2023-09-18T08:09:13Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T07:57:50Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: dummy-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. --> # dummy-model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0608 - Train Accuracy: 0.9804 - Validation Loss: 0.2496 - Validation Accuracy: 0.9140 - Epoch: 2 ## 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', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 25257, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.2262 \| 0.9143 \| 0.2503 \| 0.9094 \| 0 \| \| 0.1133 \| 0.9622 \| 0.2515 \| 0.9083 \| 1 \| \| 0.0608 \| 0.9804 \| 0.2496 \| 0.9140 \| 2 \| ### Framework versions - Transformers 4.33.1 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
kurileo/blip2-opt-2.7b-refines	kurileo	2023-09-18T08:03:37Z	2	0	peft	[ "peft", "region:us" ]	null	2023-09-18T08:02:34Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: bfloat16 ### Framework versions - PEFT 0.5.0
ernestum/sac-seals-Ant-v1	ernestum	2023-09-18T07:54:23Z	3	0	stable-baselines3	[ "stable-baselines3", "seals/Ant-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-15T11:54:00Z	--- library_name: stable-baselines3 tags: - seals/Ant-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Ant-v1 type: seals/Ant-v1 metrics: - type: mean_reward value: 1004.15 +/- 26.60 name: mean_reward verified: false --- # SAC Agent playing seals/Ant-v1 This is a trained model of a SAC agent playing seals/Ant-v1 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 sac --env seals/Ant-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Ant-v1 -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 sac --env seals/Ant-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Ant-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/Ant-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/Ant-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('buffer_size', 1000000), ('gamma', 0.98), ('learning_rate', 0.0018514039303149058), ('learning_starts', 1000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -2.2692589009754176, 'net_arch': [256, 256], 'use_sde': False}), ('tau', 0.05), ('train_freq', 64), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ernestum/sac-seals-HalfCheetah-v1	ernestum	2023-09-18T07:53:35Z	2	0	stable-baselines3	[ "stable-baselines3", "seals/HalfCheetah-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-15T11:53:34Z	--- library_name: stable-baselines3 tags: - seals/HalfCheetah-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/HalfCheetah-v1 type: seals/HalfCheetah-v1 metrics: - type: mean_reward value: 1183.52 +/- 22.65 name: mean_reward verified: false --- # SAC Agent playing seals/HalfCheetah-v1 This is a trained model of a SAC agent playing seals/HalfCheetah-v1 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 sac --env seals/HalfCheetah-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/HalfCheetah-v1 -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 sac --env seals/HalfCheetah-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/HalfCheetah-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/HalfCheetah-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/HalfCheetah-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 100000), ('gamma', 0.95), ('learning_rate', 0.000884624878315995), ('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -0.6932709443503001, 'net_arch': [64, 64], 'use_sde': False}), ('tau', 0.01), ('train_freq', 64), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ernestum/sac-seals-Hopper-v1	ernestum	2023-09-18T07:52:51Z	4	0	stable-baselines3	[ "stable-baselines3", "seals/Hopper-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-15T11:53:03Z	--- library_name: stable-baselines3 tags: - seals/Hopper-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Hopper-v1 type: seals/Hopper-v1 metrics: - type: mean_reward value: 2279.30 +/- 124.09 name: mean_reward verified: false --- # SAC Agent playing seals/Hopper-v1 This is a trained model of a SAC agent playing seals/Hopper-v1 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 sac --env seals/Hopper-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Hopper-v1 -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 sac --env seals/Hopper-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Hopper-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/Hopper-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/Hopper-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 100000), ('gamma', 0.98), ('learning_rate', 0.001709807687567946), ('learning_starts', 1000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -1.6829391077276037, 'net_arch': [256, 256], 'use_sde': False}), ('tau', 0.08), ('train_freq', 32), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
nchen909/codellama-7b-python-sft-v1.1	nchen909	2023-09-18T07:52:00Z	6	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-15T09:27:02Z	Evol-Instruct-Python --- license: cc ---
ernestum/ppo-seals-Walker2d-v1	ernestum	2023-09-18T07:48:56Z	0	0	stable-baselines3	[ "stable-baselines3", "seals/Walker2d-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-15T11:51:52Z	--- library_name: stable-baselines3 tags: - seals/Walker2d-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Walker2d-v1 type: seals/Walker2d-v1 metrics: - type: mean_reward value: 2465.56 +/- 272.31 name: mean_reward verified: false --- # PPO Agent playing seals/Walker2d-v1 This is a trained model of a PPO agent playing seals/Walker2d-v1 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 ppo --env seals/Walker2d-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Walker2d-v1 -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 ppo --env seals/Walker2d-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Walker2d-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Walker2d-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Walker2d-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 8), ('clip_range', 0.4), ('ent_coef', 0.00013057334805552262), ('gae_lambda', 0.92), ('gamma', 0.98), ('learning_rate', 3.791707778339674e-05), ('max_grad_norm', 0.6), ('n_envs', 1), ('n_epochs', 5), ('n_steps', 2048), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.98, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.ReLU'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [256, 256], 'vf': [256, 256]}]}), ('vf_coef', 0.6167177795726859), ('normalize_kwargs', {'norm_obs': {'gamma': 0.98, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ernestum/ppo-seals-Humanoid-v1	ernestum	2023-09-18T07:47:41Z	0	0	stable-baselines3	[ "stable-baselines3", "seals/Humanoid-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-18T07:46:45Z	--- library_name: stable-baselines3 tags: - seals/Humanoid-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Humanoid-v1 type: seals/Humanoid-v1 metrics: - type: mean_reward value: 3224.12 +/- 925.36 name: mean_reward verified: false --- # PPO Agent playing seals/Humanoid-v1 This is a trained model of a PPO agent playing seals/Humanoid-v1 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 ppo --env seals/Humanoid-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Humanoid-v1 -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 ppo --env seals/Humanoid-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Humanoid-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Humanoid-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Humanoid-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 0.2), ('ent_coef', 2.0745206045994986e-05), ('gae_lambda', 0.92), ('gamma', 0.999), ('learning_rate', 2.0309225666232827e-05), ('max_grad_norm', 0.5), ('n_envs', 1), ('n_epochs', 20), ('n_steps', 2048), ('n_timesteps', 10000000.0), ('normalize', {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.ReLU'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [256, 256], 'vf': [256, 256]}]}), ('vf_coef', 0.819262464558427), ('normalize_kwargs', {'norm_obs': {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ernestum/ppo-seals-Swimmer-v1	ernestum	2023-09-18T07:45:33Z	0	0	stable-baselines3	[ "stable-baselines3", "seals/Swimmer-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-15T11:50:49Z	--- library_name: stable-baselines3 tags: - seals/Swimmer-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Swimmer-v1 type: seals/Swimmer-v1 metrics: - type: mean_reward value: 292.84 +/- 3.69 name: mean_reward verified: false --- # PPO Agent playing seals/Swimmer-v1 This is a trained model of a PPO agent playing seals/Swimmer-v1 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 ppo --env seals/Swimmer-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Swimmer-v1 -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 ppo --env seals/Swimmer-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Swimmer-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Swimmer-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Swimmer-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 8), ('clip_range', 0.1), ('ent_coef', 5.167107294612664e-08), ('gae_lambda', 0.95), ('gamma', 0.999), ('learning_rate', 0.0001214437022727675), ('max_grad_norm', 2), ('n_epochs', 20), ('n_steps', 2048), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.6162112311062333), ('normalize_kwargs', {'norm_obs': {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ernestum/ppo-seals-MountainCar-v0	ernestum	2023-09-18T07:43:55Z	0	0	stable-baselines3	[ "stable-baselines3", "seals/MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-15T11:50:03Z	--- library_name: stable-baselines3 tags: - seals/MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/MountainCar-v0 type: seals/MountainCar-v0 metrics: - type: mean_reward value: -97.00 +/- 8.26 name: mean_reward verified: false --- # PPO Agent playing seals/MountainCar-v0 This is a trained model of a PPO agent playing seals/MountainCar-v0 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 ppo --env seals/MountainCar-v0 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-v0 -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 ppo --env seals/MountainCar-v0 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/MountainCar-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/MountainCar-v0 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('clip_range', 0.2), ('ent_coef', 6.4940755116195606e-06), ('gae_lambda', 0.98), ('gamma', 0.99), ('learning_rate', 0.0004476103728105138), ('max_grad_norm', 1), ('n_envs', 16), ('n_epochs', 20), ('n_steps', 256), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.25988158989488963), ('normalize_kwargs', {'norm_obs': {'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
checkiejan/prefix-paraphase-30-20-auto	checkiejan	2023-09-18T07:28:51Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-18T07:28:49Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
lepin2001/catsordogs	lepin2001	2023-09-18T07:23:07Z	0	0	fastai	[ "fastai", "code", "en", "license:apache-2.0", "region:us" ]	null	2023-09-18T07:19:04Z	--- license: apache-2.0 language: - en library_name: fastai tags: - code ---
kming/unispeech-sat-base-plus-sv-finetuned-ami-ten-percent-train	kming	2023-09-18T07:21:10Z	159	0	transformers	[ "transformers", "pytorch", "unispeech-sat", "audio-xvector", "generated_from_trainer", "dataset:edinburghcstr/ami", "base_model:microsoft/unispeech-sat-base-plus-sv", "base_model:finetune:microsoft/unispeech-sat-base-plus-sv", "endpoints_compatible", "region:us" ]	null	2023-09-18T07:11:54Z	--- base_model: microsoft/unispeech-sat-base-plus-sv tags: - generated_from_trainer datasets: - edinburghcstr/ami model-index: - name: unispeech-sat-base-plus-sv-finetuned-ami-ten-percent-train 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. --> # unispeech-sat-base-plus-sv-finetuned-ami-ten-percent-train This model is a fine-tuned version of [microsoft/unispeech-sat-base-plus-sv](https://huggingface.co/microsoft/unispeech-sat-base-plus-sv) on the ami 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
Archfiend/ardic-ai-sd-fdb	Archfiend	2023-09-18T07:17:21Z	17	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-21T20:20:03Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### ardic-ai-sd-fdb Dreambooth model trained by Archfiend Sample pictures of this concept:
marcelsamyn/lora-trained-xl-folder	marcelsamyn	2023-09-18T07:16:10Z	1	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:marcelsamyn/marcelsamyn3", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-18T06:27:31Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: marcelsamyn tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: false datasets: - marcelsamyn/marcelsamyn3 --- # LoRA DreamBooth - marcelsamyn/lora-trained-xl-folder These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on the concept prompt: `marcelsamyn` Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```python import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) # This is where you load your trained weights pipe.load_lora_weights('marcelsamyn/lora-trained-xl-folder') pipe.to("cuda") prompt = "A majestic marcelsamyn jumping from a big stone at night" image = pipe(prompt=prompt, num_inference_steps=50).images[0] ```
warp-ai/wuerstchen-prior-model-base	warp-ai	2023-09-18T07:02:05Z	24	1	diffusers	[ "diffusers", "safetensors", "arxiv:2306.00637", "arxiv:1910.09700", "license:mit", "region:us" ]	null	2023-09-03T19:39:26Z	--- license: mit --- <img src="https://cdn-uploads.huggingface.co/production/uploads/634cb5eefb80cc6bcaf63c3e/i-DYpDHw8Pwiy7QBKZVR5.jpeg" width=1500> ## Würstchen - Overview Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the [paper](https://arxiv.org/abs/2306.00637)). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, allowing also cheaper and faster inference. ## Würstchen - Prior The Prior is what we refer to as "Stage C". It is the text-conditional model, operating in the small latent space that Stage A and Stage B encode images into. During inference, its job is to generate the image latents given text. These image latents are then sent to Stages A & B to decode the latents into pixel space. ### Prior - Model - Base This is the base checkpoint for the Prior (Stage C). This means this is only pretrained and generates mostly standard images. We recommend using the [interpolated model](https://huggingface.co/warp-ai/wuerstchen-prior-model-interpolated), as this is our best checkpoint for the Prior (Stage C) because it was finetuned on a curated dataset. However, we recommend this checkpoint if you want to finetune Würstchen on your own large dataset, as the other checkpoints are already biased towards being more artistic. This checkpoint should provide a fairly standard baseline to finetune from, as long as your dataset is rather large. Note: This checkpoint was also already trained on multi-aspect-ratios, meaning you can generate larger images than just 1024x1024. Sometimes generations up to 2048x2048 even work. Feel free to try it out! Also Note: The base checkpoint usually requires a higher classifier-free-guidance value (`guidance_scale=8.0`) and also a negative caption in order to make good looking images. The [interpolated model](https://huggingface.co/warp-ai/wuerstchen-prior-model-interpolated) and [finetuned model](https://huggingface.co/warp-ai/wuerstchen-prior-model-finetuned) usually don't need a negative caption and work better with a lower classifier-free-guidance value (`guidance_scale=4.0`). ### Image Sizes Würstchen was trained on image resolutions between 1024x1024 & 1536x1536. We sometimes also observe good outputs at resolutions like 1024x2048. Feel free to try it out. We also observed that the Prior (Stage C) adapts extremely fast to new resolutions. So finetuning it at 2048x2048 should be computationally cheap. <img src="https://cdn-uploads.huggingface.co/production/uploads/634cb5eefb80cc6bcaf63c3e/IfVsUDcP15OY-5wyLYKnQ.jpeg" width=1000> ## How to run This pipeline should be run together with https://huggingface.co/warp-ai/wuerstchen: ```py import torch from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline from diffusers.pipelines.wuerstchen import WuerstchenPrior, default_stage_c_timesteps device = "cuda" dtype = torch.float16 num_images_per_prompt = 2 prior = WuerstchenPrior.from_pretrained("warp-ai/wuerstchen-prior-model-base", torch_dtype=dtype).to(device) prior_pipeline = WuerstchenPriorPipeline.from_pretrained( "warp-ai/wuerstchen-prior", prior=prior, torch_dtype=dtype ).to(device) decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained( "warp-ai/wuerstchen", torch_dtype=dtype ).to(device) caption = "Anthropomorphic cat dressed as a fire fighter" negative_prompt = "bad anatomy, blurry, fuzzy, extra arms, extra fingers, poorly drawn hands, disfigured, tiling, deformed, mutated, drawing" prior_output = prior_pipeline( prompt=caption, height=1024, width=1024, timesteps=default_stage_c_timesteps, negative_prompt=negative_prompt, guidance_scale=8.0, num_images_per_prompt=num_images_per_prompt, ) decoder_output = decoder_pipeline( image_embeddings=prior_output.image_embeddings, prompt=caption, negative_prompt=negative_prompt, num_images_per_prompt=num_images_per_prompt, guidance_scale=0.0, output_type="pil", ).images ``` ## Model Details - Developed by: Pablo Pernias, Dominic Rampas - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: MIT - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Diffusion model in the style of Stage C from the [Würstchen paper](https://arxiv.org/abs/2306.00637) that uses a fixed, pretrained text encoder ([CLIP ViT-bigG/14](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)). - Resources for more information: [GitHub Repository](https://github.com/dome272/Wuerstchen), [Paper](https://arxiv.org/abs/2306.00637). - Cite as: @misc{pernias2023wuerstchen, title={Wuerstchen: Efficient Pretraining of Text-to-Image Models}, author={Pablo Pernias and Dominic Rampas and Marc Aubreville}, year={2023}, eprint={2306.00637}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Environmental Impact Würstchen v2 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 24602 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 2275.68 kg CO2 eq.
warp-ai/wuerstchen-prior-model-interpolated	warp-ai	2023-09-18T07:01:48Z	23	3	diffusers	[ "diffusers", "safetensors", "arxiv:2306.00637", "arxiv:1910.09700", "license:mit", "region:us" ]	null	2023-09-03T19:45:43Z	--- license: mit --- <img src="https://cdn-uploads.huggingface.co/production/uploads/634cb5eefb80cc6bcaf63c3e/i-DYpDHw8Pwiy7QBKZVR5.jpeg" width=1500> ## Würstchen - Overview Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the [paper](https://arxiv.org/abs/2306.00637)). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, allowing also cheaper and faster inference. ## Würstchen - Prior The Prior is what we refer to as "Stage C". It is the text-conditional model, operating in the small latent space that Stage A and Stage B encode images into. During inference, its job is to generate the image latents given text. These image latents are then sent to Stages A & B to decode the latents into pixel space. ### Prior - Model - Interpolated The interpolated model is our current best Prior (Stage C) checkpoint. It is an interpolation between our [base model](https://huggingface.co/warp-ai/wuerstchen-prior-model-base) and the [finetuned model](https://huggingface.co/warp-ai/wuerstchen-prior-model-finetuned). We created this interpolation because the finetuned model became too artistic and often only generates artistic images. The base model, however, usually is very photorealistic. As a result, we combined both by interpolating their weights by 50%, so the middle between the base and finetuned model (`0.5 * base_weights + 0.5 * finetuned_weights`). You can also interpolate the [base model](https://huggingface.co/warp-ai/wuerstchen-prior-model-base) and the [finetuned model](https://huggingface.co/warp-ai/wuerstchen-prior-model-finetuned) as you want and maybe find an interpolation that fits your needs better than this checkpoint. ### Image Sizes Würstchen was trained on image resolutions between 1024x1024 & 1536x1536. We sometimes also observe good outputs at resolutions like 1024x2048. Feel free to try it out. We also observed that the Prior (Stage C) adapts extremely fast to new resolutions. So finetuning it at 2048x2048 should be computationally cheap. <img src="https://cdn-uploads.huggingface.co/production/uploads/634cb5eefb80cc6bcaf63c3e/IfVsUDcP15OY-5wyLYKnQ.jpeg" width=1000> ## How to run This pipeline should be run together with https://huggingface.co/warp-ai/wuerstchen: ```py import torch from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS device = "cuda" dtype = torch.float16 num_images_per_prompt = 2 prior_pipeline = WuerstchenPriorPipeline.from_pretrained( "warp-ai/wuerstchen-prior", torch_dtype=dtype ).to(device) decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained( "warp-ai/wuerstchen", torch_dtype=dtype ).to(device) caption = "Anthropomorphic cat dressed as a fire fighter" negative_prompt = "" prior_output = prior_pipeline( prompt=caption, height=1024, width=1536, timesteps=DEFAULT_STAGE_C_TIMESTEPS, negative_prompt=negative_prompt, guidance_scale=4.0, num_images_per_prompt=num_images_per_prompt, ) decoder_output = decoder_pipeline( image_embeddings=prior_output.image_embeddings, prompt=caption, negative_prompt=negative_prompt, guidance_scale=0.0, output_type="pil", ).images ``` ## Model Details - Developed by: Pablo Pernias, Dominic Rampas - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: MIT - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Diffusion model in the style of Stage C from the [Würstchen paper](https://arxiv.org/abs/2306.00637) that uses a fixed, pretrained text encoder ([CLIP ViT-bigG/14](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)). - Resources for more information: [GitHub Repository](https://github.com/dome272/Wuerstchen), [Paper](https://arxiv.org/abs/2306.00637). - Cite as: @misc{pernias2023wuerstchen, title={Wuerstchen: Efficient Pretraining of Text-to-Image Models}, author={Pablo Pernias and Dominic Rampas and Marc Aubreville}, year={2023}, eprint={2306.00637}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Environmental Impact Würstchen v2 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 24602 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 2275.68 kg CO2 eq.
Abhay1212/news_demo	Abhay1212	2023-09-18T06:57:11Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-18T06:52:21Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: float16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0
ailoveydovey/anyqngmxrl	ailoveydovey	2023-09-18T06:54:06Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-18T06:39:38Z	--- license: creativeml-openrail-m ---
etri-xainlp/polyglot-ko-12.8b-instruct	etri-xainlp	2023-09-18T06:40:24Z	2,274	2	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-12T07:48:37Z	--- license: apache-2.0 language: - ko --- # polyglot-ko-12.8b-instruct This model is a fine-tuned version of [EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) on an instruction-following dataset(260k). ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - seed: 42 - distributed_type: multi-GPU(A100 80G) - num_devices: 8 - gradient_accumulation_steps: 64 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Inference ```python import torch from transformers import pipeline, AutoModelForCausalLM MODEL = 'etri-xainlp/polyglot-ko-12.8b-instruct' model = AutoModelForCausalLM.from_pretrained( MODEL, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(device=f"cuda", non_blocking=True) model.eval() pipe = pipeline( 'text-generation', model=model, tokenizer=MODEL, device=0 ) pipe.model.config.pad_token_id = pipe.model.config.eos_token_id def ask(x, context='', is_input_full=False): ans = pipe( f"### 질문: {x}\n\n### 맥락: {context}\n\n### 답변:" if context else f"### 질문: {x}\n\n### 답변:", do_sample=True, max_new_tokens=2048, temperature=0.9, top_p=0.9, return_full_text=False, eos_token_id=2, ) return ans[0]['generated_text'] while True: quit = input('prompt?: ') if quit == 'q': break else: generation = ask(quit) print("etri_ai:", generation) ``` ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ys7yoo/sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep9_ckpt	ys7yoo	2023-09-18T06:40:01Z	107	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "base_model:finetune:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T06:06:14Z	--- base_model: ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3 tags: - generated_from_trainer datasets: - klue model-index: - name: sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep9_ckpt 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. --> # sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep9_ckpt This model is a fine-tuned version of [ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3](https://huggingface.co/ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3250 - Mse: 0.3250 - Mae: 0.4166 - R2: 0.8512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| Mae \| R2 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| 1.2084 \| 1.0 \| 183 \| 0.5071 \| 0.5071 \| 0.5306 \| 0.7678 \| \| 0.1515 \| 2.0 \| 366 \| 0.3142 \| 0.3142 \| 0.4149 \| 0.8561 \| \| 0.103 \| 3.0 \| 549 \| 0.3284 \| 0.3284 \| 0.4150 \| 0.8496 \| \| 0.0779 \| 4.0 \| 732 \| 0.3306 \| 0.3306 \| 0.4184 \| 0.8486 \| \| 0.0597 \| 5.0 \| 915 \| 0.3219 \| 0.3219 \| 0.4098 \| 0.8526 \| \| 0.0497 \| 6.0 \| 1098 \| 0.3324 \| 0.3324 \| 0.4175 \| 0.8478 \| \| 0.0407 \| 7.0 \| 1281 \| 0.3114 \| 0.3114 \| 0.4119 \| 0.8574 \| \| 0.0356 \| 8.0 \| 1464 \| 0.3305 \| 0.3305 \| 0.4199 \| 0.8486 \| \| 0.0327 \| 9.0 \| 1647 \| 0.3250 \| 0.3250 \| 0.4166 \| 0.8512 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3
checkiejan/prefix-paraphase-25-20-auto	checkiejan	2023-09-18T06:35:16Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-18T06:35:14Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
Archolic/SDArchitecture	Archolic	2023-09-18T06:23:40Z	0	0	null	[ "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-18T06:19:56Z	--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true extra_gated_prompt: \|- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ### Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). ## Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - Resources for more information: [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - Cite as: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` after generation of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training Training Data The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) Training Procedure Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - Hardware: 32 x 8 x A100 GPUs - Optimizer: AdamW - Gradient Accumulations: 2 - Batch: 32 x 8 x 2 x 4 = 2048 - Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact Stable Diffusion v1 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 150000 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).
kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new	kming	2023-09-18T06:07:31Z	162	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-xvector", "generated_from_trainer", "dataset:edinburghcstr/ami", "base_model:anton-l/wav2vec2-base-superb-sv", "base_model:finetune:anton-l/wav2vec2-base-superb-sv", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2023-09-15T09:23:26Z	--- license: apache-2.0 base_model: anton-l/wav2vec2-base-superb-sv tags: - generated_from_trainer datasets: - edinburghcstr/ami model-index: - name: wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-normalized 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. --> # wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-normalized This model is a fine-tuned version of [anton-l/wav2vec2-base-superb-sv](https://huggingface.co/anton-l/wav2vec2-base-superb-sv) on the ami 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
TamerAbdelaziz/distilbert-base-uncased-finetuned-sst2	TamerAbdelaziz	2023-09-18T05:56:36Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T05:36:37Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: TamerAbdelaziz/distilbert-base-uncased-finetuned-sst2 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. --> # TamerAbdelaziz/distilbert-base-uncased-finetuned-sst2 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: 0.0592 - Validation Loss: 0.2958 - Train Accuracy: 0.9060 - Epoch: 2 ## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 12627, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.2123 \| 0.2546 \| 0.9014 \| 0 \| \| 0.1023 \| 0.2641 \| 0.8899 \| 1 \| \| 0.0592 \| 0.2958 \| 0.9060 \| 2 \| ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/mizumoto_yukari_idolmastercinderellagirls	CyberHarem	2023-09-18T05:50:54Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/mizumoto_yukari_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-18T05:29:09Z	--- license: mit datasets: - CyberHarem/mizumoto_yukari_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of mizumoto_yukari_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 7280, you need to download `7280/mizumoto_yukari_idolmastercinderellagirls.pt` as the embedding and `7280/mizumoto_yukari_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 7280, with the score of 0.975. The trigger words are: 1. `mizumoto_yukari_idolmastercinderellagirls` 2. `brown_hair, long_hair, brown_eyes, blush, smile, bangs, open_mouth, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| pattern_13 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:----------------------------------------------------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------\|:-----------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 7800 \| 0.971 \| [Download](7800/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-7800](7800/previews/pattern_1.png) \| ![pattern_2-7800](7800/previews/pattern_2.png) \| ![pattern_3-7800](7800/previews/pattern_3.png) \| ![pattern_4-7800](7800/previews/pattern_4.png) \| [<NSFW, click to see>](7800/previews/pattern_5.png) \| [<NSFW, click to see>](7800/previews/pattern_6.png) \| ![pattern_7-7800](7800/previews/pattern_7.png) \| ![pattern_8-7800](7800/previews/pattern_8.png) \| ![pattern_9-7800](7800/previews/pattern_9.png) \| ![pattern_10-7800](7800/previews/pattern_10.png) \| ![pattern_11-7800](7800/previews/pattern_11.png) \| [<NSFW, click to see>](7800/previews/pattern_12.png) \| [<NSFW, click to see>](7800/previews/pattern_13.png) \| ![bikini-7800](7800/previews/bikini.png) \| [<NSFW, click to see>](7800/previews/bondage.png) \| ![free-7800](7800/previews/free.png) \| ![maid-7800](7800/previews/maid.png) \| ![miko-7800](7800/previews/miko.png) \| [<NSFW, click to see>](7800/previews/nude.png) \| [<NSFW, click to see>](7800/previews/nude2.png) \| ![suit-7800](7800/previews/suit.png) \| ![yukata-7800](7800/previews/yukata.png) \| \| 7280 \| 0.975 \| [Download](7280/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-7280](7280/previews/pattern_1.png) \| ![pattern_2-7280](7280/previews/pattern_2.png) \| ![pattern_3-7280](7280/previews/pattern_3.png) \| ![pattern_4-7280](7280/previews/pattern_4.png) \| [<NSFW, click to see>](7280/previews/pattern_5.png) \| [<NSFW, click to see>](7280/previews/pattern_6.png) \| ![pattern_7-7280](7280/previews/pattern_7.png) \| ![pattern_8-7280](7280/previews/pattern_8.png) \| ![pattern_9-7280](7280/previews/pattern_9.png) \| ![pattern_10-7280](7280/previews/pattern_10.png) \| ![pattern_11-7280](7280/previews/pattern_11.png) \| [<NSFW, click to see>](7280/previews/pattern_12.png) \| [<NSFW, click to see>](7280/previews/pattern_13.png) \| ![bikini-7280](7280/previews/bikini.png) \| [<NSFW, click to see>](7280/previews/bondage.png) \| ![free-7280](7280/previews/free.png) \| ![maid-7280](7280/previews/maid.png) \| ![miko-7280](7280/previews/miko.png) \| [<NSFW, click to see>](7280/previews/nude.png) \| [<NSFW, click to see>](7280/previews/nude2.png) \| ![suit-7280](7280/previews/suit.png) \| ![yukata-7280](7280/previews/yukata.png) \| \| 6760 \| 0.965 \| [Download](6760/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-6760](6760/previews/pattern_1.png) \| ![pattern_2-6760](6760/previews/pattern_2.png) \| ![pattern_3-6760](6760/previews/pattern_3.png) \| ![pattern_4-6760](6760/previews/pattern_4.png) \| [<NSFW, click to see>](6760/previews/pattern_5.png) \| [<NSFW, click to see>](6760/previews/pattern_6.png) \| ![pattern_7-6760](6760/previews/pattern_7.png) \| ![pattern_8-6760](6760/previews/pattern_8.png) \| ![pattern_9-6760](6760/previews/pattern_9.png) \| ![pattern_10-6760](6760/previews/pattern_10.png) \| ![pattern_11-6760](6760/previews/pattern_11.png) \| [<NSFW, click to see>](6760/previews/pattern_12.png) \| [<NSFW, click to see>](6760/previews/pattern_13.png) \| ![bikini-6760](6760/previews/bikini.png) \| [<NSFW, click to see>](6760/previews/bondage.png) \| ![free-6760](6760/previews/free.png) \| ![maid-6760](6760/previews/maid.png) \| ![miko-6760](6760/previews/miko.png) \| [<NSFW, click to see>](6760/previews/nude.png) \| [<NSFW, click to see>](6760/previews/nude2.png) \| ![suit-6760](6760/previews/suit.png) \| ![yukata-6760](6760/previews/yukata.png) \| \| 6240 \| 0.964 \| [Download](6240/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-6240](6240/previews/pattern_1.png) \| ![pattern_2-6240](6240/previews/pattern_2.png) \| ![pattern_3-6240](6240/previews/pattern_3.png) \| ![pattern_4-6240](6240/previews/pattern_4.png) \| [<NSFW, click to see>](6240/previews/pattern_5.png) \| [<NSFW, click to see>](6240/previews/pattern_6.png) \| ![pattern_7-6240](6240/previews/pattern_7.png) \| ![pattern_8-6240](6240/previews/pattern_8.png) \| ![pattern_9-6240](6240/previews/pattern_9.png) \| ![pattern_10-6240](6240/previews/pattern_10.png) \| ![pattern_11-6240](6240/previews/pattern_11.png) \| [<NSFW, click to see>](6240/previews/pattern_12.png) \| [<NSFW, click to see>](6240/previews/pattern_13.png) \| ![bikini-6240](6240/previews/bikini.png) \| [<NSFW, click to see>](6240/previews/bondage.png) \| ![free-6240](6240/previews/free.png) \| ![maid-6240](6240/previews/maid.png) \| ![miko-6240](6240/previews/miko.png) \| [<NSFW, click to see>](6240/previews/nude.png) \| [<NSFW, click to see>](6240/previews/nude2.png) \| ![suit-6240](6240/previews/suit.png) \| ![yukata-6240](6240/previews/yukata.png) \| \| 5720 \| 0.975 \| [Download](5720/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-5720](5720/previews/pattern_1.png) \| ![pattern_2-5720](5720/previews/pattern_2.png) \| ![pattern_3-5720](5720/previews/pattern_3.png) \| ![pattern_4-5720](5720/previews/pattern_4.png) \| [<NSFW, click to see>](5720/previews/pattern_5.png) \| [<NSFW, click to see>](5720/previews/pattern_6.png) \| ![pattern_7-5720](5720/previews/pattern_7.png) \| ![pattern_8-5720](5720/previews/pattern_8.png) \| ![pattern_9-5720](5720/previews/pattern_9.png) \| ![pattern_10-5720](5720/previews/pattern_10.png) \| ![pattern_11-5720](5720/previews/pattern_11.png) \| [<NSFW, click to see>](5720/previews/pattern_12.png) \| [<NSFW, click to see>](5720/previews/pattern_13.png) \| ![bikini-5720](5720/previews/bikini.png) \| [<NSFW, click to see>](5720/previews/bondage.png) \| ![free-5720](5720/previews/free.png) \| ![maid-5720](5720/previews/maid.png) \| ![miko-5720](5720/previews/miko.png) \| [<NSFW, click to see>](5720/previews/nude.png) \| [<NSFW, click to see>](5720/previews/nude2.png) \| ![suit-5720](5720/previews/suit.png) \| ![yukata-5720](5720/previews/yukata.png) \| \| 5200 \| 0.972 \| [Download](5200/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-5200](5200/previews/pattern_1.png) \| ![pattern_2-5200](5200/previews/pattern_2.png) \| ![pattern_3-5200](5200/previews/pattern_3.png) \| ![pattern_4-5200](5200/previews/pattern_4.png) \| [<NSFW, click to see>](5200/previews/pattern_5.png) \| [<NSFW, click to see>](5200/previews/pattern_6.png) \| ![pattern_7-5200](5200/previews/pattern_7.png) \| ![pattern_8-5200](5200/previews/pattern_8.png) \| ![pattern_9-5200](5200/previews/pattern_9.png) \| ![pattern_10-5200](5200/previews/pattern_10.png) \| ![pattern_11-5200](5200/previews/pattern_11.png) \| [<NSFW, click to see>](5200/previews/pattern_12.png) \| [<NSFW, click to see>](5200/previews/pattern_13.png) \| ![bikini-5200](5200/previews/bikini.png) \| [<NSFW, click to see>](5200/previews/bondage.png) \| ![free-5200](5200/previews/free.png) \| ![maid-5200](5200/previews/maid.png) \| ![miko-5200](5200/previews/miko.png) \| [<NSFW, click to see>](5200/previews/nude.png) \| [<NSFW, click to see>](5200/previews/nude2.png) \| ![suit-5200](5200/previews/suit.png) \| ![yukata-5200](5200/previews/yukata.png) \| \| 4680 \| 0.968 \| [Download](4680/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-4680](4680/previews/pattern_1.png) \| ![pattern_2-4680](4680/previews/pattern_2.png) \| ![pattern_3-4680](4680/previews/pattern_3.png) \| ![pattern_4-4680](4680/previews/pattern_4.png) \| [<NSFW, click to see>](4680/previews/pattern_5.png) \| [<NSFW, click to see>](4680/previews/pattern_6.png) \| ![pattern_7-4680](4680/previews/pattern_7.png) \| ![pattern_8-4680](4680/previews/pattern_8.png) \| ![pattern_9-4680](4680/previews/pattern_9.png) \| ![pattern_10-4680](4680/previews/pattern_10.png) \| ![pattern_11-4680](4680/previews/pattern_11.png) \| [<NSFW, click to see>](4680/previews/pattern_12.png) \| [<NSFW, click to see>](4680/previews/pattern_13.png) \| ![bikini-4680](4680/previews/bikini.png) \| [<NSFW, click to see>](4680/previews/bondage.png) \| ![free-4680](4680/previews/free.png) \| ![maid-4680](4680/previews/maid.png) \| ![miko-4680](4680/previews/miko.png) \| [<NSFW, click to see>](4680/previews/nude.png) \| [<NSFW, click to see>](4680/previews/nude2.png) \| ![suit-4680](4680/previews/suit.png) \| ![yukata-4680](4680/previews/yukata.png) \| \| 4160 \| 0.966 \| [Download](4160/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-4160](4160/previews/pattern_1.png) \| ![pattern_2-4160](4160/previews/pattern_2.png) \| ![pattern_3-4160](4160/previews/pattern_3.png) \| ![pattern_4-4160](4160/previews/pattern_4.png) \| [<NSFW, click to see>](4160/previews/pattern_5.png) \| [<NSFW, click to see>](4160/previews/pattern_6.png) \| ![pattern_7-4160](4160/previews/pattern_7.png) \| ![pattern_8-4160](4160/previews/pattern_8.png) \| ![pattern_9-4160](4160/previews/pattern_9.png) \| ![pattern_10-4160](4160/previews/pattern_10.png) \| ![pattern_11-4160](4160/previews/pattern_11.png) \| [<NSFW, click to see>](4160/previews/pattern_12.png) \| [<NSFW, click to see>](4160/previews/pattern_13.png) \| ![bikini-4160](4160/previews/bikini.png) \| [<NSFW, click to see>](4160/previews/bondage.png) \| ![free-4160](4160/previews/free.png) \| ![maid-4160](4160/previews/maid.png) \| ![miko-4160](4160/previews/miko.png) \| [<NSFW, click to see>](4160/previews/nude.png) \| [<NSFW, click to see>](4160/previews/nude2.png) \| ![suit-4160](4160/previews/suit.png) \| ![yukata-4160](4160/previews/yukata.png) \| \| 3640 \| 0.969 \| [Download](3640/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-3640](3640/previews/pattern_1.png) \| ![pattern_2-3640](3640/previews/pattern_2.png) \| ![pattern_3-3640](3640/previews/pattern_3.png) \| ![pattern_4-3640](3640/previews/pattern_4.png) \| [<NSFW, click to see>](3640/previews/pattern_5.png) \| [<NSFW, click to see>](3640/previews/pattern_6.png) \| ![pattern_7-3640](3640/previews/pattern_7.png) \| ![pattern_8-3640](3640/previews/pattern_8.png) \| ![pattern_9-3640](3640/previews/pattern_9.png) \| ![pattern_10-3640](3640/previews/pattern_10.png) \| ![pattern_11-3640](3640/previews/pattern_11.png) \| [<NSFW, click to see>](3640/previews/pattern_12.png) \| [<NSFW, click to see>](3640/previews/pattern_13.png) \| ![bikini-3640](3640/previews/bikini.png) \| [<NSFW, click to see>](3640/previews/bondage.png) \| ![free-3640](3640/previews/free.png) \| ![maid-3640](3640/previews/maid.png) \| ![miko-3640](3640/previews/miko.png) \| [<NSFW, click to see>](3640/previews/nude.png) \| [<NSFW, click to see>](3640/previews/nude2.png) \| ![suit-3640](3640/previews/suit.png) \| ![yukata-3640](3640/previews/yukata.png) \| \| 3120 \| 0.967 \| [Download](3120/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-3120](3120/previews/pattern_1.png) \| ![pattern_2-3120](3120/previews/pattern_2.png) \| ![pattern_3-3120](3120/previews/pattern_3.png) \| ![pattern_4-3120](3120/previews/pattern_4.png) \| [<NSFW, click to see>](3120/previews/pattern_5.png) \| [<NSFW, click to see>](3120/previews/pattern_6.png) \| ![pattern_7-3120](3120/previews/pattern_7.png) \| ![pattern_8-3120](3120/previews/pattern_8.png) \| ![pattern_9-3120](3120/previews/pattern_9.png) \| ![pattern_10-3120](3120/previews/pattern_10.png) \| ![pattern_11-3120](3120/previews/pattern_11.png) \| [<NSFW, click to see>](3120/previews/pattern_12.png) \| [<NSFW, click to see>](3120/previews/pattern_13.png) \| ![bikini-3120](3120/previews/bikini.png) \| [<NSFW, click to see>](3120/previews/bondage.png) \| ![free-3120](3120/previews/free.png) \| ![maid-3120](3120/previews/maid.png) \| ![miko-3120](3120/previews/miko.png) \| [<NSFW, click to see>](3120/previews/nude.png) \| [<NSFW, click to see>](3120/previews/nude2.png) \| ![suit-3120](3120/previews/suit.png) \| ![yukata-3120](3120/previews/yukata.png) \| \| 2600 \| 0.967 \| [Download](2600/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-2600](2600/previews/pattern_1.png) \| ![pattern_2-2600](2600/previews/pattern_2.png) \| ![pattern_3-2600](2600/previews/pattern_3.png) \| ![pattern_4-2600](2600/previews/pattern_4.png) \| [<NSFW, click to see>](2600/previews/pattern_5.png) \| [<NSFW, click to see>](2600/previews/pattern_6.png) \| ![pattern_7-2600](2600/previews/pattern_7.png) \| ![pattern_8-2600](2600/previews/pattern_8.png) \| ![pattern_9-2600](2600/previews/pattern_9.png) \| ![pattern_10-2600](2600/previews/pattern_10.png) \| ![pattern_11-2600](2600/previews/pattern_11.png) \| [<NSFW, click to see>](2600/previews/pattern_12.png) \| [<NSFW, click to see>](2600/previews/pattern_13.png) \| ![bikini-2600](2600/previews/bikini.png) \| [<NSFW, click to see>](2600/previews/bondage.png) \| ![free-2600](2600/previews/free.png) \| ![maid-2600](2600/previews/maid.png) \| ![miko-2600](2600/previews/miko.png) \| [<NSFW, click to see>](2600/previews/nude.png) \| [<NSFW, click to see>](2600/previews/nude2.png) \| ![suit-2600](2600/previews/suit.png) \| ![yukata-2600](2600/previews/yukata.png) \| \| 2080 \| 0.960 \| [Download](2080/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-2080](2080/previews/pattern_1.png) \| ![pattern_2-2080](2080/previews/pattern_2.png) \| ![pattern_3-2080](2080/previews/pattern_3.png) \| ![pattern_4-2080](2080/previews/pattern_4.png) \| [<NSFW, click to see>](2080/previews/pattern_5.png) \| [<NSFW, click to see>](2080/previews/pattern_6.png) \| ![pattern_7-2080](2080/previews/pattern_7.png) \| ![pattern_8-2080](2080/previews/pattern_8.png) \| ![pattern_9-2080](2080/previews/pattern_9.png) \| ![pattern_10-2080](2080/previews/pattern_10.png) \| ![pattern_11-2080](2080/previews/pattern_11.png) \| [<NSFW, click to see>](2080/previews/pattern_12.png) \| [<NSFW, click to see>](2080/previews/pattern_13.png) \| ![bikini-2080](2080/previews/bikini.png) \| [<NSFW, click to see>](2080/previews/bondage.png) \| ![free-2080](2080/previews/free.png) \| ![maid-2080](2080/previews/maid.png) \| ![miko-2080](2080/previews/miko.png) \| [<NSFW, click to see>](2080/previews/nude.png) \| [<NSFW, click to see>](2080/previews/nude2.png) \| ![suit-2080](2080/previews/suit.png) \| ![yukata-2080](2080/previews/yukata.png) \| \| 1560 \| 0.961 \| [Download](1560/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-1560](1560/previews/pattern_1.png) \| ![pattern_2-1560](1560/previews/pattern_2.png) \| ![pattern_3-1560](1560/previews/pattern_3.png) \| ![pattern_4-1560](1560/previews/pattern_4.png) \| [<NSFW, click to see>](1560/previews/pattern_5.png) \| [<NSFW, click to see>](1560/previews/pattern_6.png) \| ![pattern_7-1560](1560/previews/pattern_7.png) \| ![pattern_8-1560](1560/previews/pattern_8.png) \| ![pattern_9-1560](1560/previews/pattern_9.png) \| ![pattern_10-1560](1560/previews/pattern_10.png) \| ![pattern_11-1560](1560/previews/pattern_11.png) \| [<NSFW, click to see>](1560/previews/pattern_12.png) \| [<NSFW, click to see>](1560/previews/pattern_13.png) \| ![bikini-1560](1560/previews/bikini.png) \| [<NSFW, click to see>](1560/previews/bondage.png) \| ![free-1560](1560/previews/free.png) \| ![maid-1560](1560/previews/maid.png) \| ![miko-1560](1560/previews/miko.png) \| [<NSFW, click to see>](1560/previews/nude.png) \| [<NSFW, click to see>](1560/previews/nude2.png) \| ![suit-1560](1560/previews/suit.png) \| ![yukata-1560](1560/previews/yukata.png) \| \| 1040 \| 0.960 \| [Download](1040/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-1040](1040/previews/pattern_1.png) \| ![pattern_2-1040](1040/previews/pattern_2.png) \| ![pattern_3-1040](1040/previews/pattern_3.png) \| ![pattern_4-1040](1040/previews/pattern_4.png) \| [<NSFW, click to see>](1040/previews/pattern_5.png) \| [<NSFW, click to see>](1040/previews/pattern_6.png) \| ![pattern_7-1040](1040/previews/pattern_7.png) \| ![pattern_8-1040](1040/previews/pattern_8.png) \| ![pattern_9-1040](1040/previews/pattern_9.png) \| ![pattern_10-1040](1040/previews/pattern_10.png) \| ![pattern_11-1040](1040/previews/pattern_11.png) \| [<NSFW, click to see>](1040/previews/pattern_12.png) \| [<NSFW, click to see>](1040/previews/pattern_13.png) \| ![bikini-1040](1040/previews/bikini.png) \| [<NSFW, click to see>](1040/previews/bondage.png) \| ![free-1040](1040/previews/free.png) \| ![maid-1040](1040/previews/maid.png) \| ![miko-1040](1040/previews/miko.png) \| [<NSFW, click to see>](1040/previews/nude.png) \| [<NSFW, click to see>](1040/previews/nude2.png) \| ![suit-1040](1040/previews/suit.png) \| ![yukata-1040](1040/previews/yukata.png) \| \| 520 \| 0.958 \| [Download](520/mizumoto_yukari_idolmastercinderellagirls.zip) \| ![pattern_1-520](520/previews/pattern_1.png) \| ![pattern_2-520](520/previews/pattern_2.png) \| ![pattern_3-520](520/previews/pattern_3.png) \| ![pattern_4-520](520/previews/pattern_4.png) \| [<NSFW, click to see>](520/previews/pattern_5.png) \| [<NSFW, click to see>](520/previews/pattern_6.png) \| ![pattern_7-520](520/previews/pattern_7.png) \| ![pattern_8-520](520/previews/pattern_8.png) \| ![pattern_9-520](520/previews/pattern_9.png) \| ![pattern_10-520](520/previews/pattern_10.png) \| ![pattern_11-520](520/previews/pattern_11.png) \| [<NSFW, click to see>](520/previews/pattern_12.png) \| [<NSFW, click to see>](520/previews/pattern_13.png) \| ![bikini-520](520/previews/bikini.png) \| [<NSFW, click to see>](520/previews/bondage.png) \| ![free-520](520/previews/free.png) \| ![maid-520](520/previews/maid.png) \| ![miko-520](520/previews/miko.png) \| [<NSFW, click to see>](520/previews/nude.png) \| [<NSFW, click to see>](520/previews/nude2.png) \| ![suit-520](520/previews/suit.png) \| ![yukata-520](520/previews/yukata.png) \|
xtrbase/positive-llm	xtrbase	2023-09-18T05:39:21Z	3	0	peft	[ "peft", "region:us" ]	null	2023-09-18T05:38:57Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
GlennQuagmire/ER-MIX	GlennQuagmire	2023-09-18T05:26:46Z	0	0	null	[ "license:other", "region:us" ]	null	2023-08-12T02:24:36Z	--- license: other --- ## I own nothing of this model, this is solely for caching purposes Pay a visit to [author](https://space.bilibili.com/49512651) and leave your endorsement! # GIGGITY
GlennQuagmire/DisillusionMix3	GlennQuagmire	2023-09-18T05:21:26Z	0	3	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T16:18:36Z	--- license: creativeml-openrail-m --- # I own nothing of this model, All credit goes to original author,be sure to endorse his/her models on CivitAI! [Click Me](https://civitai.com/user/Rerorerorero/models) --- I start this repo to cache this model giggity
Drello/Test	Drello	2023-09-18T05:20:03Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-09-18T05:20:03Z	--- license: bigscience-openrail-m ---
IXLFreaKz/GawrGura	IXLFreaKz	2023-09-18T05:14:24Z	0	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2023-09-18T05:12:28Z	--- license: cc-by-nc-nd-4.0 ---
abhiShek1061/imdb-classification	abhiShek1061	2023-09-18T05:14:00Z	103	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T04:42:42Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: imdb-classification results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93228 --- <!-- 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. --> # imdb-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2332 - Accuracy: 0.9323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2233 \| 1.0 \| 1563 \| 0.2479 \| 0.9146 \| \| 0.149 \| 2.0 \| 3126 \| 0.2332 \| 0.9323 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
Panchovix/Synthia-70B-v1.2b-safetensors	Panchovix	2023-09-18T05:13:13Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-18T03:09:27Z	--- license: llama2 --- Safetensors conversion of Synthia-70B-v1.2b (https://huggingface.co/migtissera/Synthia-70B-v1.2b). Can be used directly on transformers, or to be used to convert/quant models with exllamav2.
ailabturkiye/sempatuco	ailabturkiye	2023-09-18T05:04:01Z	0	0	null	[ "tr", "license:openrail", "region:us" ]	null	2023-08-09T13:54:30Z	--- license: openrail language: - tr ---
ShivamMangale/XLM-Roberta-base-finetuned-squad-squad-first	ShivamMangale	2023-09-18T04:51:49Z	12	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-18T01:31:54Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-finetuned-squad-squad-first 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-squad-squad-first This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
furquan/opt_2_7_b_prompt_tuned_sentiment_analysis	furquan	2023-09-18T04:51:43Z	14	0	transformers	[ "transformers", "pytorch", "opt", "feature-extraction", "text-generation", "custom_code", "dataset:SetFit/sst5", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-18T03:45:11Z	--- datasets: - SetFit/sst5 pipeline_tag: text-generation widget: - text: 'The weather is lovely today! ' ---
pkduongsu/bert-finetuned-covidqadeepset	pkduongsu	2023-09-18T04:42:30Z	116	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-09-18T04:22:57Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - covid_qa_deepset model-index: - name: bert-finetuned-covidqa 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-covidqa This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the covid_qa_deepset 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 - 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
alayaran/bodo-pos-gpt2-fine-tune	alayaran	2023-09-18T04:37:43Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "token-classification", "br", "dataset:alayaran/bodo-pos-conll", "dataset:alayaran/bodo-monolingual-dataset", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	token-classification	2023-09-18T03:53:21Z	--- license: mit datasets: - alayaran/bodo-pos-conll - alayaran/bodo-monolingual-dataset language: - br metrics: - accuracy - seqeval widget: - text: "बर’फोरा मिथिंगा सिबियारि ।" example_title: "Example 1" - text: "गथ’फोर त्रेफिकिं खालामनायाबो भारताव मोनसे गोब्राब जेंना जागासिनो ।" example_title: "Example 2" - text: "गोबां बिबांफोरनि सोरकारनि फोरमानजों मदद होजानानै , बिमायारि आरो गथ’ देहाया देहानि मिनिसत्रिफोराव गोनांसिन जाबाय ।" example_title: "Example 3" ---
CyberHarem/yorita_yoshino_idolmastercinderellagirls	CyberHarem	2023-09-18T04:34:06Z	0	2	null	[ "art", "text-to-image", "dataset:CyberHarem/yorita_yoshino_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-18T04:11:00Z	--- license: mit datasets: - CyberHarem/yorita_yoshino_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of yorita_yoshino_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 7840, you need to download `7840/yorita_yoshino_idolmastercinderellagirls.pt` as the embedding and `7840/yorita_yoshino_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 7840, with the score of 0.849. The trigger words are: 1. `yorita_yoshino_idolmastercinderellagirls` 2. `brown_eyes, brown_hair, long_hair, bangs, blush, bow, hair_bow, smile, very_long_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| pattern_13 \| pattern_14 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:----------------------------------------------------\|:----------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 8400 \| 0.765 \| [Download](8400/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-8400](8400/previews/pattern_1.png) \| ![pattern_2-8400](8400/previews/pattern_2.png) \| ![pattern_3-8400](8400/previews/pattern_3.png) \| ![pattern_4-8400](8400/previews/pattern_4.png) \| ![pattern_5-8400](8400/previews/pattern_5.png) \| ![pattern_6-8400](8400/previews/pattern_6.png) \| ![pattern_7-8400](8400/previews/pattern_7.png) \| [<NSFW, click to see>](8400/previews/pattern_8.png) \| [<NSFW, click to see>](8400/previews/pattern_9.png) \| ![pattern_10-8400](8400/previews/pattern_10.png) \| ![pattern_11-8400](8400/previews/pattern_11.png) \| ![pattern_12-8400](8400/previews/pattern_12.png) \| ![pattern_13-8400](8400/previews/pattern_13.png) \| ![pattern_14-8400](8400/previews/pattern_14.png) \| [<NSFW, click to see>](8400/previews/bikini.png) \| [<NSFW, click to see>](8400/previews/bondage.png) \| ![free-8400](8400/previews/free.png) \| ![maid-8400](8400/previews/maid.png) \| ![miko-8400](8400/previews/miko.png) \| [<NSFW, click to see>](8400/previews/nude.png) \| [<NSFW, click to see>](8400/previews/nude2.png) \| ![suit-8400](8400/previews/suit.png) \| ![yukata-8400](8400/previews/yukata.png) \| \| 7840 \| 0.849 \| [Download](7840/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-7840](7840/previews/pattern_1.png) \| ![pattern_2-7840](7840/previews/pattern_2.png) \| ![pattern_3-7840](7840/previews/pattern_3.png) \| ![pattern_4-7840](7840/previews/pattern_4.png) \| ![pattern_5-7840](7840/previews/pattern_5.png) \| ![pattern_6-7840](7840/previews/pattern_6.png) \| ![pattern_7-7840](7840/previews/pattern_7.png) \| [<NSFW, click to see>](7840/previews/pattern_8.png) \| [<NSFW, click to see>](7840/previews/pattern_9.png) \| ![pattern_10-7840](7840/previews/pattern_10.png) \| ![pattern_11-7840](7840/previews/pattern_11.png) \| ![pattern_12-7840](7840/previews/pattern_12.png) \| ![pattern_13-7840](7840/previews/pattern_13.png) \| ![pattern_14-7840](7840/previews/pattern_14.png) \| [<NSFW, click to see>](7840/previews/bikini.png) \| [<NSFW, click to see>](7840/previews/bondage.png) \| ![free-7840](7840/previews/free.png) \| ![maid-7840](7840/previews/maid.png) \| ![miko-7840](7840/previews/miko.png) \| [<NSFW, click to see>](7840/previews/nude.png) \| [<NSFW, click to see>](7840/previews/nude2.png) \| ![suit-7840](7840/previews/suit.png) \| ![yukata-7840](7840/previews/yukata.png) \| \| 7280 \| 0.824 \| [Download](7280/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-7280](7280/previews/pattern_1.png) \| ![pattern_2-7280](7280/previews/pattern_2.png) \| ![pattern_3-7280](7280/previews/pattern_3.png) \| ![pattern_4-7280](7280/previews/pattern_4.png) \| ![pattern_5-7280](7280/previews/pattern_5.png) \| ![pattern_6-7280](7280/previews/pattern_6.png) \| ![pattern_7-7280](7280/previews/pattern_7.png) \| [<NSFW, click to see>](7280/previews/pattern_8.png) \| [<NSFW, click to see>](7280/previews/pattern_9.png) \| ![pattern_10-7280](7280/previews/pattern_10.png) \| ![pattern_11-7280](7280/previews/pattern_11.png) \| ![pattern_12-7280](7280/previews/pattern_12.png) \| ![pattern_13-7280](7280/previews/pattern_13.png) \| ![pattern_14-7280](7280/previews/pattern_14.png) \| [<NSFW, click to see>](7280/previews/bikini.png) \| [<NSFW, click to see>](7280/previews/bondage.png) \| ![free-7280](7280/previews/free.png) \| ![maid-7280](7280/previews/maid.png) \| ![miko-7280](7280/previews/miko.png) \| [<NSFW, click to see>](7280/previews/nude.png) \| [<NSFW, click to see>](7280/previews/nude2.png) \| ![suit-7280](7280/previews/suit.png) \| ![yukata-7280](7280/previews/yukata.png) \| \| 6720 \| 0.779 \| [Download](6720/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-6720](6720/previews/pattern_1.png) \| ![pattern_2-6720](6720/previews/pattern_2.png) \| ![pattern_3-6720](6720/previews/pattern_3.png) \| ![pattern_4-6720](6720/previews/pattern_4.png) \| ![pattern_5-6720](6720/previews/pattern_5.png) \| ![pattern_6-6720](6720/previews/pattern_6.png) \| ![pattern_7-6720](6720/previews/pattern_7.png) \| [<NSFW, click to see>](6720/previews/pattern_8.png) \| [<NSFW, click to see>](6720/previews/pattern_9.png) \| ![pattern_10-6720](6720/previews/pattern_10.png) \| ![pattern_11-6720](6720/previews/pattern_11.png) \| ![pattern_12-6720](6720/previews/pattern_12.png) \| ![pattern_13-6720](6720/previews/pattern_13.png) \| ![pattern_14-6720](6720/previews/pattern_14.png) \| [<NSFW, click to see>](6720/previews/bikini.png) \| [<NSFW, click to see>](6720/previews/bondage.png) \| ![free-6720](6720/previews/free.png) \| ![maid-6720](6720/previews/maid.png) \| ![miko-6720](6720/previews/miko.png) \| [<NSFW, click to see>](6720/previews/nude.png) \| [<NSFW, click to see>](6720/previews/nude2.png) \| ![suit-6720](6720/previews/suit.png) \| ![yukata-6720](6720/previews/yukata.png) \| \| 6160 \| 0.773 \| [Download](6160/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-6160](6160/previews/pattern_1.png) \| ![pattern_2-6160](6160/previews/pattern_2.png) \| ![pattern_3-6160](6160/previews/pattern_3.png) \| ![pattern_4-6160](6160/previews/pattern_4.png) \| ![pattern_5-6160](6160/previews/pattern_5.png) \| ![pattern_6-6160](6160/previews/pattern_6.png) \| ![pattern_7-6160](6160/previews/pattern_7.png) \| [<NSFW, click to see>](6160/previews/pattern_8.png) \| [<NSFW, click to see>](6160/previews/pattern_9.png) \| ![pattern_10-6160](6160/previews/pattern_10.png) \| ![pattern_11-6160](6160/previews/pattern_11.png) \| ![pattern_12-6160](6160/previews/pattern_12.png) \| ![pattern_13-6160](6160/previews/pattern_13.png) \| ![pattern_14-6160](6160/previews/pattern_14.png) \| [<NSFW, click to see>](6160/previews/bikini.png) \| [<NSFW, click to see>](6160/previews/bondage.png) \| ![free-6160](6160/previews/free.png) \| ![maid-6160](6160/previews/maid.png) \| ![miko-6160](6160/previews/miko.png) \| [<NSFW, click to see>](6160/previews/nude.png) \| [<NSFW, click to see>](6160/previews/nude2.png) \| ![suit-6160](6160/previews/suit.png) \| ![yukata-6160](6160/previews/yukata.png) \| \| 5600 \| 0.808 \| [Download](5600/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-5600](5600/previews/pattern_1.png) \| ![pattern_2-5600](5600/previews/pattern_2.png) \| ![pattern_3-5600](5600/previews/pattern_3.png) \| ![pattern_4-5600](5600/previews/pattern_4.png) \| ![pattern_5-5600](5600/previews/pattern_5.png) \| ![pattern_6-5600](5600/previews/pattern_6.png) \| ![pattern_7-5600](5600/previews/pattern_7.png) \| [<NSFW, click to see>](5600/previews/pattern_8.png) \| [<NSFW, click to see>](5600/previews/pattern_9.png) \| ![pattern_10-5600](5600/previews/pattern_10.png) \| ![pattern_11-5600](5600/previews/pattern_11.png) \| ![pattern_12-5600](5600/previews/pattern_12.png) \| ![pattern_13-5600](5600/previews/pattern_13.png) \| ![pattern_14-5600](5600/previews/pattern_14.png) \| [<NSFW, click to see>](5600/previews/bikini.png) \| [<NSFW, click to see>](5600/previews/bondage.png) \| ![free-5600](5600/previews/free.png) \| ![maid-5600](5600/previews/maid.png) \| ![miko-5600](5600/previews/miko.png) \| [<NSFW, click to see>](5600/previews/nude.png) \| [<NSFW, click to see>](5600/previews/nude2.png) \| ![suit-5600](5600/previews/suit.png) \| ![yukata-5600](5600/previews/yukata.png) \| \| 5040 \| 0.822 \| [Download](5040/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-5040](5040/previews/pattern_1.png) \| ![pattern_2-5040](5040/previews/pattern_2.png) \| ![pattern_3-5040](5040/previews/pattern_3.png) \| ![pattern_4-5040](5040/previews/pattern_4.png) \| ![pattern_5-5040](5040/previews/pattern_5.png) \| ![pattern_6-5040](5040/previews/pattern_6.png) \| ![pattern_7-5040](5040/previews/pattern_7.png) \| [<NSFW, click to see>](5040/previews/pattern_8.png) \| [<NSFW, click to see>](5040/previews/pattern_9.png) \| ![pattern_10-5040](5040/previews/pattern_10.png) \| ![pattern_11-5040](5040/previews/pattern_11.png) \| ![pattern_12-5040](5040/previews/pattern_12.png) \| ![pattern_13-5040](5040/previews/pattern_13.png) \| ![pattern_14-5040](5040/previews/pattern_14.png) \| [<NSFW, click to see>](5040/previews/bikini.png) \| [<NSFW, click to see>](5040/previews/bondage.png) \| ![free-5040](5040/previews/free.png) \| ![maid-5040](5040/previews/maid.png) \| ![miko-5040](5040/previews/miko.png) \| [<NSFW, click to see>](5040/previews/nude.png) \| [<NSFW, click to see>](5040/previews/nude2.png) \| ![suit-5040](5040/previews/suit.png) \| ![yukata-5040](5040/previews/yukata.png) \| \| 4480 \| 0.750 \| [Download](4480/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-4480](4480/previews/pattern_1.png) \| ![pattern_2-4480](4480/previews/pattern_2.png) \| ![pattern_3-4480](4480/previews/pattern_3.png) \| ![pattern_4-4480](4480/previews/pattern_4.png) \| ![pattern_5-4480](4480/previews/pattern_5.png) \| ![pattern_6-4480](4480/previews/pattern_6.png) \| ![pattern_7-4480](4480/previews/pattern_7.png) \| [<NSFW, click to see>](4480/previews/pattern_8.png) \| [<NSFW, click to see>](4480/previews/pattern_9.png) \| ![pattern_10-4480](4480/previews/pattern_10.png) \| ![pattern_11-4480](4480/previews/pattern_11.png) \| ![pattern_12-4480](4480/previews/pattern_12.png) \| ![pattern_13-4480](4480/previews/pattern_13.png) \| ![pattern_14-4480](4480/previews/pattern_14.png) \| [<NSFW, click to see>](4480/previews/bikini.png) \| [<NSFW, click to see>](4480/previews/bondage.png) \| ![free-4480](4480/previews/free.png) \| ![maid-4480](4480/previews/maid.png) \| ![miko-4480](4480/previews/miko.png) \| [<NSFW, click to see>](4480/previews/nude.png) \| [<NSFW, click to see>](4480/previews/nude2.png) \| ![suit-4480](4480/previews/suit.png) \| ![yukata-4480](4480/previews/yukata.png) \| \| 3920 \| 0.777 \| [Download](3920/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-3920](3920/previews/pattern_1.png) \| ![pattern_2-3920](3920/previews/pattern_2.png) \| ![pattern_3-3920](3920/previews/pattern_3.png) \| ![pattern_4-3920](3920/previews/pattern_4.png) \| ![pattern_5-3920](3920/previews/pattern_5.png) \| ![pattern_6-3920](3920/previews/pattern_6.png) \| ![pattern_7-3920](3920/previews/pattern_7.png) \| [<NSFW, click to see>](3920/previews/pattern_8.png) \| [<NSFW, click to see>](3920/previews/pattern_9.png) \| ![pattern_10-3920](3920/previews/pattern_10.png) \| ![pattern_11-3920](3920/previews/pattern_11.png) \| ![pattern_12-3920](3920/previews/pattern_12.png) \| ![pattern_13-3920](3920/previews/pattern_13.png) \| ![pattern_14-3920](3920/previews/pattern_14.png) \| [<NSFW, click to see>](3920/previews/bikini.png) \| [<NSFW, click to see>](3920/previews/bondage.png) \| ![free-3920](3920/previews/free.png) \| ![maid-3920](3920/previews/maid.png) \| ![miko-3920](3920/previews/miko.png) \| [<NSFW, click to see>](3920/previews/nude.png) \| [<NSFW, click to see>](3920/previews/nude2.png) \| ![suit-3920](3920/previews/suit.png) \| ![yukata-3920](3920/previews/yukata.png) \| \| 3360 \| 0.786 \| [Download](3360/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-3360](3360/previews/pattern_1.png) \| ![pattern_2-3360](3360/previews/pattern_2.png) \| ![pattern_3-3360](3360/previews/pattern_3.png) \| ![pattern_4-3360](3360/previews/pattern_4.png) \| ![pattern_5-3360](3360/previews/pattern_5.png) \| ![pattern_6-3360](3360/previews/pattern_6.png) \| ![pattern_7-3360](3360/previews/pattern_7.png) \| [<NSFW, click to see>](3360/previews/pattern_8.png) \| [<NSFW, click to see>](3360/previews/pattern_9.png) \| ![pattern_10-3360](3360/previews/pattern_10.png) \| ![pattern_11-3360](3360/previews/pattern_11.png) \| ![pattern_12-3360](3360/previews/pattern_12.png) \| ![pattern_13-3360](3360/previews/pattern_13.png) \| ![pattern_14-3360](3360/previews/pattern_14.png) \| [<NSFW, click to see>](3360/previews/bikini.png) \| [<NSFW, click to see>](3360/previews/bondage.png) \| ![free-3360](3360/previews/free.png) \| ![maid-3360](3360/previews/maid.png) \| ![miko-3360](3360/previews/miko.png) \| [<NSFW, click to see>](3360/previews/nude.png) \| [<NSFW, click to see>](3360/previews/nude2.png) \| ![suit-3360](3360/previews/suit.png) \| ![yukata-3360](3360/previews/yukata.png) \| \| 2800 \| 0.810 \| [Download](2800/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-2800](2800/previews/pattern_1.png) \| ![pattern_2-2800](2800/previews/pattern_2.png) \| ![pattern_3-2800](2800/previews/pattern_3.png) \| ![pattern_4-2800](2800/previews/pattern_4.png) \| ![pattern_5-2800](2800/previews/pattern_5.png) \| ![pattern_6-2800](2800/previews/pattern_6.png) \| ![pattern_7-2800](2800/previews/pattern_7.png) \| [<NSFW, click to see>](2800/previews/pattern_8.png) \| [<NSFW, click to see>](2800/previews/pattern_9.png) \| ![pattern_10-2800](2800/previews/pattern_10.png) \| ![pattern_11-2800](2800/previews/pattern_11.png) \| ![pattern_12-2800](2800/previews/pattern_12.png) \| ![pattern_13-2800](2800/previews/pattern_13.png) \| ![pattern_14-2800](2800/previews/pattern_14.png) \| [<NSFW, click to see>](2800/previews/bikini.png) \| [<NSFW, click to see>](2800/previews/bondage.png) \| ![free-2800](2800/previews/free.png) \| ![maid-2800](2800/previews/maid.png) \| ![miko-2800](2800/previews/miko.png) \| [<NSFW, click to see>](2800/previews/nude.png) \| [<NSFW, click to see>](2800/previews/nude2.png) \| ![suit-2800](2800/previews/suit.png) \| ![yukata-2800](2800/previews/yukata.png) \| \| 2240 \| 0.586 \| [Download](2240/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-2240](2240/previews/pattern_1.png) \| ![pattern_2-2240](2240/previews/pattern_2.png) \| ![pattern_3-2240](2240/previews/pattern_3.png) \| ![pattern_4-2240](2240/previews/pattern_4.png) \| ![pattern_5-2240](2240/previews/pattern_5.png) \| ![pattern_6-2240](2240/previews/pattern_6.png) \| ![pattern_7-2240](2240/previews/pattern_7.png) \| [<NSFW, click to see>](2240/previews/pattern_8.png) \| [<NSFW, click to see>](2240/previews/pattern_9.png) \| ![pattern_10-2240](2240/previews/pattern_10.png) \| ![pattern_11-2240](2240/previews/pattern_11.png) \| ![pattern_12-2240](2240/previews/pattern_12.png) \| ![pattern_13-2240](2240/previews/pattern_13.png) \| ![pattern_14-2240](2240/previews/pattern_14.png) \| [<NSFW, click to see>](2240/previews/bikini.png) \| [<NSFW, click to see>](2240/previews/bondage.png) \| ![free-2240](2240/previews/free.png) \| ![maid-2240](2240/previews/maid.png) \| ![miko-2240](2240/previews/miko.png) \| [<NSFW, click to see>](2240/previews/nude.png) \| [<NSFW, click to see>](2240/previews/nude2.png) \| ![suit-2240](2240/previews/suit.png) \| ![yukata-2240](2240/previews/yukata.png) \| \| 1680 \| 0.719 \| [Download](1680/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-1680](1680/previews/pattern_1.png) \| ![pattern_2-1680](1680/previews/pattern_2.png) \| ![pattern_3-1680](1680/previews/pattern_3.png) \| ![pattern_4-1680](1680/previews/pattern_4.png) \| ![pattern_5-1680](1680/previews/pattern_5.png) \| ![pattern_6-1680](1680/previews/pattern_6.png) \| ![pattern_7-1680](1680/previews/pattern_7.png) \| [<NSFW, click to see>](1680/previews/pattern_8.png) \| [<NSFW, click to see>](1680/previews/pattern_9.png) \| ![pattern_10-1680](1680/previews/pattern_10.png) \| ![pattern_11-1680](1680/previews/pattern_11.png) \| ![pattern_12-1680](1680/previews/pattern_12.png) \| ![pattern_13-1680](1680/previews/pattern_13.png) \| ![pattern_14-1680](1680/previews/pattern_14.png) \| [<NSFW, click to see>](1680/previews/bikini.png) \| [<NSFW, click to see>](1680/previews/bondage.png) \| ![free-1680](1680/previews/free.png) \| ![maid-1680](1680/previews/maid.png) \| ![miko-1680](1680/previews/miko.png) \| [<NSFW, click to see>](1680/previews/nude.png) \| [<NSFW, click to see>](1680/previews/nude2.png) \| ![suit-1680](1680/previews/suit.png) \| ![yukata-1680](1680/previews/yukata.png) \| \| 1120 \| 0.677 \| [Download](1120/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-1120](1120/previews/pattern_1.png) \| ![pattern_2-1120](1120/previews/pattern_2.png) \| ![pattern_3-1120](1120/previews/pattern_3.png) \| ![pattern_4-1120](1120/previews/pattern_4.png) \| ![pattern_5-1120](1120/previews/pattern_5.png) \| ![pattern_6-1120](1120/previews/pattern_6.png) \| ![pattern_7-1120](1120/previews/pattern_7.png) \| [<NSFW, click to see>](1120/previews/pattern_8.png) \| [<NSFW, click to see>](1120/previews/pattern_9.png) \| ![pattern_10-1120](1120/previews/pattern_10.png) \| ![pattern_11-1120](1120/previews/pattern_11.png) \| ![pattern_12-1120](1120/previews/pattern_12.png) \| ![pattern_13-1120](1120/previews/pattern_13.png) \| ![pattern_14-1120](1120/previews/pattern_14.png) \| [<NSFW, click to see>](1120/previews/bikini.png) \| [<NSFW, click to see>](1120/previews/bondage.png) \| ![free-1120](1120/previews/free.png) \| ![maid-1120](1120/previews/maid.png) \| ![miko-1120](1120/previews/miko.png) \| [<NSFW, click to see>](1120/previews/nude.png) \| [<NSFW, click to see>](1120/previews/nude2.png) \| ![suit-1120](1120/previews/suit.png) \| ![yukata-1120](1120/previews/yukata.png) \| \| 560 \| 0.433 \| [Download](560/yorita_yoshino_idolmastercinderellagirls.zip) \| ![pattern_1-560](560/previews/pattern_1.png) \| ![pattern_2-560](560/previews/pattern_2.png) \| ![pattern_3-560](560/previews/pattern_3.png) \| ![pattern_4-560](560/previews/pattern_4.png) \| ![pattern_5-560](560/previews/pattern_5.png) \| ![pattern_6-560](560/previews/pattern_6.png) \| ![pattern_7-560](560/previews/pattern_7.png) \| [<NSFW, click to see>](560/previews/pattern_8.png) \| [<NSFW, click to see>](560/previews/pattern_9.png) \| ![pattern_10-560](560/previews/pattern_10.png) \| ![pattern_11-560](560/previews/pattern_11.png) \| ![pattern_12-560](560/previews/pattern_12.png) \| ![pattern_13-560](560/previews/pattern_13.png) \| ![pattern_14-560](560/previews/pattern_14.png) \| [<NSFW, click to see>](560/previews/bikini.png) \| [<NSFW, click to see>](560/previews/bondage.png) \| ![free-560](560/previews/free.png) \| ![maid-560](560/previews/maid.png) \| ![miko-560](560/previews/miko.png) \| [<NSFW, click to see>](560/previews/nude.png) \| [<NSFW, click to see>](560/previews/nude2.png) \| ![suit-560](560/previews/suit.png) \| ![yukata-560](560/previews/yukata.png) \|
huyen89/Reinforce-CartPole-v1	huyen89	2023-09-18T04:21:03Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-09-18T04:20:58Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 223.40 +/- 21.61 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
ys7yoo/sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep7_ckpt	ys7yoo	2023-09-18T04:19:15Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "base_model:finetune:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T03:57:43Z	--- base_model: ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3 tags: - generated_from_trainer datasets: - klue model-index: - name: sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep7_ckpt 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. --> # sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep7_ckpt This model is a fine-tuned version of [ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3](https://huggingface.co/ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3202 - Mse: 0.3202 - Mae: 0.4109 - R2: 0.8534 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| Mae \| R2 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| 1.0857 \| 1.0 \| 183 \| 0.4208 \| 0.4208 \| 0.4787 \| 0.8073 \| \| 0.1397 \| 2.0 \| 366 \| 0.3135 \| 0.3135 \| 0.4191 \| 0.8565 \| \| 0.0989 \| 3.0 \| 549 \| 0.3468 \| 0.3468 \| 0.4261 \| 0.8412 \| \| 0.0757 \| 4.0 \| 732 \| 0.3006 \| 0.3006 \| 0.3959 \| 0.8623 \| \| 0.0601 \| 5.0 \| 915 \| 0.4034 \| 0.4034 \| 0.4669 \| 0.8153 \| \| 0.0502 \| 6.0 \| 1098 \| 0.3357 \| 0.3357 \| 0.4221 \| 0.8463 \| \| 0.0429 \| 7.0 \| 1281 \| 0.3202 \| 0.3202 \| 0.4109 \| 0.8534 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3
m-aliabbas1/erc_question_big_model	m-aliabbas1	2023-09-18T04:01:58Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-09-18T04:01:12Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # m-aliabbas1/erc_question_big_model This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("m-aliabbas1/erc_question_big_model") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
dsmsb/16_combo_webscrap_1709_v2_reduce_others	dsmsb	2023-09-18T04:00:21Z	7	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T01:47:02Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: 16_combo_webscrap_1709_v2_reduce_others 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. --> # 16_combo_webscrap_1709_v2_reduce_others This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1501 - Accuracy: 0.9636 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 363 \| 1.0481 \| 0.7263 \| \| 1.5287 \| 2.0 \| 726 \| 0.5613 \| 0.8655 \| \| 0.6856 \| 3.0 \| 1089 \| 0.3666 \| 0.9121 \| \| 0.6856 \| 4.0 \| 1452 \| 0.2880 \| 0.9284 \| \| 0.4313 \| 5.0 \| 1815 \| 0.2187 \| 0.9464 \| \| 0.3097 \| 6.0 \| 2178 \| 0.1992 \| 0.9505 \| \| 0.2454 \| 7.0 \| 2541 \| 0.1627 \| 0.9598 \| \| 0.2454 \| 8.0 \| 2904 \| 0.1501 \| 0.9636 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
axelit64/image_classification	axelit64	2023-09-18T03:56:43Z	229	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-18T03:07:32Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: image_classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.575 --- <!-- 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. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3340 - Accuracy: 0.575 ## 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 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 1.5156 \| 0.45 \| \| No log \| 2.0 \| 80 \| 1.4200 \| 0.4562 \| \| No log \| 3.0 \| 120 \| 1.3790 \| 0.5 \| \| No log \| 4.0 \| 160 \| 1.2859 \| 0.525 \| \| No log \| 5.0 \| 200 \| 1.2592 \| 0.5125 \| \| No log \| 6.0 \| 240 \| 1.3145 \| 0.55 \| \| No log \| 7.0 \| 280 \| 1.3267 \| 0.4813 \| \| No log \| 8.0 \| 320 \| 1.3288 \| 0.5 \| \| No log \| 9.0 \| 360 \| 1.3073 \| 0.5 \| \| No log \| 10.0 \| 400 \| 1.3066 \| 0.5188 \| \| No log \| 11.0 \| 440 \| 1.2691 \| 0.5563 \| \| No log \| 12.0 \| 480 \| 1.2809 \| 0.5437 \| \| 0.876 \| 13.0 \| 520 \| 1.2963 \| 0.5625 \| \| 0.876 \| 14.0 \| 560 \| 1.2965 \| 0.5312 \| \| 0.876 \| 15.0 \| 600 \| 1.3542 \| 0.5188 \| \| 0.876 \| 16.0 \| 640 \| 1.3489 \| 0.5125 \| \| 0.876 \| 17.0 \| 680 \| 1.3146 \| 0.5687 \| \| 0.876 \| 18.0 \| 720 \| 1.2442 \| 0.575 \| \| 0.876 \| 19.0 \| 760 \| 1.3497 \| 0.575 \| \| 0.876 \| 20.0 \| 800 \| 1.3316 \| 0.5437 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
handi88/FastJobs-Visual_Emotions_Analysis	handi88	2023-09-18T03:55:02Z	0	0	null	[ "generated_from_trainer", "dataset:FastJobs/Visual_Emotional_Analysis", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "region:us" ]	null	2023-09-18T03:42:58Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - FastJobs/Visual_Emotional_Analysis metrics: - accuracy - precision - f1 model-index: - name: emotion_classification results: - task: name: Image Classification type: image-classification dataset: name: FastJobs/Visual_Emotional_Analysis type: FastJobs/Visual_Emotional_Analysis config: FastJobs--Visual_Emotional_Analysis split: train args: FastJobs--Visual_Emotional_Analysis metrics: - name: Accuracy type: accuracy value: 0.66875 - name: Precision type: precision value: 0.7104119480438352 - name: F1 type: f1 value: 0.6712765732314218 --- # Emotion Classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset. In theory, the accuracy for a random guess on this dataset is 0.125 (8 labels and you need to choose one). It achieves the following results on the evaluation set: - Loss: 1.0511 - Accuracy: 0.6687 - Precision: 0.7104 - F1: 0.6713 ## Model description The Vision Transformer base version trained on ImageNet-21K released by Google. Further details can be found on their [repo](https://huggingface.co/google/vit-base-patch16-224-in21k). ## Training and evaluation data ### Data Split Trained on [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset. Used a 4:1 ratio for training and development sets and a random seed of 42. Also used a seed of 42 for batching the data, completely unrelated lol. ### Pre-processing Augmentation The main pre-processing phase for both training and evaluation includes: - Bilinear interpolation to resize the image to (224, 224, 3) because it uses ImageNet images to train the original model - Normalizing images using a mean and standard deviation of [0.5, 0.5, 0.5] just like the original model Other than the aforementioned pre-processing, the training set was augmented using: - Random horizontal & vertical flip - Color jitter - Random resized crop ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: cosine_with_restarts - lr_scheduler_warmup_steps: 150 - num_epochs: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\| \| 2.079 \| 1.0 \| 10 \| 2.0895 \| 0.0563 \| 0.0604 \| 0.0521 \| \| 2.0789 \| 2.0 \| 20 \| 2.0851 \| 0.0563 \| 0.0602 \| 0.0529 \| \| 2.0717 \| 3.0 \| 30 \| 2.0773 \| 0.0813 \| 0.0858 \| 0.0783 \| \| 2.0613 \| 4.0 \| 40 \| 2.0658 \| 0.125 \| 0.1997 \| 0.1333 \| \| 2.0445 \| 5.0 \| 50 \| 2.0483 \| 0.1875 \| 0.2569 \| 0.1934 \| \| 2.0176 \| 6.0 \| 60 \| 2.0206 \| 0.2313 \| 0.2692 \| 0.2384 \| \| 1.9894 \| 7.0 \| 70 \| 1.9763 \| 0.3063 \| 0.3033 \| 0.2983 \| \| 1.9232 \| 8.0 \| 80 \| 1.8912 \| 0.3625 \| 0.3307 \| 0.3194 \| \| 1.8256 \| 9.0 \| 90 \| 1.7775 \| 0.4062 \| 0.3531 \| 0.3600 \| \| 1.732 \| 10.0 \| 100 \| 1.6580 \| 0.4688 \| 0.4158 \| 0.4133 \| \| 1.6406 \| 11.0 \| 110 \| 1.5597 \| 0.5 \| 0.4358 \| 0.4370 \| \| 1.5584 \| 12.0 \| 120 \| 1.4855 \| 0.5125 \| 0.4792 \| 0.4784 \| \| 1.4898 \| 13.0 \| 130 \| 1.4248 \| 0.5437 \| 0.5011 \| 0.5098 \| \| 1.4216 \| 14.0 \| 140 \| 1.3692 \| 0.5687 \| 0.5255 \| 0.5289 \| \| 1.3701 \| 15.0 \| 150 \| 1.3158 \| 0.5687 \| 0.5346 \| 0.5360 \| \| 1.3438 \| 16.0 \| 160 \| 1.2842 \| 0.5437 \| 0.5451 \| 0.5098 \| \| 1.2799 \| 17.0 \| 170 \| 1.2620 \| 0.5625 \| 0.5169 \| 0.5194 \| \| 1.2481 \| 18.0 \| 180 \| 1.2321 \| 0.5938 \| 0.6003 \| 0.5811 \| \| 1.1993 \| 19.0 \| 190 \| 1.2108 \| 0.5687 \| 0.5640 \| 0.5412 \| \| 1.1599 \| 20.0 \| 200 \| 1.1853 \| 0.55 \| 0.5434 \| 0.5259 \| \| 1.1087 \| 21.0 \| 210 \| 1.1839 \| 0.5563 \| 0.5670 \| 0.5380 \| \| 1.0757 \| 22.0 \| 220 \| 1.1905 \| 0.55 \| 0.5682 \| 0.5308 \| \| 0.9985 \| 23.0 \| 230 \| 1.1509 \| 0.6375 \| 0.6714 \| 0.6287 \| \| 0.9776 \| 24.0 \| 240 \| 1.1048 \| 0.6188 \| 0.6222 \| 0.6127 \| \| 0.9331 \| 25.0 \| 250 \| 1.1196 \| 0.6125 \| 0.6345 \| 0.6072 \| \| 0.8887 \| 26.0 \| 260 \| 1.1424 \| 0.5938 \| 0.6174 \| 0.5867 \| \| 0.879 \| 27.0 \| 270 \| 1.1232 \| 0.6062 \| 0.6342 \| 0.5978 \| \| 0.8369 \| 28.0 \| 280 \| 1.1172 \| 0.6 \| 0.6480 \| 0.5865 \| \| 0.7864 \| 29.0 \| 290 \| 1.1285 \| 0.5938 \| 0.6819 \| 0.5763 \| \| 0.7775 \| 30.0 \| 300 \| 1.0511 \| 0.6687 \| 0.7104 \| 0.6713 \| \| 0.7281 \| 31.0 \| 310 \| 1.0295 \| 0.6562 \| 0.6596 \| 0.6514 \| \| 0.7348 \| 32.0 \| 320 \| 1.0398 \| 0.6375 \| 0.6353 \| 0.6319 \| \| 0.6896 \| 33.0 \| 330 \| 1.0729 \| 0.6062 \| 0.6205 \| 0.6062 \| \| 0.613 \| 34.0 \| 340 \| 1.0505 \| 0.6438 \| 0.6595 \| 0.6421 \| \| 0.6034 \| 35.0 \| 350 \| 1.0827 \| 0.6375 \| 0.6593 \| 0.6376 \| \| 0.6236 \| 36.0 \| 360 \| 1.1271 \| 0.6125 \| 0.6238 \| 0.6087 \| \| 0.5607 \| 37.0 \| 370 \| 1.0985 \| 0.6062 \| 0.6254 \| 0.6015 \| \| 0.5835 \| 38.0 \| 380 \| 1.0791 \| 0.6375 \| 0.6624 \| 0.6370 \| \| 0.5889 \| 39.0 \| 390 \| 1.1300 \| 0.6062 \| 0.6529 \| 0.6092 \| \| 0.5137 \| 40.0 \| 400 \| 1.1062 \| 0.625 \| 0.6457 \| 0.6226 \| \| 0.4804 \| 41.0 \| 410 \| 1.1452 \| 0.6188 \| 0.6403 \| 0.6158 \| \| 0.4811 \| 42.0 \| 420 \| 1.1271 \| 0.6375 \| 0.6478 \| 0.6347 \| \| 0.5179 \| 43.0 \| 430 \| 1.1942 \| 0.5875 \| 0.6185 \| 0.5874 \| \| 0.4744 \| 44.0 \| 440 \| 1.1515 \| 0.6125 \| 0.6329 \| 0.6160 \| \| 0.4327 \| 45.0 \| 450 \| 1.1321 \| 0.6375 \| 0.6669 \| 0.6412 \| \| 0.4565 \| 46.0 \| 460 \| 1.1742 \| 0.625 \| 0.6478 \| 0.6251 \| \| 0.4006 \| 47.0 \| 470 \| 1.1675 \| 0.6062 \| 0.6361 \| 0.6079 \| \| 0.4541 \| 48.0 \| 480 \| 1.1542 \| 0.6125 \| 0.6404 \| 0.6152 \| \| 0.3689 \| 49.0 \| 490 \| 1.2190 \| 0.5875 \| 0.6134 \| 0.5896 \| \| 0.3794 \| 50.0 \| 500 \| 1.2002 \| 0.6062 \| 0.6155 \| 0.6005 \| \| 0.429 \| 51.0 \| 510 \| 1.2904 \| 0.575 \| 0.6207 \| 0.5849 \| \| 0.431 \| 52.0 \| 520 \| 1.2416 \| 0.5875 \| 0.6028 \| 0.5794 \| \| 0.3813 \| 53.0 \| 530 \| 1.2073 \| 0.6125 \| 0.6449 \| 0.6142 \| \| 0.365 \| 54.0 \| 540 \| 1.2083 \| 0.6062 \| 0.6454 \| 0.6075 \| \| 0.3714 \| 55.0 \| 550 \| 1.1627 \| 0.6375 \| 0.6576 \| 0.6390 \| \| 0.3393 \| 56.0 \| 560 \| 1.1620 \| 0.6438 \| 0.6505 \| 0.6389 \| \| 0.3676 \| 57.0 \| 570 \| 1.1501 \| 0.625 \| 0.6294 \| 0.6258 \| \| 0.3371 \| 58.0 \| 580 \| 1.2779 \| 0.5875 \| 0.6000 \| 0.5792 \| \| 0.3325 \| 59.0 \| 590 \| 1.2719 \| 0.575 \| 0.5843 \| 0.5651 \| \| 0.3509 \| 60.0 \| 600 \| 1.2956 \| 0.6 \| 0.6422 \| 0.6059 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
m-aliabbas1/set_fit_practice	m-aliabbas1	2023-09-18T03:49:01Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-09-18T03:48:43Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # m-aliabbas1/set_fit_practice This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("m-aliabbas1/set_fit_practice") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
shaowenchen/chinese-alpaca-2-13b-gguf	shaowenchen	2023-09-18T03:44:45Z	100	0	null	[ "gguf", "meta", "llama", "llama-2", "alpaca", "alpaca-2", "chinese", "text-generation", "zh", "license:other", "region:us" ]	text-generation	2023-09-16T23:34:00Z	--- inference: false language: - zh license: other model_creator: ziqingyang model_link: https://huggingface.co/ziqingyang/chinese-alpaca-2-13b model_name: chinese-alpaca-2-13b model_type: llama pipeline_tag: text-generation quantized_by: shaowenchen tasks: - text2text-generation tags: - meta - gguf - llama - llama-2 - alpaca - alpaca-2 - chinese --- ## Provided files \| Name \| Quant method \| Size \| \| -------------------------------- \| ------------ \| ------- \| \| chinese-alpaca-2-13b.Q2_K.gguf \| Q2_K \| 5.2 GB \| \| chinese-alpaca-2-13b.Q3_K.gguf \| Q3_K \| 6.0 GB \| \| chinese-alpaca-2-13b.Q3_K_L.gguf \| Q3_K_L \| 6.6 GB \| \| chinese-alpaca-2-13b.Q3_K_S.gguf \| Q3_K_S \| 5.4 GB \| \| chinese-alpaca-2-13b.Q4_0.gguf \| Q4_0 \| 7.0 GB \| \| chinese-alpaca-2-13b.Q4_1.gguf \| Q4_1 \| 7.8 GB \| \| chinese-alpaca-2-13b.Q4_K.gguf \| Q4_K \| 7.5 GB \| \| chinese-alpaca-2-13b.Q4_K_S.gguf \| Q4_K_S \| 7.1 GB \| \| chinese-alpaca-2-13b.Q5_0.gguf \| Q5_0 \| 8.5 GB \| \| chinese-alpaca-2-13b.Q5_1.gguf \| Q5_1 \| 9.3 GB \| \| chinese-alpaca-2-13b.Q5_K.gguf \| Q5_K \| 8.8 GB \| \| chinese-alpaca-2-13b.Q5_K_S.gguf \| Q5_K_S \| 8.5 GB \| \| chinese-alpaca-2-13b.Q6_K.gguf \| Q6_K \| 10.0 GB \| \| chinese-alpaca-2-13b.Q8_0.gguf \| Q8_0 \| 13.0 GB \| \| chinese-alpaca-2-13b.gguf \| full \| 25.0 GB \| Usage: ``` docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/gguf-model-name.gguf hubimage/llama-cpp-python:latest ``` and you can view http://localhost:8000/docs to see the swagger UI.
Chickenfish/Dayte_dreambooth	Chickenfish	2023-09-18T03:41:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-22T07:18:04Z	--- license: creativeml-openrail-m ---
nemesis1/chlldrgnrc	nemesis1	2023-09-18T03:28:31Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-18T03:28:31Z	--- license: creativeml-openrail-m ---
LarryAIDraw/shana1-000008	LarryAIDraw	2023-09-18T03:26:44Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-18T03:21:22Z	--- license: creativeml-openrail-m --- https://civitai.com/models/47454/shana-or-character-lora-974
ys7yoo/sts_roberta-large_lr1e-05_wd1e-03_ep7_ckpt	ys7yoo	2023-09-18T03:26:33Z	108	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T03:03:05Z	--- base_model: klue/roberta-large tags: - generated_from_trainer datasets: - klue model-index: - name: sts_roberta-large_lr1e-05_wd1e-03_ep7_ckpt 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. --> # sts_roberta-large_lr1e-05_wd1e-03_ep7_ckpt This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3621 - Mse: 0.3621 - Mae: 0.4438 - R2: 0.8342 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| Mae \| R2 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| 1.8712 \| 1.0 \| 183 \| 0.5118 \| 0.5118 \| 0.5409 \| 0.7656 \| \| 0.1606 \| 2.0 \| 366 \| 0.4621 \| 0.4621 \| 0.5142 \| 0.7884 \| \| 0.1111 \| 3.0 \| 549 \| 0.4687 \| 0.4687 \| 0.5088 \| 0.7854 \| \| 0.0837 \| 4.0 \| 732 \| 0.4317 \| 0.4317 \| 0.4906 \| 0.8023 \| \| 0.0681 \| 5.0 \| 915 \| 0.4662 \| 0.4662 \| 0.5091 \| 0.7865 \| \| 0.0559 \| 6.0 \| 1098 \| 0.3742 \| 0.3742 \| 0.4524 \| 0.8286 \| \| 0.0485 \| 7.0 \| 1281 \| 0.3621 \| 0.3621 \| 0.4438 \| 0.8342 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3
LarryAIDraw/Goddess_of_Light_Avatar	LarryAIDraw	2023-09-18T03:26:22Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-18T03:20:25Z	--- license: creativeml-openrail-m --- https://civitai.com/models/145816/tang-wutong-or-or-goddess-of-light-fusion-skill-avatar-or-soul-land-ii-or-douluo-dalu-ii-jueshi-tangmen-or-2-or-manhua
LarryAIDraw/schwarz_arknights	LarryAIDraw	2023-09-18T03:25:10Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-18T03:19:18Z	--- license: creativeml-openrail-m --- https://civitai.com/models/130905/schwarz-arknights
guydebruyn/Reinforce-Copter2	guydebruyn	2023-09-18T03:24:04Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-09-18T03:24:01Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Copter2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
zeenfts/output_dir	zeenfts	2023-09-18T03:17:42Z	28	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-16T08:08:06Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: output_dir results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6 --- <!-- 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. --> # output_dir This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2976 - Accuracy: 0.6 ## 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: 7e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: reduce_lr_on_plateau - num_epochs: 77 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 2 \| 2.0706 \| 0.15 \| \| No log \| 2.0 \| 5 \| 2.0309 \| 0.2313 \| \| No log \| 2.8 \| 7 \| 1.9846 \| 0.2562 \| \| 1.9868 \| 4.0 \| 10 \| 1.8915 \| 0.4062 \| \| 1.9868 \| 4.8 \| 12 \| 1.8529 \| 0.3125 \| \| 1.9868 \| 6.0 \| 15 \| 1.7422 \| 0.4125 \| \| 1.9868 \| 6.8 \| 17 \| 1.6761 \| 0.4313 \| \| 1.6815 \| 8.0 \| 20 \| 1.6310 \| 0.4562 \| \| 1.6815 \| 8.8 \| 22 \| 1.5900 \| 0.45 \| \| 1.6815 \| 10.0 \| 25 \| 1.5402 \| 0.4313 \| \| 1.6815 \| 10.8 \| 27 \| 1.5018 \| 0.5 \| \| 1.4233 \| 12.0 \| 30 \| 1.4620 \| 0.4875 \| \| 1.4233 \| 12.8 \| 32 \| 1.4286 \| 0.5062 \| \| 1.4233 \| 14.0 \| 35 \| 1.4045 \| 0.5125 \| \| 1.4233 \| 14.8 \| 37 \| 1.3860 \| 0.5312 \| \| 1.2127 \| 16.0 \| 40 \| 1.3571 \| 0.5 \| \| 1.2127 \| 16.8 \| 42 \| 1.3293 \| 0.5375 \| \| 1.2127 \| 18.0 \| 45 \| 1.3742 \| 0.4813 \| \| 1.2127 \| 18.8 \| 47 \| 1.3151 \| 0.5437 \| \| 1.0075 \| 20.0 \| 50 \| 1.3053 \| 0.5312 \| \| 1.0075 \| 20.8 \| 52 \| 1.3266 \| 0.5375 \| \| 1.0075 \| 22.0 \| 55 \| 1.2964 \| 0.5312 \| \| 1.0075 \| 22.8 \| 57 \| 1.2278 \| 0.5875 \| \| 0.8232 \| 24.0 \| 60 \| 1.2501 \| 0.5563 \| \| 0.8232 \| 24.8 \| 62 \| 1.2330 \| 0.575 \| \| 0.8232 \| 26.0 \| 65 \| 1.2198 \| 0.5625 \| \| 0.8232 \| 26.8 \| 67 \| 1.2071 \| 0.5875 \| \| 0.6738 \| 28.0 \| 70 \| 1.2643 \| 0.5875 \| \| 0.6738 \| 28.8 \| 72 \| 1.2594 \| 0.5563 \| \| 0.6738 \| 30.0 \| 75 \| 1.2263 \| 0.5312 \| \| 0.6738 \| 30.8 \| 77 \| 1.3218 \| 0.5188 \| \| 0.5715 \| 32.0 \| 80 \| 1.2593 \| 0.5312 \| \| 0.5715 \| 32.8 \| 82 \| 1.2214 \| 0.5625 \| \| 0.5715 \| 34.0 \| 85 \| 1.3060 \| 0.55 \| \| 0.5715 \| 34.8 \| 87 \| 1.2727 \| 0.5563 \| \| 0.4523 \| 36.0 \| 90 \| 1.2749 \| 0.5375 \| \| 0.4523 \| 36.8 \| 92 \| 1.3570 \| 0.5437 \| \| 0.4523 \| 38.0 \| 95 \| 1.2815 \| 0.5687 \| \| 0.4523 \| 38.8 \| 97 \| 1.2233 \| 0.6062 \| \| 0.3971 \| 40.0 \| 100 \| 1.2097 \| 0.6 \| \| 0.3971 \| 40.8 \| 102 \| 1.2881 \| 0.5813 \| \| 0.3971 \| 42.0 \| 105 \| 1.2400 \| 0.575 \| \| 0.3971 \| 42.8 \| 107 \| 1.3140 \| 0.5375 \| \| 0.3616 \| 44.0 \| 110 \| 1.1525 \| 0.6125 \| \| 0.3616 \| 44.8 \| 112 \| 1.2725 \| 0.5938 \| \| 0.3616 \| 46.0 \| 115 \| 1.2634 \| 0.5813 \| \| 0.3616 \| 46.8 \| 117 \| 1.2299 \| 0.6 \| \| 0.338 \| 48.0 \| 120 \| 1.3408 \| 0.5375 \| \| 0.338 \| 48.8 \| 122 \| 1.1931 \| 0.5938 \| \| 0.338 \| 50.0 \| 125 \| 1.2806 \| 0.5938 \| \| 0.338 \| 50.8 \| 127 \| 1.2410 \| 0.575 \| \| 0.3445 \| 52.0 \| 130 \| 1.2901 \| 0.5813 \| \| 0.3445 \| 52.8 \| 132 \| 1.2504 \| 0.6062 \| \| 0.3445 \| 54.0 \| 135 \| 1.1614 \| 0.5875 \| \| 0.3445 \| 54.8 \| 137 \| 1.2247 \| 0.6062 \| \| 0.3299 \| 56.0 \| 140 \| 1.2591 \| 0.5625 \| \| 0.3299 \| 56.8 \| 142 \| 1.2629 \| 0.5687 \| \| 0.3299 \| 58.0 \| 145 \| 1.2369 \| 0.5938 \| \| 0.3299 \| 58.8 \| 147 \| 1.2771 \| 0.575 \| \| 0.3292 \| 60.0 \| 150 \| 1.3284 \| 0.5875 \| \| 0.3292 \| 60.8 \| 152 \| 1.2550 \| 0.5625 \| \| 0.3292 \| 61.6 \| 154 \| 1.3047 \| 0.55 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/hayasaka_mirei_idolmastercinderellagirls	CyberHarem	2023-09-18T03:11:12Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/hayasaka_mirei_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-18T02:48:28Z	--- license: mit datasets: - CyberHarem/hayasaka_mirei_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of hayasaka_mirei_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4320, you need to download `4320/hayasaka_mirei_idolmastercinderellagirls.pt` as the embedding and `4320/hayasaka_mirei_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4320, with the score of 0.973. The trigger words are: 1. `hayasaka_mirei_idolmastercinderellagirls` 2. `purple_hair, eyepatch, multicolored_hair, brown_eyes, short_hair, blush, red_hair, streaked_hair, open_mouth, fang, heart, hair_between_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| pattern_13 \| pattern_14 \| pattern_15 \| pattern_16 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 8100 \| 0.966 \| [Download](8100/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-8100](8100/previews/pattern_1.png) \| ![pattern_2-8100](8100/previews/pattern_2.png) \| ![pattern_3-8100](8100/previews/pattern_3.png) \| ![pattern_4-8100](8100/previews/pattern_4.png) \| ![pattern_5-8100](8100/previews/pattern_5.png) \| ![pattern_6-8100](8100/previews/pattern_6.png) \| ![pattern_7-8100](8100/previews/pattern_7.png) \| ![pattern_8-8100](8100/previews/pattern_8.png) \| ![pattern_9-8100](8100/previews/pattern_9.png) \| ![pattern_10-8100](8100/previews/pattern_10.png) \| ![pattern_11-8100](8100/previews/pattern_11.png) \| ![pattern_12-8100](8100/previews/pattern_12.png) \| ![pattern_13-8100](8100/previews/pattern_13.png) \| ![pattern_14-8100](8100/previews/pattern_14.png) \| ![pattern_15-8100](8100/previews/pattern_15.png) \| ![pattern_16-8100](8100/previews/pattern_16.png) \| ![bikini-8100](8100/previews/bikini.png) \| [<NSFW, click to see>](8100/previews/bondage.png) \| ![free-8100](8100/previews/free.png) \| ![maid-8100](8100/previews/maid.png) \| ![miko-8100](8100/previews/miko.png) \| [<NSFW, click to see>](8100/previews/nude.png) \| [<NSFW, click to see>](8100/previews/nude2.png) \| ![suit-8100](8100/previews/suit.png) \| ![yukata-8100](8100/previews/yukata.png) \| \| 7560 \| 0.969 \| [Download](7560/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-7560](7560/previews/pattern_1.png) \| ![pattern_2-7560](7560/previews/pattern_2.png) \| ![pattern_3-7560](7560/previews/pattern_3.png) \| ![pattern_4-7560](7560/previews/pattern_4.png) \| ![pattern_5-7560](7560/previews/pattern_5.png) \| ![pattern_6-7560](7560/previews/pattern_6.png) \| ![pattern_7-7560](7560/previews/pattern_7.png) \| ![pattern_8-7560](7560/previews/pattern_8.png) \| ![pattern_9-7560](7560/previews/pattern_9.png) \| ![pattern_10-7560](7560/previews/pattern_10.png) \| ![pattern_11-7560](7560/previews/pattern_11.png) \| ![pattern_12-7560](7560/previews/pattern_12.png) \| ![pattern_13-7560](7560/previews/pattern_13.png) \| ![pattern_14-7560](7560/previews/pattern_14.png) \| ![pattern_15-7560](7560/previews/pattern_15.png) \| ![pattern_16-7560](7560/previews/pattern_16.png) \| ![bikini-7560](7560/previews/bikini.png) \| [<NSFW, click to see>](7560/previews/bondage.png) \| ![free-7560](7560/previews/free.png) \| ![maid-7560](7560/previews/maid.png) \| ![miko-7560](7560/previews/miko.png) \| [<NSFW, click to see>](7560/previews/nude.png) \| [<NSFW, click to see>](7560/previews/nude2.png) \| ![suit-7560](7560/previews/suit.png) \| ![yukata-7560](7560/previews/yukata.png) \| \| 7020 \| 0.957 \| [Download](7020/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-7020](7020/previews/pattern_1.png) \| ![pattern_2-7020](7020/previews/pattern_2.png) \| ![pattern_3-7020](7020/previews/pattern_3.png) \| ![pattern_4-7020](7020/previews/pattern_4.png) \| ![pattern_5-7020](7020/previews/pattern_5.png) \| ![pattern_6-7020](7020/previews/pattern_6.png) \| ![pattern_7-7020](7020/previews/pattern_7.png) \| ![pattern_8-7020](7020/previews/pattern_8.png) \| ![pattern_9-7020](7020/previews/pattern_9.png) \| ![pattern_10-7020](7020/previews/pattern_10.png) \| ![pattern_11-7020](7020/previews/pattern_11.png) \| ![pattern_12-7020](7020/previews/pattern_12.png) \| ![pattern_13-7020](7020/previews/pattern_13.png) \| ![pattern_14-7020](7020/previews/pattern_14.png) \| ![pattern_15-7020](7020/previews/pattern_15.png) \| ![pattern_16-7020](7020/previews/pattern_16.png) \| ![bikini-7020](7020/previews/bikini.png) \| [<NSFW, click to see>](7020/previews/bondage.png) \| ![free-7020](7020/previews/free.png) \| ![maid-7020](7020/previews/maid.png) \| ![miko-7020](7020/previews/miko.png) \| [<NSFW, click to see>](7020/previews/nude.png) \| [<NSFW, click to see>](7020/previews/nude2.png) \| ![suit-7020](7020/previews/suit.png) \| ![yukata-7020](7020/previews/yukata.png) \| \| 6480 \| 0.968 \| [Download](6480/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-6480](6480/previews/pattern_1.png) \| ![pattern_2-6480](6480/previews/pattern_2.png) \| ![pattern_3-6480](6480/previews/pattern_3.png) \| ![pattern_4-6480](6480/previews/pattern_4.png) \| ![pattern_5-6480](6480/previews/pattern_5.png) \| ![pattern_6-6480](6480/previews/pattern_6.png) \| ![pattern_7-6480](6480/previews/pattern_7.png) \| ![pattern_8-6480](6480/previews/pattern_8.png) \| ![pattern_9-6480](6480/previews/pattern_9.png) \| ![pattern_10-6480](6480/previews/pattern_10.png) \| ![pattern_11-6480](6480/previews/pattern_11.png) \| ![pattern_12-6480](6480/previews/pattern_12.png) \| ![pattern_13-6480](6480/previews/pattern_13.png) \| ![pattern_14-6480](6480/previews/pattern_14.png) \| ![pattern_15-6480](6480/previews/pattern_15.png) \| ![pattern_16-6480](6480/previews/pattern_16.png) \| ![bikini-6480](6480/previews/bikini.png) \| [<NSFW, click to see>](6480/previews/bondage.png) \| ![free-6480](6480/previews/free.png) \| ![maid-6480](6480/previews/maid.png) \| ![miko-6480](6480/previews/miko.png) \| [<NSFW, click to see>](6480/previews/nude.png) \| [<NSFW, click to see>](6480/previews/nude2.png) \| ![suit-6480](6480/previews/suit.png) \| ![yukata-6480](6480/previews/yukata.png) \| \| 5940 \| 0.970 \| [Download](5940/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-5940](5940/previews/pattern_1.png) \| ![pattern_2-5940](5940/previews/pattern_2.png) \| ![pattern_3-5940](5940/previews/pattern_3.png) \| ![pattern_4-5940](5940/previews/pattern_4.png) \| ![pattern_5-5940](5940/previews/pattern_5.png) \| ![pattern_6-5940](5940/previews/pattern_6.png) \| ![pattern_7-5940](5940/previews/pattern_7.png) \| ![pattern_8-5940](5940/previews/pattern_8.png) \| ![pattern_9-5940](5940/previews/pattern_9.png) \| ![pattern_10-5940](5940/previews/pattern_10.png) \| ![pattern_11-5940](5940/previews/pattern_11.png) \| ![pattern_12-5940](5940/previews/pattern_12.png) \| ![pattern_13-5940](5940/previews/pattern_13.png) \| ![pattern_14-5940](5940/previews/pattern_14.png) \| ![pattern_15-5940](5940/previews/pattern_15.png) \| ![pattern_16-5940](5940/previews/pattern_16.png) \| ![bikini-5940](5940/previews/bikini.png) \| [<NSFW, click to see>](5940/previews/bondage.png) \| ![free-5940](5940/previews/free.png) \| ![maid-5940](5940/previews/maid.png) \| ![miko-5940](5940/previews/miko.png) \| [<NSFW, click to see>](5940/previews/nude.png) \| [<NSFW, click to see>](5940/previews/nude2.png) \| ![suit-5940](5940/previews/suit.png) \| ![yukata-5940](5940/previews/yukata.png) \| \| 5400 \| 0.907 \| [Download](5400/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-5400](5400/previews/pattern_1.png) \| ![pattern_2-5400](5400/previews/pattern_2.png) \| ![pattern_3-5400](5400/previews/pattern_3.png) \| ![pattern_4-5400](5400/previews/pattern_4.png) \| ![pattern_5-5400](5400/previews/pattern_5.png) \| ![pattern_6-5400](5400/previews/pattern_6.png) \| ![pattern_7-5400](5400/previews/pattern_7.png) \| ![pattern_8-5400](5400/previews/pattern_8.png) \| ![pattern_9-5400](5400/previews/pattern_9.png) \| ![pattern_10-5400](5400/previews/pattern_10.png) \| ![pattern_11-5400](5400/previews/pattern_11.png) \| ![pattern_12-5400](5400/previews/pattern_12.png) \| ![pattern_13-5400](5400/previews/pattern_13.png) \| ![pattern_14-5400](5400/previews/pattern_14.png) \| ![pattern_15-5400](5400/previews/pattern_15.png) \| ![pattern_16-5400](5400/previews/pattern_16.png) \| ![bikini-5400](5400/previews/bikini.png) \| [<NSFW, click to see>](5400/previews/bondage.png) \| ![free-5400](5400/previews/free.png) \| ![maid-5400](5400/previews/maid.png) \| ![miko-5400](5400/previews/miko.png) \| [<NSFW, click to see>](5400/previews/nude.png) \| [<NSFW, click to see>](5400/previews/nude2.png) \| ![suit-5400](5400/previews/suit.png) \| ![yukata-5400](5400/previews/yukata.png) \| \| 4860 \| 0.972 \| [Download](4860/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-4860](4860/previews/pattern_1.png) \| ![pattern_2-4860](4860/previews/pattern_2.png) \| ![pattern_3-4860](4860/previews/pattern_3.png) \| ![pattern_4-4860](4860/previews/pattern_4.png) \| ![pattern_5-4860](4860/previews/pattern_5.png) \| ![pattern_6-4860](4860/previews/pattern_6.png) \| ![pattern_7-4860](4860/previews/pattern_7.png) \| ![pattern_8-4860](4860/previews/pattern_8.png) \| ![pattern_9-4860](4860/previews/pattern_9.png) \| ![pattern_10-4860](4860/previews/pattern_10.png) \| ![pattern_11-4860](4860/previews/pattern_11.png) \| ![pattern_12-4860](4860/previews/pattern_12.png) \| ![pattern_13-4860](4860/previews/pattern_13.png) \| ![pattern_14-4860](4860/previews/pattern_14.png) \| ![pattern_15-4860](4860/previews/pattern_15.png) \| ![pattern_16-4860](4860/previews/pattern_16.png) \| ![bikini-4860](4860/previews/bikini.png) \| [<NSFW, click to see>](4860/previews/bondage.png) \| ![free-4860](4860/previews/free.png) \| ![maid-4860](4860/previews/maid.png) \| ![miko-4860](4860/previews/miko.png) \| [<NSFW, click to see>](4860/previews/nude.png) \| [<NSFW, click to see>](4860/previews/nude2.png) \| ![suit-4860](4860/previews/suit.png) \| ![yukata-4860](4860/previews/yukata.png) \| \| 4320 \| 0.973 \| [Download](4320/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-4320](4320/previews/pattern_1.png) \| ![pattern_2-4320](4320/previews/pattern_2.png) \| ![pattern_3-4320](4320/previews/pattern_3.png) \| ![pattern_4-4320](4320/previews/pattern_4.png) \| ![pattern_5-4320](4320/previews/pattern_5.png) \| ![pattern_6-4320](4320/previews/pattern_6.png) \| ![pattern_7-4320](4320/previews/pattern_7.png) \| ![pattern_8-4320](4320/previews/pattern_8.png) \| ![pattern_9-4320](4320/previews/pattern_9.png) \| ![pattern_10-4320](4320/previews/pattern_10.png) \| ![pattern_11-4320](4320/previews/pattern_11.png) \| ![pattern_12-4320](4320/previews/pattern_12.png) \| ![pattern_13-4320](4320/previews/pattern_13.png) \| ![pattern_14-4320](4320/previews/pattern_14.png) \| ![pattern_15-4320](4320/previews/pattern_15.png) \| ![pattern_16-4320](4320/previews/pattern_16.png) \| ![bikini-4320](4320/previews/bikini.png) \| [<NSFW, click to see>](4320/previews/bondage.png) \| ![free-4320](4320/previews/free.png) \| ![maid-4320](4320/previews/maid.png) \| ![miko-4320](4320/previews/miko.png) \| [<NSFW, click to see>](4320/previews/nude.png) \| [<NSFW, click to see>](4320/previews/nude2.png) \| ![suit-4320](4320/previews/suit.png) \| ![yukata-4320](4320/previews/yukata.png) \| \| 3780 \| 0.964 \| [Download](3780/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-3780](3780/previews/pattern_1.png) \| ![pattern_2-3780](3780/previews/pattern_2.png) \| ![pattern_3-3780](3780/previews/pattern_3.png) \| ![pattern_4-3780](3780/previews/pattern_4.png) \| ![pattern_5-3780](3780/previews/pattern_5.png) \| ![pattern_6-3780](3780/previews/pattern_6.png) \| ![pattern_7-3780](3780/previews/pattern_7.png) \| ![pattern_8-3780](3780/previews/pattern_8.png) \| ![pattern_9-3780](3780/previews/pattern_9.png) \| ![pattern_10-3780](3780/previews/pattern_10.png) \| ![pattern_11-3780](3780/previews/pattern_11.png) \| ![pattern_12-3780](3780/previews/pattern_12.png) \| ![pattern_13-3780](3780/previews/pattern_13.png) \| ![pattern_14-3780](3780/previews/pattern_14.png) \| ![pattern_15-3780](3780/previews/pattern_15.png) \| ![pattern_16-3780](3780/previews/pattern_16.png) \| ![bikini-3780](3780/previews/bikini.png) \| [<NSFW, click to see>](3780/previews/bondage.png) \| ![free-3780](3780/previews/free.png) \| ![maid-3780](3780/previews/maid.png) \| ![miko-3780](3780/previews/miko.png) \| [<NSFW, click to see>](3780/previews/nude.png) \| [<NSFW, click to see>](3780/previews/nude2.png) \| ![suit-3780](3780/previews/suit.png) \| ![yukata-3780](3780/previews/yukata.png) \| \| 3240 \| 0.961 \| [Download](3240/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-3240](3240/previews/pattern_1.png) \| ![pattern_2-3240](3240/previews/pattern_2.png) \| ![pattern_3-3240](3240/previews/pattern_3.png) \| ![pattern_4-3240](3240/previews/pattern_4.png) \| ![pattern_5-3240](3240/previews/pattern_5.png) \| ![pattern_6-3240](3240/previews/pattern_6.png) \| ![pattern_7-3240](3240/previews/pattern_7.png) \| ![pattern_8-3240](3240/previews/pattern_8.png) \| ![pattern_9-3240](3240/previews/pattern_9.png) \| ![pattern_10-3240](3240/previews/pattern_10.png) \| ![pattern_11-3240](3240/previews/pattern_11.png) \| ![pattern_12-3240](3240/previews/pattern_12.png) \| ![pattern_13-3240](3240/previews/pattern_13.png) \| ![pattern_14-3240](3240/previews/pattern_14.png) \| ![pattern_15-3240](3240/previews/pattern_15.png) \| ![pattern_16-3240](3240/previews/pattern_16.png) \| ![bikini-3240](3240/previews/bikini.png) \| [<NSFW, click to see>](3240/previews/bondage.png) \| ![free-3240](3240/previews/free.png) \| ![maid-3240](3240/previews/maid.png) \| ![miko-3240](3240/previews/miko.png) \| [<NSFW, click to see>](3240/previews/nude.png) \| [<NSFW, click to see>](3240/previews/nude2.png) \| ![suit-3240](3240/previews/suit.png) \| ![yukata-3240](3240/previews/yukata.png) \| \| 2700 \| 0.958 \| [Download](2700/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-2700](2700/previews/pattern_1.png) \| ![pattern_2-2700](2700/previews/pattern_2.png) \| ![pattern_3-2700](2700/previews/pattern_3.png) \| ![pattern_4-2700](2700/previews/pattern_4.png) \| ![pattern_5-2700](2700/previews/pattern_5.png) \| ![pattern_6-2700](2700/previews/pattern_6.png) \| ![pattern_7-2700](2700/previews/pattern_7.png) \| ![pattern_8-2700](2700/previews/pattern_8.png) \| ![pattern_9-2700](2700/previews/pattern_9.png) \| ![pattern_10-2700](2700/previews/pattern_10.png) \| ![pattern_11-2700](2700/previews/pattern_11.png) \| ![pattern_12-2700](2700/previews/pattern_12.png) \| ![pattern_13-2700](2700/previews/pattern_13.png) \| ![pattern_14-2700](2700/previews/pattern_14.png) \| ![pattern_15-2700](2700/previews/pattern_15.png) \| ![pattern_16-2700](2700/previews/pattern_16.png) \| ![bikini-2700](2700/previews/bikini.png) \| [<NSFW, click to see>](2700/previews/bondage.png) \| ![free-2700](2700/previews/free.png) \| ![maid-2700](2700/previews/maid.png) \| ![miko-2700](2700/previews/miko.png) \| [<NSFW, click to see>](2700/previews/nude.png) \| [<NSFW, click to see>](2700/previews/nude2.png) \| ![suit-2700](2700/previews/suit.png) \| ![yukata-2700](2700/previews/yukata.png) \| \| 2160 \| 0.968 \| [Download](2160/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-2160](2160/previews/pattern_1.png) \| ![pattern_2-2160](2160/previews/pattern_2.png) \| ![pattern_3-2160](2160/previews/pattern_3.png) \| ![pattern_4-2160](2160/previews/pattern_4.png) \| ![pattern_5-2160](2160/previews/pattern_5.png) \| ![pattern_6-2160](2160/previews/pattern_6.png) \| ![pattern_7-2160](2160/previews/pattern_7.png) \| ![pattern_8-2160](2160/previews/pattern_8.png) \| ![pattern_9-2160](2160/previews/pattern_9.png) \| ![pattern_10-2160](2160/previews/pattern_10.png) \| ![pattern_11-2160](2160/previews/pattern_11.png) \| ![pattern_12-2160](2160/previews/pattern_12.png) \| ![pattern_13-2160](2160/previews/pattern_13.png) \| ![pattern_14-2160](2160/previews/pattern_14.png) \| ![pattern_15-2160](2160/previews/pattern_15.png) \| ![pattern_16-2160](2160/previews/pattern_16.png) \| ![bikini-2160](2160/previews/bikini.png) \| [<NSFW, click to see>](2160/previews/bondage.png) \| ![free-2160](2160/previews/free.png) \| ![maid-2160](2160/previews/maid.png) \| ![miko-2160](2160/previews/miko.png) \| [<NSFW, click to see>](2160/previews/nude.png) \| [<NSFW, click to see>](2160/previews/nude2.png) \| ![suit-2160](2160/previews/suit.png) \| ![yukata-2160](2160/previews/yukata.png) \| \| 1620 \| 0.972 \| [Download](1620/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-1620](1620/previews/pattern_1.png) \| ![pattern_2-1620](1620/previews/pattern_2.png) \| ![pattern_3-1620](1620/previews/pattern_3.png) \| ![pattern_4-1620](1620/previews/pattern_4.png) \| ![pattern_5-1620](1620/previews/pattern_5.png) \| ![pattern_6-1620](1620/previews/pattern_6.png) \| ![pattern_7-1620](1620/previews/pattern_7.png) \| ![pattern_8-1620](1620/previews/pattern_8.png) \| ![pattern_9-1620](1620/previews/pattern_9.png) \| ![pattern_10-1620](1620/previews/pattern_10.png) \| ![pattern_11-1620](1620/previews/pattern_11.png) \| ![pattern_12-1620](1620/previews/pattern_12.png) \| ![pattern_13-1620](1620/previews/pattern_13.png) \| ![pattern_14-1620](1620/previews/pattern_14.png) \| ![pattern_15-1620](1620/previews/pattern_15.png) \| ![pattern_16-1620](1620/previews/pattern_16.png) \| ![bikini-1620](1620/previews/bikini.png) \| [<NSFW, click to see>](1620/previews/bondage.png) \| ![free-1620](1620/previews/free.png) \| ![maid-1620](1620/previews/maid.png) \| ![miko-1620](1620/previews/miko.png) \| [<NSFW, click to see>](1620/previews/nude.png) \| [<NSFW, click to see>](1620/previews/nude2.png) \| ![suit-1620](1620/previews/suit.png) \| ![yukata-1620](1620/previews/yukata.png) \| \| 1080 \| 0.959 \| [Download](1080/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-1080](1080/previews/pattern_1.png) \| ![pattern_2-1080](1080/previews/pattern_2.png) \| ![pattern_3-1080](1080/previews/pattern_3.png) \| ![pattern_4-1080](1080/previews/pattern_4.png) \| ![pattern_5-1080](1080/previews/pattern_5.png) \| ![pattern_6-1080](1080/previews/pattern_6.png) \| ![pattern_7-1080](1080/previews/pattern_7.png) \| ![pattern_8-1080](1080/previews/pattern_8.png) \| ![pattern_9-1080](1080/previews/pattern_9.png) \| ![pattern_10-1080](1080/previews/pattern_10.png) \| ![pattern_11-1080](1080/previews/pattern_11.png) \| ![pattern_12-1080](1080/previews/pattern_12.png) \| ![pattern_13-1080](1080/previews/pattern_13.png) \| ![pattern_14-1080](1080/previews/pattern_14.png) \| ![pattern_15-1080](1080/previews/pattern_15.png) \| ![pattern_16-1080](1080/previews/pattern_16.png) \| ![bikini-1080](1080/previews/bikini.png) \| [<NSFW, click to see>](1080/previews/bondage.png) \| ![free-1080](1080/previews/free.png) \| ![maid-1080](1080/previews/maid.png) \| ![miko-1080](1080/previews/miko.png) \| [<NSFW, click to see>](1080/previews/nude.png) \| [<NSFW, click to see>](1080/previews/nude2.png) \| ![suit-1080](1080/previews/suit.png) \| ![yukata-1080](1080/previews/yukata.png) \| \| 540 \| 0.929 \| [Download](540/hayasaka_mirei_idolmastercinderellagirls.zip) \| ![pattern_1-540](540/previews/pattern_1.png) \| ![pattern_2-540](540/previews/pattern_2.png) \| ![pattern_3-540](540/previews/pattern_3.png) \| ![pattern_4-540](540/previews/pattern_4.png) \| ![pattern_5-540](540/previews/pattern_5.png) \| ![pattern_6-540](540/previews/pattern_6.png) \| ![pattern_7-540](540/previews/pattern_7.png) \| ![pattern_8-540](540/previews/pattern_8.png) \| ![pattern_9-540](540/previews/pattern_9.png) \| ![pattern_10-540](540/previews/pattern_10.png) \| ![pattern_11-540](540/previews/pattern_11.png) \| ![pattern_12-540](540/previews/pattern_12.png) \| ![pattern_13-540](540/previews/pattern_13.png) \| ![pattern_14-540](540/previews/pattern_14.png) \| ![pattern_15-540](540/previews/pattern_15.png) \| ![pattern_16-540](540/previews/pattern_16.png) \| ![bikini-540](540/previews/bikini.png) \| [<NSFW, click to see>](540/previews/bondage.png) \| ![free-540](540/previews/free.png) \| ![maid-540](540/previews/maid.png) \| ![miko-540](540/previews/miko.png) \| [<NSFW, click to see>](540/previews/nude.png) \| [<NSFW, click to see>](540/previews/nude2.png) \| ![suit-540](540/previews/suit.png) \| ![yukata-540](540/previews/yukata.png) \|
EldritchAdam/LaxpeintXL	EldritchAdam	2023-09-18T03:10:49Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-09-04T19:16:06Z	--- license: openrail --- <div><p><strong><span style="color:rgb(250, 82, 82)">LaxpeintXL - tentatively final version for SDXL 1.0</span></strong></p> <p>This model is a companion to <a target="_blank" rel="ugc" href="https://huggingface.co/EldritchAdam/ClassipeintXL">ClassipeintXL</a>. Although I see ClassipeintXL as really crucial to SDXL (and how I use it) LaxpeintXL is not so obviously necessary. You can get much of this style with the right combination of artist names and aesthetic terms. So why use a LoRA?</p> <p>As much as SDXL is a huge leap forward from SD2, it shares a failing - albeit to a much lesser extent - that keeping an aesthetic consistent is very difficult. The same terms and artist names will not have the same effect for a portrait as for a landscape or a sci-fi scene etc.</p> <p>This LoRA helps you to more consistently get that slick digital paint style in every image. Prompt for whatever you want, it's going to be beautiful.</p> <p><strong><em><span style="color:rgb(190, 75, 219)">Recommended settings for use:</span></em></strong></p><p><a target="_blank" rel="ugc" href="https://pastebin.com/tXKwTkxC"><strong><em><span style="color:rgb(76, 110, 245)">You can go here (pastebin) to download a ComfyUI workflow</span></em></strong></a><span style="color:rgb(34, 139, 230)"> like what I used, but without custom nodes that are embedded in my image uploads on CivitAI.</span></p> <ul> <li> <p>Start with a full 1.0 LoRA strength and adjust down to 0.7 or 0.8 for a subtler painterly effect. You can adjust upward (to 1.2 or maybe a little more) to maximize the painterly appearance, but it can start to introduce some quirks</p> </li> <li> <p>Use the LoRA with your preferred SDXL model with no refiner. I have so far just stuck with base SDXL1.0 but other finetunes work great as well.</p> </li> <li> <p>I recommend the DPM samplers, but use your favorite. Some may produce softer painting styles that don't suit my taste as much but whatever you prefer is great.</p> </li> <li> <p>Don't do anything special for your prompt - just describe what you want to see. You don't really need to use any keywords unless some subject matter seems to override the LoRA's style, then you can bring it back in line by using the terms "digital painting of..." and "by LaxpeintXL".</p> </li> </ul> </div> <div style="max-width:500px"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/0B4gg9e6HNzYI-2dJzIZH.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/gH9bA1TDD2S_bJzheUXr_.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/cu0EyW4eOqr9iVhTN2Cgc.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/o0El5-8ms0J-Ae1gqNi71.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/CbnMKPkqAXM4st88RqXmj.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/mCmmJXYUmD8QamftYjWuQ.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/z4DXgHzHjKbh1mkfW7ur_.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/YdvSPWp38oa-JZgEqnEfp.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/zR1huUXvEl7b6kFdbuxRg.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/jiFLLFahcoE72BcjFKuws.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/8JB6sAgRnaHJ5jsgTpHki.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/LJQJw0V1E3NCdVEMUwgW7.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/LyZL9NLV2mSxQtQae4trO.png"> </div>
GAS17/fgdpersn	GAS17	2023-09-18T03:07:47Z	1	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:GAS17/fgdperson", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-18T02:33:17Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: fgd person tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: false datasets: - GAS17/fgdperson --- # LoRA DreamBooth - GAS17/fgdpersn These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on the concept prompt: `fgd person` Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```python import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) # This is where you load your trained weights pipe.load_lora_weights('GAS17/fgdpersn') pipe.to("cuda") prompt = "A majestic fgd person jumping from a big stone at night" image = pipe(prompt=prompt, num_inference_steps=50).images[0] ```
ys7yoo/sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep5_ckpt	ys7yoo	2023-09-18T02:54:33Z	106	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "base_model:finetune:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T02:25:10Z	--- base_model: ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3 tags: - generated_from_trainer datasets: - klue model-index: - name: sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep5_ckpt 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. --> # sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep5_ckpt This model is a fine-tuned version of [ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3](https://huggingface.co/ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3191 - Mse: 0.3191 - Mae: 0.4161 - R2: 0.8539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| Mae \| R2 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| 1.0641 \| 1.0 \| 183 \| 0.5074 \| 0.5074 \| 0.5341 \| 0.7676 \| \| 0.1359 \| 2.0 \| 366 \| 0.3199 \| 0.3199 \| 0.4232 \| 0.8535 \| \| 0.0958 \| 3.0 \| 549 \| 0.3589 \| 0.3589 \| 0.4349 \| 0.8356 \| \| 0.0748 \| 4.0 \| 732 \| 0.3385 \| 0.3385 \| 0.4284 \| 0.8450 \| \| 0.0617 \| 5.0 \| 915 \| 0.3191 \| 0.3191 \| 0.4161 \| 0.8539 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3
AyanKumarBhunia/textual_inversion_cat	AyanKumarBhunia	2023-09-18T02:49:48Z	30	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-18T02:21:58Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - AyanKumarBhunia/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
huyen89/taxi-v3	huyen89	2023-09-18T02:33:55Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-23T01:57:15Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 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="huyen89/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"]) ```
hegelty/KcBERT-Base-finetuned-hate	hegelty	2023-09-18T02:30:55Z	113	1	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "ko", "license:bsd", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-07T04:23:36Z	--- license: bsd language: - ko library_name: transformers --- # 혐오표현 분류 tag 0: 혐오 tag 1: 일반 # 소스코드 https://github.com/hegelty/hate-classifier # 데이터셋 https://github.com/smilegate-ai/korean_unsmile_dataset
ShivamMangale/XLM-Roberta-base-finetuned-squad-syn-first	ShivamMangale	2023-09-18T02:21:07Z	20	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-18T01:29:23Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-finetuned-squad-syn-first 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-squad-syn-first This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
wu981526092/Sentence-Level-Stereotype-Detector	wu981526092	2023-09-18T01:49:58Z	15,593	4	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "en", "dataset:stereoset", "dataset:crows_pairs", "dataset:wu981526092/MGSD", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-29T16:02:37Z	--- license: mit datasets: - stereoset - crows_pairs - wu981526092/MGSD language: - en metrics: - f1 - recall - precision - accuracy --- # Sentence-Level Stereotype Classifier The Sentence-Level Stereotype Classifier is a transformer-based model developed to detect and classify different types of stereotypes present in the text at the sentence level. It is designed to recognize stereotypical and anti-stereotypical stereotypes towards gender, race, profession, and religion. The model can help in developing applications aimed at mitigating Stereotypical language use and promoting fairness and inclusivity in natural language processing tasks. ## Model Architecture The model is built using the pre-trained Distilbert model. It is fine-tuned on MGS Dataset for the task of sentence-level stereotype classification. ## Classes The model identifies nine classes, including: 0. unrelated: The token does not indicate any stereotype. 1. stereotype_gender: The token indicates a gender stereotype. 2. anti-stereotype_gender: The token indicates an anti-gender stereotype. 3. stereotype_race: The token indicates a racial stereotype. 4. anti-stereotype_race: The token indicates an anti-racial stereotype. 5. stereotype_profession: The token indicates a professional stereotype. 6. anti-stereotype_profession: The token indicates an anti-professional stereotype. 7. stereotype_religion: The token indicates a religious stereotype. 8. anti-stereotype_religion: The token indicates an anti-religious stereotype. ## Usage The model can be used as a part of the Hugging Face's pipeline for Text Classification. ```python from transformers import pipeline nlp = pipeline("text-classification", model="wu981526092/Sentence-Level-Stereotype-Detector", tokenizer="wu981526092/Sentence-Level-Stereotype-Detector") result = nlp("Text containing potential stereotype...") print(result) ```
wu981526092/Token-Level-Stereotype-Detector	wu981526092	2023-09-18T01:48:45Z	110	2	transformers	[ "transformers", "pytorch", "distilbert", "token-classification", "en", "dataset:stereoset", "dataset:crows_pairs", "dataset:wu981526092/MGSD", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-24T10:21:27Z	--- license: mit datasets: - stereoset - crows_pairs - wu981526092/MGSD language: - en metrics: - f1 - recall - precision - accuracy --- # Token-Level Stereotype Classifier The Token-Level Stereotype Classifier is a transformer-based model developed to detect and classify different types of stereotypes present in the text at the token level. It is designed to recognize stereotypical and anti-stereotypical stereotypes towards gender, race, profession, and religion. The model can help in developing applications aimed at mitigating stereotypical language use and promoting fairness and inclusivity in natural language processing tasks. ## Model Architecture The model is built using the pretrained Distilbert model. It is fine-tuned on MGS Dataset for the task of token-level classification. ## Classes The model identifies nine classes, including: 1. unrelated: The token does not indicate any stereotype. 2. stereotype_gender: The token indicates a gender stereotype. 3. anti-stereotype_gender: The token indicates an anti-gender stereotype. 4. stereotype_race: The token indicates a racial stereotype. 5. anti-stereotype_race: The token indicates an anti-racial stereotype. 6. stereotype_profession: The token indicates a professional stereotype. 7. anti-stereotype_profession: The token indicates an anti-professional stereotype. 8. stereotype_religion: The token indicates a religious stereotype. 9. anti-stereotype_religion: The token indicates an anti-religious stereotype. ## Usage The model can be used as a part of the Hugging Face's pipeline for Named Entity Recognition (NER). ```python from transformers import pipeline nlp = pipeline("ner", model="wu981526092/Token-Level-Stereotype-Detector", tokenizer="wu981526092/Token-Level-Stereotype-Detector") result = nlp("Text containing potential stereotype...") print(result) ```
kiranahp/indobert_qa_skripsi_big	kiranahp	2023-09-18T01:32:54Z	122	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-18T01:20:42Z	--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: indobert_qa_skripsi_big 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. --> # indobert_qa_skripsi_big This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8011 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.6607 \| 1.0 \| 4101 \| 1.7878 \| \| 1.3687 \| 2.0 \| 8202 \| 1.7563 \| \| 1.1822 \| 3.0 \| 12303 \| 1.8011 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
KETI-AIR-Downstream/long-ke-t5-base-summarization_e10	KETI-AIR-Downstream	2023-09-18T01:28:33Z	119	0	transformers	[ "transformers", "pytorch", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "dataset:jsonl_dataset_sum.py", "base_model:KETI-AIR/long-ke-t5-base", "base_model:finetune:KETI-AIR/long-ke-t5-base", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-05T04:24:59Z	--- tags: - generated_from_trainer datasets: - jsonl_dataset_sum.py metrics: - rouge widget: - text: 'summarization-num_lines-1: 현대자동차는 18일(현지 시간) 이탈리아 레이크 코모에서 개최된 ''현대 리유니온'' 행사에서 ''포니 쿠페 콘셉트'' 복원 모델을 세계에 첫 공개했습니다. 이 프로젝트는 현대차의 창업자인 정주영 선대 회장의 수출보국(輸出報國) 정신과 포니 쿠페를 통한 글로벌 브랜드 정립에 대한 끊임없는 열정과 도전 정신을 재조명하기 위한 것입니다. 현대차에 따르면, 이번 현대 리유니온 행사는 회사의 역사를 다시 돌아보며 변하지 않는 미래 지향적인 비전과 방향성을 공유하는 브랜드 유산 행사입니다.' example_title: sample 1 base_model: KETI-AIR/long-ke-t5-base model-index: - name: summarization_all results: - task: type: summarization name: Summarization dataset: name: jsonl_dataset_sum.py type: jsonl_dataset_sum.py config: 'null' split: None metrics: - type: rouge value: 21.9857 name: Rouge1 --- <!-- 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. --> # summarization_all This model is a fine-tuned version of [KETI-AIR/long-ke-t5-base](https://huggingface.co/KETI-AIR/long-ke-t5-base) on the jsonl_dataset_sum.py dataset. It achieves the following results on the evaluation set: - Loss: 1.1442 - Rouge1: 21.9857 - Rouge2: 10.2876 - Rougel: 21.4026 - Rougelsum: 21.4278 - Gen Len: 86.2560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-------:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.2503 \| 1.0 \| 184670 \| 1.2439 \| 20.2525 \| 9.1467 \| 19.7454 \| 19.771 \| 87.1766 \| \| 1.1629 \| 2.0 \| 369340 \| 1.1773 \| 21.0068 \| 9.6691 \| 20.4565 \| 20.4888 \| 89.6074 \| \| 1.1087 \| 3.0 \| 554010 \| 1.1431 \| 21.0216 \| 9.6545 \| 20.489 \| 20.5108 \| 85.5895 \| \| 1.056 \| 4.0 \| 738680 \| 1.1247 \| 21.6776 \| 10.1424 \| 21.09 \| 21.1168 \| 89.6576 \| \| 1.0199 \| 5.0 \| 923350 \| 1.1179 \| 21.6563 \| 10.0965 \| 21.0814 \| 21.1056 \| 89.2454 \| \| 0.9652 \| 6.0 \| 1108020 \| 1.1122 \| 21.6209 \| 10.0725 \| 21.0623 \| 21.0864 \| 86.7079 \| \| 0.92 \| 7.0 \| 1292690 \| 1.1136 \| 21.9396 \| 10.2734 \| 21.3465 \| 21.3745 \| 86.5547 \| \| 0.8804 \| 8.0 \| 1477360 \| 1.1228 \| 21.8457 \| 10.1858 \| 21.2552 \| 21.278 \| 87.6413 \| \| 0.8447 \| 9.0 \| 1662030 \| 1.1327 \| 21.92 \| 10.2635 \| 21.3415 \| 21.3633 \| 86.4453 \| \| 0.7678 \| 10.0 \| 1846700 \| 1.1442 \| 21.9857 \| 10.2876 \| 21.4026 \| 21.4278 \| 86.2560 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.0 - Datasets 2.8.0 - Tokenizers 0.13.2
KETI-AIR-Downstream/long-ke-t5-base-translation-aihub-en2ko	KETI-AIR-Downstream	2023-09-18T01:27:39Z	159	3	transformers	[ "transformers", "pytorch", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "translation", "en", "ko", "base_model:KETI-AIR/long-ke-t5-base", "base_model:finetune:KETI-AIR/long-ke-t5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-04-28T14:19:27Z	--- language: - en - ko license: apache-2.0 tags: - generated_from_trainer datasets: - KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation metrics: - bleu pipeline_tag: translation widget: - text: 'translate_en2ko: The Seoul Metropolitan Government said Wednesday that it would develop an AI-based congestion monitoring system to provide better information to passengers about crowd density at each subway station.' example_title: Sample 1 - text: 'translate_en2ko: According to Seoul Metro, the operator of the subway service in Seoul, the new service will help analyze the real-time flow of passengers and crowd levels in subway compartments, improving operational efficiency.' example_title: Sample 2 base_model: KETI-AIR/long-ke-t5-base model-index: - name: en2ko results: - task: type: translation name: Translation dataset: name: KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation koen,none,none,none,none type: KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation args: koen,none,none,none,none metrics: - type: bleu value: 42.463 name: Bleu --- <!-- 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. --> # en2ko This model is a fine-tuned version of [KETI-AIR/long-ke-t5-base](https://huggingface.co/KETI-AIR/long-ke-t5-base) on the KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation koen,none,none,none,none dataset. It achieves the following results on the evaluation set: - Loss: 0.6000 - Bleu: 42.463 - Gen Len: 30.6512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:-------:\| \| 0.6989 \| 1.0 \| 93762 \| 0.6666 \| 20.3697 \| 18.1258 \| \| 0.6143 \| 2.0 \| 187524 \| 0.6181 \| 21.2903 \| 18.1428 \| \| 0.5544 \| 3.0 \| 281286 \| 0.6000 \| 21.9763 \| 18.1424 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.0 - Datasets 2.8.0 - Tokenizers 0.13.2
KETI-AIR/ke-t5-large	KETI-AIR	2023-09-18T01:24:55Z	102	8	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "en", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- license: apache-2.0 language: [en, ko] tags: - t5 eos_token: "</s>" widget: - text: 아버지가 방에 들어가신다.</s> --- # ke-t5 base Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details. ## How to use ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("KETI-AIR/ke-t5-large") tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-large") ``` ## BibTeX entry and citation info ```bibtex @inproceedings{kim-etal-2021-model-cross, title = "A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems", author = "Kim, San and Jang, Jin Yea and Jung, Minyoung and Shin, Saim", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.33", doi = "10.18653/v1/2021.findings-emnlp.33", pages = "352--365", abstract = "Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.", } ```
KETI-AIR/ke-t5-base-ko	KETI-AIR	2023-09-18T01:24:34Z	378	7	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "ko", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- language: ko license: apache-2.0 tags: - t5 eos_token: </s> widget: - text: 아버지가 방에 들어가신다.</s> --- # Model Card for ke-t5-base-ko # Model Details ## Model Description - Developed by: Korea Electronics Technology Institute Artificial Intelligence Research Center - Shared by [Optional]: More information needed - Model type: Text2Text Generation - Language(s) (NLP): More information needed - License: More information needed - Related Models: - Parent Model: T5 - Resources for more information: - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints) - [KE-T5 Github Repo](https://github.com/AIRC-KETI/ke-t5) - [Paper](https://aclanthology.org/2021.findings-emnlp.33/) - [Associated Paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) - [Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) # Uses ## Direct Use This model can be used for the task of Text2Text Generation ## Downstream Use [Optional] More information needed ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5. The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.). See the [t5-base model card](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) for further information. ## Training Procedure ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: More information needed - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed # Citation BibTeX: ```bibtex @inproceedings{kim-etal-2021-model-cross, title = "A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems", author = "Kim, San and Jang, Jin Yea and Jung, Minyoung and Shin, Saim", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.33", doi = "10.18653/v1/2021.findings-emnlp.33", pages = "352--365", abstract = "Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.", } ``` ```bibtex @article{2020t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} } ``` APA: ``` - Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. ``` # Glossary [optional] More information needed # More Information [optional] More information needed # Model Card Authors [optional] Korea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-base-ko") model = AutoModelForSeq2SeqLM.from_pretrained("KETI-AIR/ke-t5-base-ko") ``` </details>
Navu45/neon_sd_model	Navu45	2023-09-18T01:14:45Z	0	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-18T00:02:58Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - Navu45/neon_sd_model These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Navu45/neon_dreambooth dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3_ckpt	ys7yoo	2023-09-18T01:08:41Z	109	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-18T00:46:19Z	--- base_model: klue/roberta-large tags: - generated_from_trainer datasets: - klue metrics: - accuracy - f1 model-index: - name: nli_roberta-large_lr1e-05_wd1e-03_ep3_ckpt results: - task: name: Text Classification type: text-classification dataset: name: klue type: klue config: nli split: validation args: nli metrics: - name: Accuracy type: accuracy value: 0.9026666666666666 - name: F1 type: f1 value: 0.9025716877431428 --- <!-- 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. --> # nli_roberta-large_lr1e-05_wd1e-03_ep3_ckpt This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3425 - Accuracy: 0.9027 - F1: 0.9026 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5725 \| 1.0 \| 391 \| 0.3381 \| 0.8813 \| 0.8811 \| \| 0.2182 \| 2.0 \| 782 \| 0.3055 \| 0.898 \| 0.8979 \| \| 0.112 \| 3.0 \| 1173 \| 0.3425 \| 0.9027 \| 0.9026 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3
natsusakiyomi/momijimix-xl	natsusakiyomi	2023-09-18T00:51:44Z	0	2	null	[ "license:openrail++", "region:us" ]	null	2023-09-17T21:06:41Z	--- license: openrail++ --- License [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) 〇Use the model without crediting the creator<br> 〇Sell images they generate<br> 〇Run on services that generate images for money<br> 〇Share merges using this model<br> ×Sell this model or merges using this model<br> ×Have different permissions when sharing merges<br>
TrevorJS/mtg-phi-1_5-dpo-qlora	TrevorJS	2023-09-18T00:31:30Z	0	0	null	[ "generated_from_trainer", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "region:us" ]	null	2023-09-18T00:20:06Z	--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Rewards/chosen: -7.5874 - Rewards/rejected: -24.0497 - Rewards/accuracies: 1.0 - Rewards/margins: 16.4623 - Logps/rejected: -274.3435 - Logps/chosen: -143.2090 - Logits/rejected: -1.8100 - Logits/chosen: -1.4786 ## 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: 4 - eval_batch_size: 1 - 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: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.0417 \| 0.07 \| 100 \| 0.0418 \| -0.3892 \| -8.0118 \| 0.9792 \| 7.6226 \| -113.9640 \| -71.2264 \| 1.8258 \| 1.7898 \| \| 0.0221 \| 0.15 \| 200 \| 0.0303 \| -2.5657 \| -10.9212 \| 0.9896 \| 8.3555 \| -143.0585 \| -92.9920 \| 1.9704 \| 2.1047 \| \| 0.0107 \| 0.22 \| 300 \| 0.0131 \| -1.7388 \| -11.6047 \| 0.9965 \| 9.8659 \| -149.8935 \| -84.7232 \| 1.0731 \| 0.9750 \| \| 0.0204 \| 0.29 \| 400 \| 0.0108 \| -2.0131 \| -11.9647 \| 0.9965 \| 9.9516 \| -153.4932 \| -87.4658 \| 1.3610 \| 1.6740 \| \| 0.0067 \| 0.36 \| 500 \| 0.0080 \| -5.9488 \| -19.6561 \| 0.9974 \| 13.7073 \| -230.4076 \| -126.8228 \| -0.4464 \| -0.2114 \| \| 0.0 \| 0.44 \| 600 \| 0.0047 \| -5.6456 \| -20.2381 \| 0.9983 \| 14.5924 \| -236.2268 \| -123.7909 \| -0.4142 \| -0.0244 \| \| 0.0003 \| 0.51 \| 700 \| 0.0018 \| -7.2250 \| -21.3351 \| 0.9991 \| 14.1101 \| -247.1974 \| -139.5853 \| -0.3510 \| -0.0203 \| \| 0.0005 \| 0.58 \| 800 \| 0.0008 \| -7.2263 \| -21.2475 \| 0.9991 \| 14.0211 \| -246.3209 \| -139.5981 \| -0.8673 \| -0.7010 \| \| 0.0 \| 0.66 \| 900 \| 0.0009 \| -10.2371 \| -26.0402 \| 0.9991 \| 15.8031 \| -294.2486 \| -169.7062 \| -1.9784 \| -1.7799 \| \| 0.0 \| 0.73 \| 1000 \| 0.0008 \| -5.9544 \| -22.0767 \| 0.9991 \| 16.1223 \| -254.6137 \| -126.8789 \| -1.0623 \| -0.6039 \| \| 0.0 \| 0.8 \| 1100 \| 0.0007 \| -7.3374 \| -23.8700 \| 0.9991 \| 16.5327 \| -272.5467 \| -140.7083 \| -1.5517 \| -1.1710 \| \| 0.0 \| 0.87 \| 1200 \| 0.0007 \| -7.6398 \| -24.1605 \| 0.9991 \| 16.5207 \| -275.4509 \| -143.7327 \| -1.8124 \| -1.4901 \| \| 0.0 \| 0.95 \| 1300 \| 0.0001 \| -7.5920 \| -24.0476 \| 1.0 \| 16.4556 \| -274.3220 \| -143.2550 \| -1.8115 \| -1.4816 \| \| 0.0001 \| 1.02 \| 1400 \| 0.0001 \| -7.5872 \| -24.0480 \| 1.0 \| 16.4608 \| -274.3262 \| -143.2065 \| -1.8102 \| -1.4791 \| \| 0.0 \| 1.09 \| 1500 \| 0.0001 \| -7.5874 \| -24.0497 \| 1.0 \| 16.4623 \| -274.3435 \| -143.2090 \| -1.8100 \| -1.4786 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Evan-Lin/yelp-attractive-keyword-1	Evan-Lin	2023-09-18T00:07:04Z	47	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-09-17T10:03:06Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpypedqoes/Evan-Lin/yelp-attractive-keyword-1") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpypedqoes/Evan-Lin/yelp-attractive-keyword-1") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpypedqoes/Evan-Lin/yelp-attractive-keyword-1") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

prince99/results1

prince99

2023-09-18T10:40:17Z

0

null

[ "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:finetune:meta-llama/Llama-2-13b-chat-hf", "region:us" ]

null

2023-09-18T10:40:13Z

--- base_model: meta-llama/Llama-2-13b-chat-hf tags: - generated_from_trainer model-index: - name: results1 results: [] ---  # results1 This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5723 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

dvrkdvys/Ted_Cruz_G_157500

dvrkdvys

2023-09-18T10:13:47Z

0

null

[ "natural language generation", "voice conversion", "adversarial learning", "license:openrail", "region:us" ]

null

2023-09-18T10:07:47Z

--- license: openrail tags: - natural language generation - voice conversion - adversarial learning ---

nxa277/falcon-7b_medichat_finetuned_final

nxa277

2023-09-18T10:12:35Z

0

peft

[ "peft", "region:us" ]

null

2023-08-20T17:08:19Z

--- library_name: peft --- # Fine-tuned Falcon-7B Model for Medical Diagnosis ## Model Details ### Model Description: This model is a fine-tuned version of the Falcon-7B model. This model was fine-tuned on Gretel.ai's "Symtoms to Diagnosis" dataset, found at the following link: https://huggingface.co/datasets/gretelai/symptom_to_diagnosis, in order to provide preliminary diagnoses based on the symptom descriptions it is prompted with. ### Baseline Model: For more details about the baseline Falcon-7B model, please see the following links: 1. https://huggingface.co/tiiuae/falcon-7b 2. https://huggingface.co/blog/falcon ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

thomas0104/whisper-large-v2-nan-tw-only-char

thomas0104

2023-09-18T09:58:56Z

27

1

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "zh", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-large-v2", "base_model:finetune:openai/whisper-large-v2", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-03-01T08:08:34Z

--- language: - zh license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer base_model: openai/whisper-large-v2 model-index: - name: Whisper large-v2 nan-tw only char results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: mozilla-foundation/common_voice_11_0 nan-tw type: mozilla-foundation/common_voice_11_0 config: nan-tw split: test args: nan-tw metrics: - type: wer value: 45.37404580152672 name: Wer ---  # Whisper large-v2 nan-tw only char This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 nan-tw dataset. It achieves the following results on the evaluation set: - Loss: 1.0351 - Wer: 45.3740 - Cer: 45.4573 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.6011 | 1.04 | 1000 | 1.1100 | 55.0229 | 55.2068 | | 0.1773 | 2.08 | 2000 | 1.2055 | 58.6565 | 58.7685 | | 0.015 | 3.13 | 3000 | 1.0932 | 48.6412 | 48.8077 | | 0.0131 | 5.01 | 4000 | 1.0531 | 45.7099 | 45.8497 | | 0.0001 | 6.05 | 5000 | 1.0351 | 45.3740 | 45.4573 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

anggtpd/emotion_recognition

anggtpd

2023-09-18T09:58:50Z

9

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-14T12:40:05Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion_recognition results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.45625 ---  # emotion_recognition This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6139 - Accuracy: 0.4562 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 5 | 1.9416 | 0.3438 | | 1.8445 | 2.0 | 10 | 1.8517 | 0.3937 | | 1.8445 | 3.0 | 15 | 1.7436 | 0.3875 | | 1.6748 | 4.0 | 20 | 1.6654 | 0.475 | | 1.6748 | 5.0 | 25 | 1.6098 | 0.5062 | | 1.5405 | 6.0 | 30 | 1.5734 | 0.4875 | | 1.5405 | 7.0 | 35 | 1.5446 | 0.4938 | | 1.4603 | 8.0 | 40 | 1.5415 | 0.4938 | | 1.4603 | 9.0 | 45 | 1.5173 | 0.5062 | | 1.4154 | 10.0 | 50 | 1.4983 | 0.5062 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

bardsai/finance-sentiment-fr-base

bardsai

2023-09-18T09:54:48Z

728

5

transformers

[ "transformers", "pytorch", "camembert", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "fr", "dataset:datasets/financial_phrasebank", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T09:53:50Z

--- language: fr tags: - text-classification - financial-sentiment-analysis - sentiment-analysis datasets: - datasets/financial_phrasebank metrics: - f1 - accuracy - precision - recall widget: - text: "Le chiffre d'affaires net a augmenté de 30 % pour atteindre 36 millions d'euros." example_title: "Example 1" - text: "Coup d'envoi du vendredi fou. Liste des promotions en magasin." example_title: "Example 2" - text: "Les actions de CDPROJEKT ont enregistré la plus forte baisse parmi les entreprises cotées au WSE." example_title: "Example 3" --- # Finance Sentiment FR (base) Finance Sentiment FR (base) is a model based on [camembert-base](https://huggingface.co/camembert-base) for analyzing sentiment of French financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu. The model will give you a three labels: positive, negative and neutral. ## How to use You can use this model directly with a pipeline for sentiment-analysis: ```python from transformers import pipeline nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-fr-base") nlp("Le chiffre d'affaires net a augmenté de 30 % pour atteindre 36 millions d'euros.") ``` ```bash [{'label': 'positive', 'score': 0.9987998807375955}] ``` ## Performance | Metric | Value | | --- | ----------- | | f1 macro | 0.963 | | precision macro | 0.959 | | recall macro | 0.967 | | accuracy | 0.971 | | samples per second | 140.8 | (The performance was evaluated on RTX 3090 gpu) ## Changelog - 2023-09-18: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/) Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]

Karsinogenic69/emotion_classification

Karsinogenic69

2023-09-18T09:53:45Z

200

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-18T09:50:26Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion_classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.5 ---  # emotion_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4512 - Accuracy: 0.5 ## 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: - 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 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 1.4449 | 0.4688 | | No log | 2.0 | 80 | 1.4457 | 0.4938 | | No log | 3.0 | 120 | 1.3813 | 0.5563 | | No log | 4.0 | 160 | 1.5903 | 0.4313 | | No log | 5.0 | 200 | 1.4512 | 0.5 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/yusa_kozue_idolmastercinderellagirls

CyberHarem

2023-09-18T09:40:26Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/yusa_kozue_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-18T09:22:35Z

--- license: mit datasets: - CyberHarem/yusa_kozue_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of yusa_kozue_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 7800, you need to download `7800/yusa_kozue_idolmastercinderellagirls.pt` as the embedding and `7800/yusa_kozue_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 7800**, with the score of 0.924. The trigger words are: 1. `yusa_kozue_idolmastercinderellagirls` 2. `blonde_hair, ahoge, blush, green_eyes, twintails, low_twintails, long_hair, open_mouth` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | **7800** | **0.924** | [**Download**](7800/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](7800/previews/pattern_1.png) | [<NSFW, click to see>](7800/previews/pattern_2.png) | ![pattern_3-7800](7800/previews/pattern_3.png) | ![pattern_4-7800](7800/previews/pattern_4.png) | ![pattern_5-7800](7800/previews/pattern_5.png) | ![pattern_6-7800](7800/previews/pattern_6.png) | ![pattern_7-7800](7800/previews/pattern_7.png) | ![pattern_8-7800](7800/previews/pattern_8.png) | ![pattern_9-7800](7800/previews/pattern_9.png) | ![pattern_10-7800](7800/previews/pattern_10.png) | ![pattern_11-7800](7800/previews/pattern_11.png) | [<NSFW, click to see>](7800/previews/pattern_12.png) | [<NSFW, click to see>](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) | ![free-7800](7800/previews/free.png) | ![maid-7800](7800/previews/maid.png) | ![miko-7800](7800/previews/miko.png) | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) | ![suit-7800](7800/previews/suit.png) | ![yukata-7800](7800/previews/yukata.png) | | 7280 | 0.909 | [Download](7280/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](7280/previews/pattern_1.png) | [<NSFW, click to see>](7280/previews/pattern_2.png) | ![pattern_3-7280](7280/previews/pattern_3.png) | ![pattern_4-7280](7280/previews/pattern_4.png) | ![pattern_5-7280](7280/previews/pattern_5.png) | ![pattern_6-7280](7280/previews/pattern_6.png) | ![pattern_7-7280](7280/previews/pattern_7.png) | ![pattern_8-7280](7280/previews/pattern_8.png) | ![pattern_9-7280](7280/previews/pattern_9.png) | ![pattern_10-7280](7280/previews/pattern_10.png) | ![pattern_11-7280](7280/previews/pattern_11.png) | [<NSFW, click to see>](7280/previews/pattern_12.png) | [<NSFW, click to see>](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) | ![free-7280](7280/previews/free.png) | ![maid-7280](7280/previews/maid.png) | ![miko-7280](7280/previews/miko.png) | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) | ![suit-7280](7280/previews/suit.png) | ![yukata-7280](7280/previews/yukata.png) | | 6760 | 0.853 | [Download](6760/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](6760/previews/pattern_1.png) | [<NSFW, click to see>](6760/previews/pattern_2.png) | ![pattern_3-6760](6760/previews/pattern_3.png) | ![pattern_4-6760](6760/previews/pattern_4.png) | ![pattern_5-6760](6760/previews/pattern_5.png) | ![pattern_6-6760](6760/previews/pattern_6.png) | ![pattern_7-6760](6760/previews/pattern_7.png) | ![pattern_8-6760](6760/previews/pattern_8.png) | ![pattern_9-6760](6760/previews/pattern_9.png) | ![pattern_10-6760](6760/previews/pattern_10.png) | ![pattern_11-6760](6760/previews/pattern_11.png) | [<NSFW, click to see>](6760/previews/pattern_12.png) | [<NSFW, click to see>](6760/previews/bikini.png) | [<NSFW, click to see>](6760/previews/bondage.png) | ![free-6760](6760/previews/free.png) | ![maid-6760](6760/previews/maid.png) | ![miko-6760](6760/previews/miko.png) | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) | ![suit-6760](6760/previews/suit.png) | ![yukata-6760](6760/previews/yukata.png) | | 6240 | 0.920 | [Download](6240/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](6240/previews/pattern_1.png) | [<NSFW, click to see>](6240/previews/pattern_2.png) | ![pattern_3-6240](6240/previews/pattern_3.png) | ![pattern_4-6240](6240/previews/pattern_4.png) | ![pattern_5-6240](6240/previews/pattern_5.png) | ![pattern_6-6240](6240/previews/pattern_6.png) | ![pattern_7-6240](6240/previews/pattern_7.png) | ![pattern_8-6240](6240/previews/pattern_8.png) | ![pattern_9-6240](6240/previews/pattern_9.png) | ![pattern_10-6240](6240/previews/pattern_10.png) | ![pattern_11-6240](6240/previews/pattern_11.png) | [<NSFW, click to see>](6240/previews/pattern_12.png) | [<NSFW, click to see>](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | 5720 | 0.862 | [Download](5720/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](5720/previews/pattern_1.png) | [<NSFW, click to see>](5720/previews/pattern_2.png) | ![pattern_3-5720](5720/previews/pattern_3.png) | ![pattern_4-5720](5720/previews/pattern_4.png) | ![pattern_5-5720](5720/previews/pattern_5.png) | ![pattern_6-5720](5720/previews/pattern_6.png) | ![pattern_7-5720](5720/previews/pattern_7.png) | ![pattern_8-5720](5720/previews/pattern_8.png) | ![pattern_9-5720](5720/previews/pattern_9.png) | ![pattern_10-5720](5720/previews/pattern_10.png) | ![pattern_11-5720](5720/previews/pattern_11.png) | [<NSFW, click to see>](5720/previews/pattern_12.png) | [<NSFW, click to see>](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) | ![free-5720](5720/previews/free.png) | ![maid-5720](5720/previews/maid.png) | ![miko-5720](5720/previews/miko.png) | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) | ![suit-5720](5720/previews/suit.png) | ![yukata-5720](5720/previews/yukata.png) | | 5200 | 0.784 | [Download](5200/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](5200/previews/pattern_1.png) | [<NSFW, click to see>](5200/previews/pattern_2.png) | ![pattern_3-5200](5200/previews/pattern_3.png) | ![pattern_4-5200](5200/previews/pattern_4.png) | ![pattern_5-5200](5200/previews/pattern_5.png) | ![pattern_6-5200](5200/previews/pattern_6.png) | ![pattern_7-5200](5200/previews/pattern_7.png) | ![pattern_8-5200](5200/previews/pattern_8.png) | ![pattern_9-5200](5200/previews/pattern_9.png) | ![pattern_10-5200](5200/previews/pattern_10.png) | ![pattern_11-5200](5200/previews/pattern_11.png) | [<NSFW, click to see>](5200/previews/pattern_12.png) | [<NSFW, click to see>](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) | ![free-5200](5200/previews/free.png) | ![maid-5200](5200/previews/maid.png) | ![miko-5200](5200/previews/miko.png) | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) | ![suit-5200](5200/previews/suit.png) | ![yukata-5200](5200/previews/yukata.png) | | 4680 | 0.770 | [Download](4680/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](4680/previews/pattern_1.png) | [<NSFW, click to see>](4680/previews/pattern_2.png) | ![pattern_3-4680](4680/previews/pattern_3.png) | ![pattern_4-4680](4680/previews/pattern_4.png) | ![pattern_5-4680](4680/previews/pattern_5.png) | ![pattern_6-4680](4680/previews/pattern_6.png) | ![pattern_7-4680](4680/previews/pattern_7.png) | ![pattern_8-4680](4680/previews/pattern_8.png) | ![pattern_9-4680](4680/previews/pattern_9.png) | ![pattern_10-4680](4680/previews/pattern_10.png) | ![pattern_11-4680](4680/previews/pattern_11.png) | [<NSFW, click to see>](4680/previews/pattern_12.png) | [<NSFW, click to see>](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) | ![free-4680](4680/previews/free.png) | ![maid-4680](4680/previews/maid.png) | ![miko-4680](4680/previews/miko.png) | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) | ![suit-4680](4680/previews/suit.png) | ![yukata-4680](4680/previews/yukata.png) | | 4160 | 0.749 | [Download](4160/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](4160/previews/pattern_1.png) | [<NSFW, click to see>](4160/previews/pattern_2.png) | ![pattern_3-4160](4160/previews/pattern_3.png) | ![pattern_4-4160](4160/previews/pattern_4.png) | ![pattern_5-4160](4160/previews/pattern_5.png) | ![pattern_6-4160](4160/previews/pattern_6.png) | ![pattern_7-4160](4160/previews/pattern_7.png) | ![pattern_8-4160](4160/previews/pattern_8.png) | ![pattern_9-4160](4160/previews/pattern_9.png) | ![pattern_10-4160](4160/previews/pattern_10.png) | ![pattern_11-4160](4160/previews/pattern_11.png) | [<NSFW, click to see>](4160/previews/pattern_12.png) | [<NSFW, click to see>](4160/previews/bikini.png) | [<NSFW, click to see>](4160/previews/bondage.png) | ![free-4160](4160/previews/free.png) | ![maid-4160](4160/previews/maid.png) | ![miko-4160](4160/previews/miko.png) | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) | ![suit-4160](4160/previews/suit.png) | ![yukata-4160](4160/previews/yukata.png) | | 3640 | 0.700 | [Download](3640/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](3640/previews/pattern_1.png) | [<NSFW, click to see>](3640/previews/pattern_2.png) | ![pattern_3-3640](3640/previews/pattern_3.png) | ![pattern_4-3640](3640/previews/pattern_4.png) | ![pattern_5-3640](3640/previews/pattern_5.png) | ![pattern_6-3640](3640/previews/pattern_6.png) | ![pattern_7-3640](3640/previews/pattern_7.png) | ![pattern_8-3640](3640/previews/pattern_8.png) | ![pattern_9-3640](3640/previews/pattern_9.png) | ![pattern_10-3640](3640/previews/pattern_10.png) | ![pattern_11-3640](3640/previews/pattern_11.png) | [<NSFW, click to see>](3640/previews/pattern_12.png) | [<NSFW, click to see>](3640/previews/bikini.png) | [<NSFW, click to see>](3640/previews/bondage.png) | ![free-3640](3640/previews/free.png) | ![maid-3640](3640/previews/maid.png) | ![miko-3640](3640/previews/miko.png) | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) | ![suit-3640](3640/previews/suit.png) | ![yukata-3640](3640/previews/yukata.png) | | 3120 | 0.802 | [Download](3120/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](3120/previews/pattern_1.png) | [<NSFW, click to see>](3120/previews/pattern_2.png) | ![pattern_3-3120](3120/previews/pattern_3.png) | ![pattern_4-3120](3120/previews/pattern_4.png) | ![pattern_5-3120](3120/previews/pattern_5.png) | ![pattern_6-3120](3120/previews/pattern_6.png) | ![pattern_7-3120](3120/previews/pattern_7.png) | ![pattern_8-3120](3120/previews/pattern_8.png) | ![pattern_9-3120](3120/previews/pattern_9.png) | ![pattern_10-3120](3120/previews/pattern_10.png) | ![pattern_11-3120](3120/previews/pattern_11.png) | [<NSFW, click to see>](3120/previews/pattern_12.png) | [<NSFW, click to see>](3120/previews/bikini.png) | [<NSFW, click to see>](3120/previews/bondage.png) | ![free-3120](3120/previews/free.png) | ![maid-3120](3120/previews/maid.png) | ![miko-3120](3120/previews/miko.png) | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) | ![suit-3120](3120/previews/suit.png) | ![yukata-3120](3120/previews/yukata.png) | | 2600 | 0.677 | [Download](2600/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](2600/previews/pattern_1.png) | [<NSFW, click to see>](2600/previews/pattern_2.png) | ![pattern_3-2600](2600/previews/pattern_3.png) | ![pattern_4-2600](2600/previews/pattern_4.png) | ![pattern_5-2600](2600/previews/pattern_5.png) | ![pattern_6-2600](2600/previews/pattern_6.png) | ![pattern_7-2600](2600/previews/pattern_7.png) | ![pattern_8-2600](2600/previews/pattern_8.png) | ![pattern_9-2600](2600/previews/pattern_9.png) | ![pattern_10-2600](2600/previews/pattern_10.png) | ![pattern_11-2600](2600/previews/pattern_11.png) | [<NSFW, click to see>](2600/previews/pattern_12.png) | [<NSFW, click to see>](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) | ![free-2600](2600/previews/free.png) | ![maid-2600](2600/previews/maid.png) | ![miko-2600](2600/previews/miko.png) | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) | ![suit-2600](2600/previews/suit.png) | ![yukata-2600](2600/previews/yukata.png) | | 2080 | 0.705 | [Download](2080/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](2080/previews/pattern_1.png) | [<NSFW, click to see>](2080/previews/pattern_2.png) | ![pattern_3-2080](2080/previews/pattern_3.png) | ![pattern_4-2080](2080/previews/pattern_4.png) | ![pattern_5-2080](2080/previews/pattern_5.png) | ![pattern_6-2080](2080/previews/pattern_6.png) | ![pattern_7-2080](2080/previews/pattern_7.png) | ![pattern_8-2080](2080/previews/pattern_8.png) | ![pattern_9-2080](2080/previews/pattern_9.png) | ![pattern_10-2080](2080/previews/pattern_10.png) | ![pattern_11-2080](2080/previews/pattern_11.png) | [<NSFW, click to see>](2080/previews/pattern_12.png) | [<NSFW, click to see>](2080/previews/bikini.png) | [<NSFW, click to see>](2080/previews/bondage.png) | ![free-2080](2080/previews/free.png) | ![maid-2080](2080/previews/maid.png) | ![miko-2080](2080/previews/miko.png) | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) | ![suit-2080](2080/previews/suit.png) | ![yukata-2080](2080/previews/yukata.png) | | 1560 | 0.478 | [Download](1560/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](1560/previews/pattern_1.png) | [<NSFW, click to see>](1560/previews/pattern_2.png) | ![pattern_3-1560](1560/previews/pattern_3.png) | ![pattern_4-1560](1560/previews/pattern_4.png) | ![pattern_5-1560](1560/previews/pattern_5.png) | ![pattern_6-1560](1560/previews/pattern_6.png) | ![pattern_7-1560](1560/previews/pattern_7.png) | ![pattern_8-1560](1560/previews/pattern_8.png) | ![pattern_9-1560](1560/previews/pattern_9.png) | ![pattern_10-1560](1560/previews/pattern_10.png) | ![pattern_11-1560](1560/previews/pattern_11.png) | [<NSFW, click to see>](1560/previews/pattern_12.png) | [<NSFW, click to see>](1560/previews/bikini.png) | [<NSFW, click to see>](1560/previews/bondage.png) | ![free-1560](1560/previews/free.png) | ![maid-1560](1560/previews/maid.png) | ![miko-1560](1560/previews/miko.png) | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) | ![suit-1560](1560/previews/suit.png) | ![yukata-1560](1560/previews/yukata.png) | | 1040 | 0.492 | [Download](1040/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](1040/previews/pattern_1.png) | [<NSFW, click to see>](1040/previews/pattern_2.png) | ![pattern_3-1040](1040/previews/pattern_3.png) | ![pattern_4-1040](1040/previews/pattern_4.png) | ![pattern_5-1040](1040/previews/pattern_5.png) | ![pattern_6-1040](1040/previews/pattern_6.png) | ![pattern_7-1040](1040/previews/pattern_7.png) | ![pattern_8-1040](1040/previews/pattern_8.png) | ![pattern_9-1040](1040/previews/pattern_9.png) | ![pattern_10-1040](1040/previews/pattern_10.png) | ![pattern_11-1040](1040/previews/pattern_11.png) | [<NSFW, click to see>](1040/previews/pattern_12.png) | [<NSFW, click to see>](1040/previews/bikini.png) | [<NSFW, click to see>](1040/previews/bondage.png) | ![free-1040](1040/previews/free.png) | ![maid-1040](1040/previews/maid.png) | ![miko-1040](1040/previews/miko.png) | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) | ![suit-1040](1040/previews/suit.png) | ![yukata-1040](1040/previews/yukata.png) | | 520 | 0.554 | [Download](520/yusa_kozue_idolmastercinderellagirls.zip) | [<NSFW, click to see>](520/previews/pattern_1.png) | [<NSFW, click to see>](520/previews/pattern_2.png) | ![pattern_3-520](520/previews/pattern_3.png) | ![pattern_4-520](520/previews/pattern_4.png) | ![pattern_5-520](520/previews/pattern_5.png) | ![pattern_6-520](520/previews/pattern_6.png) | ![pattern_7-520](520/previews/pattern_7.png) | ![pattern_8-520](520/previews/pattern_8.png) | ![pattern_9-520](520/previews/pattern_9.png) | ![pattern_10-520](520/previews/pattern_10.png) | ![pattern_11-520](520/previews/pattern_11.png) | [<NSFW, click to see>](520/previews/pattern_12.png) | [<NSFW, click to see>](520/previews/bikini.png) | [<NSFW, click to see>](520/previews/bondage.png) | ![free-520](520/previews/free.png) | ![maid-520](520/previews/maid.png) | ![miko-520](520/previews/miko.png) | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) | ![suit-520](520/previews/suit.png) | ![yukata-520](520/previews/yukata.png) |

kbbabu/flanT5_grammerly_ft

kbbabu

2023-09-18T09:36:15Z

4

0

transformers

[ "transformers", "generated_from_trainer", "dataset:grammarly/coedit", "base_model:google/flan-t5-large", "base_model:finetune:google/flan-t5-large", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2023-09-15T10:57:08Z

--- license: apache-2.0 base_model: google/flan-t5-large tags: - generated_from_trainer model-index: - name: coedit-finetuned results: [] datasets: - grammarly/coedit ---  # coedit-finetuned This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an CoEdit 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: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Vishal24/Llama-2-7b-chat-hf-fine-tuned-adapters

Vishal24

2023-09-18T09:35:24Z

0

peft

[ "peft", "region:us" ]

null

2023-09-18T08:11:24Z

--- 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: bfloat16 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: bfloat16 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0

dai152/1

dai152

2023-09-18T09:31:54Z

0

null

[ "license:bigcode-openrail-m", "region:us" ]

null

2023-09-18T09:31:54Z

--- license: bigcode-openrail-m ---

junaid20/llama-fine-tuned-qa

junaid20

2023-09-18T09:26:36Z

0

null

[ "tensorboard", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:finetune:NousResearch/Llama-2-7b-chat-hf", "region:us" ]

null

2023-09-18T09:18:02Z

--- base_model: NousResearch/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: llama-fine-tuned-qa results: [] ---  # llama-fine-tuned-qa This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

matelorg/q-FrozenLake-v1-4x4-noSlippery

matelorg

2023-09-18T09:25:08Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-09-18T09:25:06Z

--- 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="matelorg/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"]) ```

Naveen2910/Taxi-V3

Naveen2910

2023-09-18T09:14:34Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-09-18T09:14:33Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-V3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="Naveen2910/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"]) ```

Lamurias/ppo-Pyramids

Lamurias

2023-09-18T08:59:02Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-09-15T16:59:26Z

--- 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: Lamurias/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Charishma13/my_awesome_model

Charishma13

2023-09-18T08:48:22Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T07:35:11Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: my_awesome_model results: [] ---  # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Cherishh/whisper-slu-1

Cherishh

2023-09-18T08:42:48Z

0

peft

[ "peft", "region:us" ]

null

2023-09-18T08:42:44Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - 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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

pmarar96/ddpm-celebahq-finetuned-butterflies-2epochs

pmarar96

2023-09-18T08:30:39Z

44

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-09-18T08:30:17Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('pmarar96/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```

nickprock/xlm-roberta-base-banking77-classification

nickprock

2023-09-18T08:30:35Z

120

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:banking77", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-08-16T11:02:45Z

--- license: mit tags: - generated_from_trainer datasets: - banking77 metrics: - accuracy widget: - text: 'Can I track the card you sent to me? ' example_title: Card Arrival Example - English - text: 'Posso tracciare la carta che mi avete spedito? ' example_title: Card Arrival Example - Italian - text: Can you explain your exchange rate policy to me? example_title: Exchange Rate Example - English - text: Potete spiegarmi la vostra politica dei tassi di cambio? example_title: Exchange Rate Example - Italian - text: I can't pay by my credit card example_title: Card Not Working Example - English - text: Non riesco a pagare con la mia carta di credito example_title: Card Not Working Example - Italian base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-banking77-classification results: - task: type: text-classification name: Text Classification dataset: name: banking77 type: banking77 config: default split: train args: default metrics: - type: accuracy value: 0.9321428571428572 name: Accuracy - task: type: text-classification name: Text Classification dataset: name: banking77 type: banking77 config: default split: test metrics: - type: accuracy value: 0.9321428571428572 name: Accuracy verified: true - type: precision value: 0.9339627666926148 name: Precision Macro verified: true - type: precision value: 0.9321428571428572 name: Precision Micro verified: true - type: precision value: 0.9339627666926148 name: Precision Weighted verified: true - type: recall value: 0.9321428571428572 name: Recall Macro verified: true - type: recall value: 0.9321428571428572 name: Recall Micro verified: true - type: recall value: 0.9321428571428572 name: Recall Weighted verified: true - type: f1 value: 0.9320514513719953 name: F1 Macro verified: true - type: f1 value: 0.9321428571428572 name: F1 Micro verified: true - type: f1 value: 0.9320514513719956 name: F1 Weighted verified: true - type: loss value: 0.30337899923324585 name: loss verified: true ---  # xlm-roberta-base-banking77-classification This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.3034 - Accuracy: 0.9321 - F1 Score: 0.9321 ## Model description Experiment on a cross-language model to assess how accurate the classification is by using for fine tuning an English dataset but later querying the model in Italian. ## Intended uses & limitations The model can be used on text classification. In particular is fine tuned on banking domain for multilingual task. ## Training and evaluation data The dataset used is [banking77](https://huggingface.co/datasets/banking77) The 77 labels are: |label|intent| |:---:|:----:| |0|activate_my_card| |1|age_limit| |2|apple_pay_or_google_pay| |3|atm_support| |4|automatic_top_up| |5|balance_not_updated_after_bank_transfer| |6|balance_not_updated_after_cheque_or_cash_deposit| |7|beneficiary_not_allowed| |8|cancel_transfer| |9|card_about_to_expire| |10|card_acceptance| |11|card_arrival| |12|card_delivery_estimate| |13|card_linking| |14|card_not_working| |15|card_payment_fee_charged| |16|card_payment_not_recognised| |17|card_payment_wrong_exchange_rate| |18|card_swallowed| |19|cash_withdrawal_charge| |20|cash_withdrawal_not_recognised| |21|change_pin| |22|compromised_card| |23|contactless_not_working| |24|country_support| |25|declined_card_payment| |26|declined_cash_withdrawal| |27|declined_transfer| |28|direct_debit_payment_not_recognised| |29|disposable_card_limits| |30|edit_personal_details| |31|exchange_charge| |32|exchange_rate| |33|exchange_via_app| |34|extra_charge_on_statement| |35|failed_transfer| |36|fiat_currency_support| |37|get_disposable_virtual_card| |38|get_physical_card| |39|getting_spare_card| |40|getting_virtual_card| |41|lost_or_stolen_card| |42|lost_or_stolen_phone| |43|order_physical_card| |44|passcode_forgotten| |45|pending_card_payment| |46|pending_cash_withdrawal| |47|pending_top_up| |48|pending_transfer| |49|pin_blocked| |50|receiving_money| |51|Refund_not_showing_up| |52|request_refund| |53|reverted_card_payment?| |54|supported_cards_and_currencies| |55|terminate_account| |56|top_up_by_bank_transfer_charge| |57|top_up_by_card_charge| |58|top_up_by_cash_or_cheque| |59|top_up_failed| |60|top_up_limits| |61|top_up_reverted| |62|topping_up_by_card| |63|transaction_charged_twice| |64|transfer_fee_charged| |65|transfer_into_account| |66|transfer_not_received_by_recipient| |67|transfer_timing| |68|unable_to_verify_identity| |69|verify_my_identity| |70|verify_source_of_funds| |71|verify_top_up| |72|virtual_card_not_working| |73|visa_or_mastercard| |74|why_verify_identity| |75|wrong_amount_of_cash_received| |76|wrong_exchange_rate_for_cash_withdrawal| ## Training procedure ``` from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/xlm-roberta-base-banking77-classification") pipe("Non riesco a pagare con la carta di credito") ``` ### 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 3.8002 | 1.0 | 157 | 2.7771 | 0.5159 | 0.4483 | | 2.4006 | 2.0 | 314 | 1.6937 | 0.7140 | 0.6720 | | 1.4633 | 3.0 | 471 | 1.0385 | 0.8308 | 0.8153 | | 0.9234 | 4.0 | 628 | 0.7008 | 0.8789 | 0.8761 | | 0.6163 | 5.0 | 785 | 0.5029 | 0.9068 | 0.9063 | | 0.4282 | 6.0 | 942 | 0.4084 | 0.9123 | 0.9125 | | 0.3203 | 7.0 | 1099 | 0.3515 | 0.9253 | 0.9253 | | 0.245 | 8.0 | 1256 | 0.3295 | 0.9227 | 0.9225 | | 0.1863 | 9.0 | 1413 | 0.3092 | 0.9269 | 0.9269 | | 0.1518 | 10.0 | 1570 | 0.2901 | 0.9338 | 0.9338 | | 0.1179 | 11.0 | 1727 | 0.2938 | 0.9318 | 0.9319 | | 0.0969 | 12.0 | 1884 | 0.2906 | 0.9328 | 0.9328 | | 0.0805 | 13.0 | 2041 | 0.2963 | 0.9295 | 0.9295 | | 0.063 | 14.0 | 2198 | 0.2998 | 0.9289 | 0.9288 | | 0.0554 | 15.0 | 2355 | 0.2933 | 0.9351 | 0.9349 | | 0.046 | 16.0 | 2512 | 0.2960 | 0.9328 | 0.9326 | | 0.04 | 17.0 | 2669 | 0.3032 | 0.9318 | 0.9318 | | 0.035 | 18.0 | 2826 | 0.3061 | 0.9312 | 0.9312 | | 0.0317 | 19.0 | 2983 | 0.3030 | 0.9331 | 0.9330 | | 0.0315 | 20.0 | 3140 | 0.3034 | 0.9321 | 0.9321 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

Vicky0522/RSFNet-models

Vicky0522

2023-09-18T08:28:16Z

0

null

[ "arxiv:2303.08682", "region:us" ]

null

2023-09-12T14:21:04Z

Pretrained models for RSFNet Paper: https://arxiv.org/abs/2303.08682 Code: https://github.com/Vicky0522/RSFNet If our work is helpful for your research, please consider citing: ``` @article{oywq2023rsfnet, title={RSFNet: A white-Box image retouching approach using region-specific color filters}, author={Wenqi Ouyang and Yi Dong and Xiaoyang Kang and Peiran Ren and Xin Xu and Xuansong Xie}, journal={https://arxiv.org/abs/2303.08682}, year={2023} } ```

CyberHarem/mukai_takumi_idolmastercinderellagirls

CyberHarem

2023-09-18T08:27:50Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/mukai_takumi_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-18T08:03:55Z

--- license: mit datasets: - CyberHarem/mukai_takumi_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of mukai_takumi_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 6480, you need to download `6480/mukai_takumi_idolmastercinderellagirls.pt` as the embedding and `6480/mukai_takumi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 6480**, with the score of 0.806. The trigger words are: 1. `mukai_takumi_idolmastercinderellagirls` 2. `long_hair, breasts, blush, black_hair, large_breasts, brown_hair, cleavage, bangs, collarbone, green_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:----------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 8100 | 0.797 | [Download](8100/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-8100](8100/previews/pattern_1.png) | ![pattern_2-8100](8100/previews/pattern_2.png) | [<NSFW, click to see>](8100/previews/pattern_3.png) | ![pattern_4-8100](8100/previews/pattern_4.png) | ![pattern_5-8100](8100/previews/pattern_5.png) | [<NSFW, click to see>](8100/previews/pattern_6.png) | ![pattern_7-8100](8100/previews/pattern_7.png) | ![pattern_8-8100](8100/previews/pattern_8.png) | ![pattern_9-8100](8100/previews/pattern_9.png) | ![pattern_10-8100](8100/previews/pattern_10.png) | ![pattern_11-8100](8100/previews/pattern_11.png) | ![pattern_12-8100](8100/previews/pattern_12.png) | ![bikini-8100](8100/previews/bikini.png) | [<NSFW, click to see>](8100/previews/bondage.png) | ![free-8100](8100/previews/free.png) | ![maid-8100](8100/previews/maid.png) | ![miko-8100](8100/previews/miko.png) | [<NSFW, click to see>](8100/previews/nude.png) | [<NSFW, click to see>](8100/previews/nude2.png) | ![suit-8100](8100/previews/suit.png) | ![yukata-8100](8100/previews/yukata.png) | | 7560 | 0.783 | [Download](7560/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-7560](7560/previews/pattern_1.png) | ![pattern_2-7560](7560/previews/pattern_2.png) | [<NSFW, click to see>](7560/previews/pattern_3.png) | ![pattern_4-7560](7560/previews/pattern_4.png) | ![pattern_5-7560](7560/previews/pattern_5.png) | [<NSFW, click to see>](7560/previews/pattern_6.png) | ![pattern_7-7560](7560/previews/pattern_7.png) | ![pattern_8-7560](7560/previews/pattern_8.png) | ![pattern_9-7560](7560/previews/pattern_9.png) | ![pattern_10-7560](7560/previews/pattern_10.png) | ![pattern_11-7560](7560/previews/pattern_11.png) | ![pattern_12-7560](7560/previews/pattern_12.png) | ![bikini-7560](7560/previews/bikini.png) | [<NSFW, click to see>](7560/previews/bondage.png) | ![free-7560](7560/previews/free.png) | ![maid-7560](7560/previews/maid.png) | ![miko-7560](7560/previews/miko.png) | [<NSFW, click to see>](7560/previews/nude.png) | [<NSFW, click to see>](7560/previews/nude2.png) | ![suit-7560](7560/previews/suit.png) | ![yukata-7560](7560/previews/yukata.png) | | 7020 | 0.736 | [Download](7020/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-7020](7020/previews/pattern_1.png) | ![pattern_2-7020](7020/previews/pattern_2.png) | [<NSFW, click to see>](7020/previews/pattern_3.png) | ![pattern_4-7020](7020/previews/pattern_4.png) | ![pattern_5-7020](7020/previews/pattern_5.png) | [<NSFW, click to see>](7020/previews/pattern_6.png) | ![pattern_7-7020](7020/previews/pattern_7.png) | ![pattern_8-7020](7020/previews/pattern_8.png) | ![pattern_9-7020](7020/previews/pattern_9.png) | ![pattern_10-7020](7020/previews/pattern_10.png) | ![pattern_11-7020](7020/previews/pattern_11.png) | ![pattern_12-7020](7020/previews/pattern_12.png) | ![bikini-7020](7020/previews/bikini.png) | [<NSFW, click to see>](7020/previews/bondage.png) | ![free-7020](7020/previews/free.png) | ![maid-7020](7020/previews/maid.png) | ![miko-7020](7020/previews/miko.png) | [<NSFW, click to see>](7020/previews/nude.png) | [<NSFW, click to see>](7020/previews/nude2.png) | ![suit-7020](7020/previews/suit.png) | ![yukata-7020](7020/previews/yukata.png) | | **6480** | **0.806** | [**Download**](6480/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-6480](6480/previews/pattern_1.png) | ![pattern_2-6480](6480/previews/pattern_2.png) | [<NSFW, click to see>](6480/previews/pattern_3.png) | ![pattern_4-6480](6480/previews/pattern_4.png) | ![pattern_5-6480](6480/previews/pattern_5.png) | [<NSFW, click to see>](6480/previews/pattern_6.png) | ![pattern_7-6480](6480/previews/pattern_7.png) | ![pattern_8-6480](6480/previews/pattern_8.png) | ![pattern_9-6480](6480/previews/pattern_9.png) | ![pattern_10-6480](6480/previews/pattern_10.png) | ![pattern_11-6480](6480/previews/pattern_11.png) | ![pattern_12-6480](6480/previews/pattern_12.png) | ![bikini-6480](6480/previews/bikini.png) | [<NSFW, click to see>](6480/previews/bondage.png) | ![free-6480](6480/previews/free.png) | ![maid-6480](6480/previews/maid.png) | ![miko-6480](6480/previews/miko.png) | [<NSFW, click to see>](6480/previews/nude.png) | [<NSFW, click to see>](6480/previews/nude2.png) | ![suit-6480](6480/previews/suit.png) | ![yukata-6480](6480/previews/yukata.png) | | 5940 | 0.766 | [Download](5940/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-5940](5940/previews/pattern_1.png) | ![pattern_2-5940](5940/previews/pattern_2.png) | [<NSFW, click to see>](5940/previews/pattern_3.png) | ![pattern_4-5940](5940/previews/pattern_4.png) | ![pattern_5-5940](5940/previews/pattern_5.png) | [<NSFW, click to see>](5940/previews/pattern_6.png) | ![pattern_7-5940](5940/previews/pattern_7.png) | ![pattern_8-5940](5940/previews/pattern_8.png) | ![pattern_9-5940](5940/previews/pattern_9.png) | ![pattern_10-5940](5940/previews/pattern_10.png) | ![pattern_11-5940](5940/previews/pattern_11.png) | ![pattern_12-5940](5940/previews/pattern_12.png) | ![bikini-5940](5940/previews/bikini.png) | [<NSFW, click to see>](5940/previews/bondage.png) | ![free-5940](5940/previews/free.png) | ![maid-5940](5940/previews/maid.png) | ![miko-5940](5940/previews/miko.png) | [<NSFW, click to see>](5940/previews/nude.png) | [<NSFW, click to see>](5940/previews/nude2.png) | ![suit-5940](5940/previews/suit.png) | ![yukata-5940](5940/previews/yukata.png) | | 5400 | 0.792 | [Download](5400/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-5400](5400/previews/pattern_1.png) | ![pattern_2-5400](5400/previews/pattern_2.png) | [<NSFW, click to see>](5400/previews/pattern_3.png) | ![pattern_4-5400](5400/previews/pattern_4.png) | ![pattern_5-5400](5400/previews/pattern_5.png) | [<NSFW, click to see>](5400/previews/pattern_6.png) | ![pattern_7-5400](5400/previews/pattern_7.png) | ![pattern_8-5400](5400/previews/pattern_8.png) | ![pattern_9-5400](5400/previews/pattern_9.png) | ![pattern_10-5400](5400/previews/pattern_10.png) | ![pattern_11-5400](5400/previews/pattern_11.png) | ![pattern_12-5400](5400/previews/pattern_12.png) | ![bikini-5400](5400/previews/bikini.png) | [<NSFW, click to see>](5400/previews/bondage.png) | ![free-5400](5400/previews/free.png) | ![maid-5400](5400/previews/maid.png) | ![miko-5400](5400/previews/miko.png) | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) | ![suit-5400](5400/previews/suit.png) | ![yukata-5400](5400/previews/yukata.png) | | 4860 | 0.794 | [Download](4860/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-4860](4860/previews/pattern_1.png) | ![pattern_2-4860](4860/previews/pattern_2.png) | [<NSFW, click to see>](4860/previews/pattern_3.png) | ![pattern_4-4860](4860/previews/pattern_4.png) | ![pattern_5-4860](4860/previews/pattern_5.png) | [<NSFW, click to see>](4860/previews/pattern_6.png) | ![pattern_7-4860](4860/previews/pattern_7.png) | ![pattern_8-4860](4860/previews/pattern_8.png) | ![pattern_9-4860](4860/previews/pattern_9.png) | ![pattern_10-4860](4860/previews/pattern_10.png) | ![pattern_11-4860](4860/previews/pattern_11.png) | ![pattern_12-4860](4860/previews/pattern_12.png) | ![bikini-4860](4860/previews/bikini.png) | [<NSFW, click to see>](4860/previews/bondage.png) | ![free-4860](4860/previews/free.png) | ![maid-4860](4860/previews/maid.png) | ![miko-4860](4860/previews/miko.png) | [<NSFW, click to see>](4860/previews/nude.png) | [<NSFW, click to see>](4860/previews/nude2.png) | ![suit-4860](4860/previews/suit.png) | ![yukata-4860](4860/previews/yukata.png) | | 4320 | 0.747 | [Download](4320/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | ![pattern_2-4320](4320/previews/pattern_2.png) | [<NSFW, click to see>](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | ![pattern_5-4320](4320/previews/pattern_5.png) | [<NSFW, click to see>](4320/previews/pattern_6.png) | ![pattern_7-4320](4320/previews/pattern_7.png) | ![pattern_8-4320](4320/previews/pattern_8.png) | ![pattern_9-4320](4320/previews/pattern_9.png) | ![pattern_10-4320](4320/previews/pattern_10.png) | ![pattern_11-4320](4320/previews/pattern_11.png) | ![pattern_12-4320](4320/previews/pattern_12.png) | ![bikini-4320](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | ![free-4320](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | 3780 | 0.761 | [Download](3780/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-3780](3780/previews/pattern_1.png) | ![pattern_2-3780](3780/previews/pattern_2.png) | [<NSFW, click to see>](3780/previews/pattern_3.png) | ![pattern_4-3780](3780/previews/pattern_4.png) | ![pattern_5-3780](3780/previews/pattern_5.png) | [<NSFW, click to see>](3780/previews/pattern_6.png) | ![pattern_7-3780](3780/previews/pattern_7.png) | ![pattern_8-3780](3780/previews/pattern_8.png) | ![pattern_9-3780](3780/previews/pattern_9.png) | ![pattern_10-3780](3780/previews/pattern_10.png) | ![pattern_11-3780](3780/previews/pattern_11.png) | ![pattern_12-3780](3780/previews/pattern_12.png) | ![bikini-3780](3780/previews/bikini.png) | [<NSFW, click to see>](3780/previews/bondage.png) | ![free-3780](3780/previews/free.png) | ![maid-3780](3780/previews/maid.png) | ![miko-3780](3780/previews/miko.png) | [<NSFW, click to see>](3780/previews/nude.png) | [<NSFW, click to see>](3780/previews/nude2.png) | ![suit-3780](3780/previews/suit.png) | ![yukata-3780](3780/previews/yukata.png) | | 3240 | 0.769 | [Download](3240/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-3240](3240/previews/pattern_1.png) | ![pattern_2-3240](3240/previews/pattern_2.png) | [<NSFW, click to see>](3240/previews/pattern_3.png) | ![pattern_4-3240](3240/previews/pattern_4.png) | ![pattern_5-3240](3240/previews/pattern_5.png) | [<NSFW, click to see>](3240/previews/pattern_6.png) | ![pattern_7-3240](3240/previews/pattern_7.png) | ![pattern_8-3240](3240/previews/pattern_8.png) | ![pattern_9-3240](3240/previews/pattern_9.png) | ![pattern_10-3240](3240/previews/pattern_10.png) | ![pattern_11-3240](3240/previews/pattern_11.png) | ![pattern_12-3240](3240/previews/pattern_12.png) | ![bikini-3240](3240/previews/bikini.png) | [<NSFW, click to see>](3240/previews/bondage.png) | ![free-3240](3240/previews/free.png) | ![maid-3240](3240/previews/maid.png) | ![miko-3240](3240/previews/miko.png) | [<NSFW, click to see>](3240/previews/nude.png) | [<NSFW, click to see>](3240/previews/nude2.png) | ![suit-3240](3240/previews/suit.png) | ![yukata-3240](3240/previews/yukata.png) | | 2700 | 0.693 | [Download](2700/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-2700](2700/previews/pattern_1.png) | ![pattern_2-2700](2700/previews/pattern_2.png) | [<NSFW, click to see>](2700/previews/pattern_3.png) | ![pattern_4-2700](2700/previews/pattern_4.png) | ![pattern_5-2700](2700/previews/pattern_5.png) | [<NSFW, click to see>](2700/previews/pattern_6.png) | ![pattern_7-2700](2700/previews/pattern_7.png) | ![pattern_8-2700](2700/previews/pattern_8.png) | ![pattern_9-2700](2700/previews/pattern_9.png) | ![pattern_10-2700](2700/previews/pattern_10.png) | ![pattern_11-2700](2700/previews/pattern_11.png) | ![pattern_12-2700](2700/previews/pattern_12.png) | ![bikini-2700](2700/previews/bikini.png) | [<NSFW, click to see>](2700/previews/bondage.png) | ![free-2700](2700/previews/free.png) | ![maid-2700](2700/previews/maid.png) | ![miko-2700](2700/previews/miko.png) | [<NSFW, click to see>](2700/previews/nude.png) | [<NSFW, click to see>](2700/previews/nude2.png) | ![suit-2700](2700/previews/suit.png) | ![yukata-2700](2700/previews/yukata.png) | | 2160 | 0.810 | [Download](2160/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-2160](2160/previews/pattern_1.png) | ![pattern_2-2160](2160/previews/pattern_2.png) | [<NSFW, click to see>](2160/previews/pattern_3.png) | ![pattern_4-2160](2160/previews/pattern_4.png) | ![pattern_5-2160](2160/previews/pattern_5.png) | [<NSFW, click to see>](2160/previews/pattern_6.png) | ![pattern_7-2160](2160/previews/pattern_7.png) | ![pattern_8-2160](2160/previews/pattern_8.png) | ![pattern_9-2160](2160/previews/pattern_9.png) | ![pattern_10-2160](2160/previews/pattern_10.png) | ![pattern_11-2160](2160/previews/pattern_11.png) | ![pattern_12-2160](2160/previews/pattern_12.png) | ![bikini-2160](2160/previews/bikini.png) | [<NSFW, click to see>](2160/previews/bondage.png) | ![free-2160](2160/previews/free.png) | ![maid-2160](2160/previews/maid.png) | ![miko-2160](2160/previews/miko.png) | [<NSFW, click to see>](2160/previews/nude.png) | [<NSFW, click to see>](2160/previews/nude2.png) | ![suit-2160](2160/previews/suit.png) | ![yukata-2160](2160/previews/yukata.png) | | 1620 | 0.718 | [Download](1620/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-1620](1620/previews/pattern_1.png) | ![pattern_2-1620](1620/previews/pattern_2.png) | [<NSFW, click to see>](1620/previews/pattern_3.png) | ![pattern_4-1620](1620/previews/pattern_4.png) | ![pattern_5-1620](1620/previews/pattern_5.png) | [<NSFW, click to see>](1620/previews/pattern_6.png) | ![pattern_7-1620](1620/previews/pattern_7.png) | ![pattern_8-1620](1620/previews/pattern_8.png) | ![pattern_9-1620](1620/previews/pattern_9.png) | ![pattern_10-1620](1620/previews/pattern_10.png) | ![pattern_11-1620](1620/previews/pattern_11.png) | ![pattern_12-1620](1620/previews/pattern_12.png) | ![bikini-1620](1620/previews/bikini.png) | [<NSFW, click to see>](1620/previews/bondage.png) | ![free-1620](1620/previews/free.png) | ![maid-1620](1620/previews/maid.png) | ![miko-1620](1620/previews/miko.png) | [<NSFW, click to see>](1620/previews/nude.png) | [<NSFW, click to see>](1620/previews/nude2.png) | ![suit-1620](1620/previews/suit.png) | ![yukata-1620](1620/previews/yukata.png) | | 1080 | 0.703 | [Download](1080/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-1080](1080/previews/pattern_1.png) | ![pattern_2-1080](1080/previews/pattern_2.png) | [<NSFW, click to see>](1080/previews/pattern_3.png) | ![pattern_4-1080](1080/previews/pattern_4.png) | ![pattern_5-1080](1080/previews/pattern_5.png) | [<NSFW, click to see>](1080/previews/pattern_6.png) | ![pattern_7-1080](1080/previews/pattern_7.png) | ![pattern_8-1080](1080/previews/pattern_8.png) | ![pattern_9-1080](1080/previews/pattern_9.png) | ![pattern_10-1080](1080/previews/pattern_10.png) | ![pattern_11-1080](1080/previews/pattern_11.png) | ![pattern_12-1080](1080/previews/pattern_12.png) | ![bikini-1080](1080/previews/bikini.png) | [<NSFW, click to see>](1080/previews/bondage.png) | ![free-1080](1080/previews/free.png) | ![maid-1080](1080/previews/maid.png) | ![miko-1080](1080/previews/miko.png) | [<NSFW, click to see>](1080/previews/nude.png) | [<NSFW, click to see>](1080/previews/nude2.png) | ![suit-1080](1080/previews/suit.png) | ![yukata-1080](1080/previews/yukata.png) | | 540 | 0.610 | [Download](540/mukai_takumi_idolmastercinderellagirls.zip) | ![pattern_1-540](540/previews/pattern_1.png) | ![pattern_2-540](540/previews/pattern_2.png) | [<NSFW, click to see>](540/previews/pattern_3.png) | ![pattern_4-540](540/previews/pattern_4.png) | ![pattern_5-540](540/previews/pattern_5.png) | [<NSFW, click to see>](540/previews/pattern_6.png) | ![pattern_7-540](540/previews/pattern_7.png) | ![pattern_8-540](540/previews/pattern_8.png) | ![pattern_9-540](540/previews/pattern_9.png) | ![pattern_10-540](540/previews/pattern_10.png) | ![pattern_11-540](540/previews/pattern_11.png) | ![pattern_12-540](540/previews/pattern_12.png) | ![bikini-540](540/previews/bikini.png) | [<NSFW, click to see>](540/previews/bondage.png) | ![free-540](540/previews/free.png) | ![maid-540](540/previews/maid.png) | ![miko-540](540/previews/miko.png) | [<NSFW, click to see>](540/previews/nude.png) | [<NSFW, click to see>](540/previews/nude2.png) | ![suit-540](540/previews/suit.png) | ![yukata-540](540/previews/yukata.png) |

BAAI/Aquila-7B

BAAI

2023-09-18T08:26:37Z

1,824

17

transformers

[ "transformers", "pytorch", "aquila", "custom_code", "license:other", "endpoints_compatible", "region:us" ]

null

2023-06-08T07:25:29Z

--- license: other --- ![Aquila_logo](./log.jpeg) <h4 align="center"> English | <a href="https://huggingface.co/BAAI/Aquila-7B/blob/main/README_zh.md">简体中文</a> | </h4> Aquila Language Model is the first open source language model that supports both Chinese and English knowledge, commercial license agreements, and compliance with domestic data regulations. - 🌟 **Supports open source commercial licenses**. The source code of the Aquila series models is based on the [Apache 2.0 agreement](https://www.apache.org/licenses/LICENSE-2.0), while the model weight is based on the [BAAI Aquila Model License Agreement](https://huggingface.co/BAAI/Aquila-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf). Users can use it for commercial purposes as long as they meet the licensing restrictions. - ✍️ **Possesses Chinese and English knowledge**. The Aquila series model is trained from scratch on a high-quality corpus of Chinese and English languages, with Chinese corpora accounting for about 40%, ensuring that the model accumulates native Chinese world knowledge during the pre-training phase, rather than translated knowledge. - 👮‍♀️ **Complies with domestic data regulations**. The Chinese corpora of the Aquila series models come from Intelligence Source's accumulated Chinese datasets over the years, including Chinese internet data from over 10,000 sources (more than 99% of which are domestic sources), as well as high-quality Chinese literature and book data supported by authoritative domestic organizations. We will continue to accumulate high-quality and diverse datasets and incorporate them into the subsequent training of the Aquila base models. - 🎯 **Continuous improvements and open sourcing**. We will continue to improve training data, optimize training methods, and enhance model performance, cultivate a flourishing "model tree" on a better base model foundation, and continuously update open-source versions. The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels, including the [FlagAI GitHub repository](https://github.com/FlagAI-Open/FlagAI/), [FlagAI's Zhihu account](https://www.zhihu.com/people/95-22-20-18) and [FlagAI's official technical communication group](https://github.com/FlagAI-Open/FlagAI/blob/master/wechat-qrcode.jpg). | Model | Model Type | Description | Status | GPUs Used | | :----------------- | :----------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :--------------| :----------- | | Aquila-7B | Base model, 7 billion parameters | **Aquila Base Model** inherits the architectural design advantages of GPT-3 and LLaMA. It replaces a batch of more efficient underlying operator implementations, redesigns the implementation of bilingual tokenizer, upgrades BMTrain parallel training method, and achieves nearly 8 times the training efficiency of Magtron+DeepSpeed ZeRO-2. | Released | Nvidia-A100 | | Aquila-33B | Base model, 33 billion parameters | Same as above | Coming soon | Nvidia-A100 | | AquilaChat-7B | SFT model, fine-tuned and RL based on Aquila-7B | **AquilaChat Dialog Model** supports fluent text dialogue and multiple language generation tasks, and realizes the call of AquilaChat to other models and tools by defining an expandable special instruction specification, which is easy to extend. For example, calling the open source **[AltDiffusion](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion-m18) multimodal language image generation model** of Flagship Intelligence achieved smooth image generation capability. Together with Flagship Intelligence's **InstructFace multi-step controllable text-picture model**, it is easy to achieve multi-step controllable editing of human face images. | Released | Nvidia-A100 | | AquilaChat-33B | SFT model, fine-tuned and RL based on Aquila-33B | Same as above | Coming soon | Nvidia-A100 | | AquilaCode-7B-NV | Base model, "text-code" generation model, further pre-trained based on Aquila-7B, trained on Nvidia | AquilaCode-7B achieves high performance with small data sets and parameters, and is currently the best open source code model that supports both Chinese and English, trained using training code data with compliant open source licenses after high-quality filtering. AquilaCode-7B has been trained on both Nvidia and domestic chips for code models. | Released on GitHub | Nvidia-A100 | | AquilaCode-7B-TS | Base model, "text-code" generation model, further pre-trained based on Aquila-7B, trained on Horizon Robotics chips | Same as above | Released on GitHub | Tianshu-BI-V100 | We will continue to release improved versions of Aquila model as open source. - 2023/08/15 ：release v0.10 - Aquila-7B-01 md5: 4279db72e68df1a0705ecc8d4c7be3db - Aquila-7B-02 md5: 621f8ce4c8deebe1635f5a09aa4b80f2 - AquilaChat-7B-01 md5: 22b22ffaed51388ce23f8e328a9b6a18 - AquilaChat-7B-02 md5: 6e84423fe2837c79c0ced6817c316bd4 Aquila-7B has shown improvements in the FlagEval large model evaluation ("Objective") compared to last version. It achieved improvements of approximately 9.09% on TruthfulQA datasets. For detailed evaluation results, please refer to the website http://flageval.baai.ac.cn. For detailed version change history, see [Change Log](https://huggingface.co/BAAI/Aquila-7B/blob/main/change_log.log). ## Quick Start Aquila-7B ### 1. Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_info = "BAAI/Aquila-7B" tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True) model.eval() model.to("cuda:0") text = "汽车EDR是什么" tokens = tokenizer.encode_plus(text)['input_ids'][:-1] tokens = torch.tensor(tokens)[None,].to("cuda:0") with torch.no_grad(): out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007)[0] out = tokenizer.decode(out.cpu().numpy().tolist()) print(out) ``` ## License Aquila-7B and AquilaChat-33B open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/Aquila-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf)

bardsai/twitter-emotion-pl-base

bardsai

2023-09-18T08:23:29Z

898

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "twitter", "pl", "dataset:datasets/tweet_eval", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-19T10:55:07Z

--- language: pl tags: - text-classification - twitter datasets: - datasets/tweet_eval metrics: - f1 - accuracy - precision - recall widget: - text: "Nigdy przegrana nie sprawiła mi takiej radości. Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl" example_title: "Example 1" - text: "Osoby z Ukrainy zapłacą za życie w centrach pomocy? Sprzeczne prawem UE, niehumanitarne, okrutne." example_title: "Example 2" --- # Twitter emotion PL (base) Twitter emotion PL (base) is a model based on [herbert-base](https://huggingface.co/allegro/herbert-base-cased) for analyzing emotion of Polish twitter posts. It was trained on the translated version of [TweetEval](https://www.researchgate.net/publication/347233661_TweetEval_Unified_Benchmark_and_Comparative_Evaluation_for_Tweet_Classification) by Barbieri et al., 2020 for 10 epochs on single RTX3090 gpu. The model will give you a four labels: joy, optimism, sadness and anger. ## How to use You can use this model directly with a pipeline for text classification: ```python from transformers import pipeline nlp = pipeline("text-classification", model="bardsai/twitter-emotion-pl-base") nlp("Nigdy przegrana nie sprawiła mi takiej radości. Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl") ``` ```bash [{'label': 'joy', 'score': 0.5163766145706177}] ``` ## Performance | Metric | Value | | --- | ----------- | | f1 macro | 0.756 | | precision macro | 0.767 | | recall macro | 0.750 | | accuracy | 0.789 | | samples per second | 131.6 | (The performance was evaluated on RTX 3090 gpu) ## Changelog - 2023-07-19: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/) Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]

octava/audio_classification

octava

2023-09-18T08:23:27Z

161

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-09-11T13:23:00Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: audio_classification results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.09734513274336283 ---  # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6471 - Accuracy: 0.0973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 15 | 2.6423 | 0.0531 | | No log | 2.0 | 30 | 2.6471 | 0.0973 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

AlexanderBond/distilbert-base-uncased-finetuned-emotion

AlexanderBond

2023-09-18T08:12:36Z

102

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T06:01:24Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion 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: 0.922 - name: F1 type: f1 value: 0.9218912616592688 ---  # distilbert-base-uncased-finetuned-emotion 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: 0.2165 - Accuracy: 0.922 - F1: 0.9219 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8214 | 1.0 | 250 | 0.3159 | 0.909 | 0.9085 | | 0.2497 | 2.0 | 500 | 0.2165 | 0.922 | 0.9219 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3

vdivya/dummy-model

vdivya

2023-09-18T08:09:13Z

61

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T07:57:50Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: dummy-model results: [] ---  # dummy-model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0608 - Train Accuracy: 0.9804 - Validation Loss: 0.2496 - Validation Accuracy: 0.9140 - Epoch: 2 ## 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', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 25257, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2262 | 0.9143 | 0.2503 | 0.9094 | 0 | | 0.1133 | 0.9622 | 0.2515 | 0.9083 | 1 | | 0.0608 | 0.9804 | 0.2496 | 0.9140 | 2 | ### Framework versions - Transformers 4.33.1 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3

kurileo/blip2-opt-2.7b-refines

kurileo

2023-09-18T08:03:37Z

2

0

peft

[ "peft", "region:us" ]

null

2023-09-18T08:02:34Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: bfloat16 ### Framework versions - PEFT 0.5.0

ernestum/sac-seals-Ant-v1

ernestum

2023-09-18T07:54:23Z

3

0

stable-baselines3

[ "stable-baselines3", "seals/Ant-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-15T11:54:00Z

--- library_name: stable-baselines3 tags: - seals/Ant-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Ant-v1 type: seals/Ant-v1 metrics: - type: mean_reward value: 1004.15 +/- 26.60 name: mean_reward verified: false --- # **SAC** Agent playing **seals/Ant-v1** This is a trained model of a **SAC** agent playing **seals/Ant-v1** 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 sac --env seals/Ant-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Ant-v1 -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 sac --env seals/Ant-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Ant-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/Ant-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/Ant-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('buffer_size', 1000000), ('gamma', 0.98), ('learning_rate', 0.0018514039303149058), ('learning_starts', 1000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -2.2692589009754176, 'net_arch': [256, 256], 'use_sde': False}), ('tau', 0.05), ('train_freq', 64), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ernestum/sac-seals-HalfCheetah-v1

ernestum

2023-09-18T07:53:35Z

2

0

stable-baselines3

[ "stable-baselines3", "seals/HalfCheetah-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-15T11:53:34Z

--- library_name: stable-baselines3 tags: - seals/HalfCheetah-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/HalfCheetah-v1 type: seals/HalfCheetah-v1 metrics: - type: mean_reward value: 1183.52 +/- 22.65 name: mean_reward verified: false --- # **SAC** Agent playing **seals/HalfCheetah-v1** This is a trained model of a **SAC** agent playing **seals/HalfCheetah-v1** 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 sac --env seals/HalfCheetah-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/HalfCheetah-v1 -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 sac --env seals/HalfCheetah-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/HalfCheetah-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/HalfCheetah-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/HalfCheetah-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 100000), ('gamma', 0.95), ('learning_rate', 0.000884624878315995), ('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -0.6932709443503001, 'net_arch': [64, 64], 'use_sde': False}), ('tau', 0.01), ('train_freq', 64), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ernestum/sac-seals-Hopper-v1

ernestum

2023-09-18T07:52:51Z

4

0

stable-baselines3

[ "stable-baselines3", "seals/Hopper-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-15T11:53:03Z

--- library_name: stable-baselines3 tags: - seals/Hopper-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Hopper-v1 type: seals/Hopper-v1 metrics: - type: mean_reward value: 2279.30 +/- 124.09 name: mean_reward verified: false --- # **SAC** Agent playing **seals/Hopper-v1** This is a trained model of a **SAC** agent playing **seals/Hopper-v1** 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 sac --env seals/Hopper-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Hopper-v1 -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 sac --env seals/Hopper-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Hopper-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/Hopper-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/Hopper-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 100000), ('gamma', 0.98), ('learning_rate', 0.001709807687567946), ('learning_starts', 1000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -1.6829391077276037, 'net_arch': [256, 256], 'use_sde': False}), ('tau', 0.08), ('train_freq', 32), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

nchen909/codellama-7b-python-sft-v1.1

nchen909

2023-09-18T07:52:00Z

6

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-15T09:27:02Z

Evol-Instruct-Python --- license: cc ---

ernestum/ppo-seals-Walker2d-v1

ernestum

2023-09-18T07:48:56Z

0

stable-baselines3

[ "stable-baselines3", "seals/Walker2d-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-15T11:51:52Z

--- library_name: stable-baselines3 tags: - seals/Walker2d-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Walker2d-v1 type: seals/Walker2d-v1 metrics: - type: mean_reward value: 2465.56 +/- 272.31 name: mean_reward verified: false --- # **PPO** Agent playing **seals/Walker2d-v1** This is a trained model of a **PPO** agent playing **seals/Walker2d-v1** 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 ppo --env seals/Walker2d-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Walker2d-v1 -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 ppo --env seals/Walker2d-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Walker2d-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Walker2d-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Walker2d-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 8), ('clip_range', 0.4), ('ent_coef', 0.00013057334805552262), ('gae_lambda', 0.92), ('gamma', 0.98), ('learning_rate', 3.791707778339674e-05), ('max_grad_norm', 0.6), ('n_envs', 1), ('n_epochs', 5), ('n_steps', 2048), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.98, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.ReLU'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [256, 256], 'vf': [256, 256]}]}), ('vf_coef', 0.6167177795726859), ('normalize_kwargs', {'norm_obs': {'gamma': 0.98, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ernestum/ppo-seals-Humanoid-v1

ernestum

2023-09-18T07:47:41Z

0

stable-baselines3

[ "stable-baselines3", "seals/Humanoid-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-18T07:46:45Z

--- library_name: stable-baselines3 tags: - seals/Humanoid-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Humanoid-v1 type: seals/Humanoid-v1 metrics: - type: mean_reward value: 3224.12 +/- 925.36 name: mean_reward verified: false --- # **PPO** Agent playing **seals/Humanoid-v1** This is a trained model of a **PPO** agent playing **seals/Humanoid-v1** 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 ppo --env seals/Humanoid-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Humanoid-v1 -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 ppo --env seals/Humanoid-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Humanoid-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Humanoid-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Humanoid-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 0.2), ('ent_coef', 2.0745206045994986e-05), ('gae_lambda', 0.92), ('gamma', 0.999), ('learning_rate', 2.0309225666232827e-05), ('max_grad_norm', 0.5), ('n_envs', 1), ('n_epochs', 20), ('n_steps', 2048), ('n_timesteps', 10000000.0), ('normalize', {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.ReLU'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [256, 256], 'vf': [256, 256]}]}), ('vf_coef', 0.819262464558427), ('normalize_kwargs', {'norm_obs': {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ernestum/ppo-seals-Swimmer-v1

ernestum

2023-09-18T07:45:33Z

0

stable-baselines3

[ "stable-baselines3", "seals/Swimmer-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-15T11:50:49Z

--- library_name: stable-baselines3 tags: - seals/Swimmer-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Swimmer-v1 type: seals/Swimmer-v1 metrics: - type: mean_reward value: 292.84 +/- 3.69 name: mean_reward verified: false --- # **PPO** Agent playing **seals/Swimmer-v1** This is a trained model of a **PPO** agent playing **seals/Swimmer-v1** 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 ppo --env seals/Swimmer-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Swimmer-v1 -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 ppo --env seals/Swimmer-v1 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Swimmer-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Swimmer-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Swimmer-v1 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 8), ('clip_range', 0.1), ('ent_coef', 5.167107294612664e-08), ('gae_lambda', 0.95), ('gamma', 0.999), ('learning_rate', 0.0001214437022727675), ('max_grad_norm', 2), ('n_epochs', 20), ('n_steps', 2048), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.6162112311062333), ('normalize_kwargs', {'norm_obs': {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ernestum/ppo-seals-MountainCar-v0

ernestum

2023-09-18T07:43:55Z

0

stable-baselines3

[ "stable-baselines3", "seals/MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-15T11:50:03Z

--- library_name: stable-baselines3 tags: - seals/MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/MountainCar-v0 type: seals/MountainCar-v0 metrics: - type: mean_reward value: -97.00 +/- 8.26 name: mean_reward verified: false --- # **PPO** Agent playing **seals/MountainCar-v0** This is a trained model of a **PPO** agent playing **seals/MountainCar-v0** 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 ppo --env seals/MountainCar-v0 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-v0 -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 ppo --env seals/MountainCar-v0 -orga ernestum -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/MountainCar-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/MountainCar-v0 -f logs/ -orga ernestum ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('clip_range', 0.2), ('ent_coef', 6.4940755116195606e-06), ('gae_lambda', 0.98), ('gamma', 0.99), ('learning_rate', 0.0004476103728105138), ('max_grad_norm', 1), ('n_envs', 16), ('n_epochs', 20), ('n_steps', 256), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.25988158989488963), ('normalize_kwargs', {'norm_obs': {'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

checkiejan/prefix-paraphase-30-20-auto

checkiejan

2023-09-18T07:28:51Z

0

peft

[ "peft", "region:us" ]

null

2023-09-18T07:28:49Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

lepin2001/catsordogs

lepin2001

2023-09-18T07:23:07Z

0

fastai

[ "fastai", "code", "en", "license:apache-2.0", "region:us" ]

null

2023-09-18T07:19:04Z

--- license: apache-2.0 language: - en library_name: fastai tags: - code ---

kming/unispeech-sat-base-plus-sv-finetuned-ami-ten-percent-train

kming

2023-09-18T07:21:10Z

159

0

transformers

[ "transformers", "pytorch", "unispeech-sat", "audio-xvector", "generated_from_trainer", "dataset:edinburghcstr/ami", "base_model:microsoft/unispeech-sat-base-plus-sv", "base_model:finetune:microsoft/unispeech-sat-base-plus-sv", "endpoints_compatible", "region:us" ]

null

2023-09-18T07:11:54Z

--- base_model: microsoft/unispeech-sat-base-plus-sv tags: - generated_from_trainer datasets: - edinburghcstr/ami model-index: - name: unispeech-sat-base-plus-sv-finetuned-ami-ten-percent-train results: [] ---  # unispeech-sat-base-plus-sv-finetuned-ami-ten-percent-train This model is a fine-tuned version of [microsoft/unispeech-sat-base-plus-sv](https://huggingface.co/microsoft/unispeech-sat-base-plus-sv) on the ami 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

Archfiend/ardic-ai-sd-fdb

Archfiend

2023-09-18T07:17:21Z

17

1

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-21T20:20:03Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### ardic-ai-sd-fdb Dreambooth model trained by Archfiend Sample pictures of this concept:

marcelsamyn/lora-trained-xl-folder

marcelsamyn

2023-09-18T07:16:10Z

1

0

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:marcelsamyn/marcelsamyn3", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-09-18T06:27:31Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: marcelsamyn tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: false datasets: - marcelsamyn/marcelsamyn3 --- # LoRA DreamBooth - marcelsamyn/lora-trained-xl-folder These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on the concept prompt: `marcelsamyn` Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```python import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) # This is where you load your trained weights pipe.load_lora_weights('marcelsamyn/lora-trained-xl-folder') pipe.to("cuda") prompt = "A majestic marcelsamyn jumping from a big stone at night" image = pipe(prompt=prompt, num_inference_steps=50).images[0] ```

warp-ai/wuerstchen-prior-model-base

warp-ai

2023-09-18T07:02:05Z

24

1

diffusers

[ "diffusers", "safetensors", "arxiv:2306.00637", "arxiv:1910.09700", "license:mit", "region:us" ]

null

2023-09-03T19:39:26Z

--- license: mit --- <img src="https://cdn-uploads.huggingface.co/production/uploads/634cb5eefb80cc6bcaf63c3e/i-DYpDHw8Pwiy7QBKZVR5.jpeg" width=1500> ## Würstchen - Overview Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the [paper](https://arxiv.org/abs/2306.00637)). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, allowing also cheaper and faster inference. ## Würstchen - Prior The Prior is what we refer to as "Stage C". It is the text-conditional model, operating in the small latent space that Stage A and Stage B encode images into. During inference, its job is to generate the image latents given text. These image latents are then sent to Stages A & B to decode the latents into pixel space. ### Prior - Model - Base This is the base checkpoint for the Prior (Stage C). This means this is only pretrained and generates mostly standard images. We recommend using the [interpolated model](https://huggingface.co/warp-ai/wuerstchen-prior-model-interpolated), as this is our best checkpoint for the Prior (Stage C) because it was finetuned on a curated dataset. However, we recommend this checkpoint if you want to finetune Würstchen on your own large dataset, as the other checkpoints are already biased towards being more artistic. This checkpoint should provide a fairly standard baseline to finetune from, as long as your dataset is rather large. **Note:** This checkpoint was also already trained on multi-aspect-ratios, meaning you can generate larger images than just 1024x1024. Sometimes generations up to 2048x2048 even work. Feel free to try it out! **Also Note:** The base checkpoint usually requires a higher classifier-free-guidance value (`guidance_scale=8.0`) and also a negative caption in order to make good looking images. The [interpolated model](https://huggingface.co/warp-ai/wuerstchen-prior-model-interpolated) and [finetuned model](https://huggingface.co/warp-ai/wuerstchen-prior-model-finetuned) usually don't need a negative caption and work better with a lower classifier-free-guidance value (`guidance_scale=4.0`). ### Image Sizes Würstchen was trained on image resolutions between 1024x1024 & 1536x1536. We sometimes also observe good outputs at resolutions like 1024x2048. Feel free to try it out. We also observed that the Prior (Stage C) adapts extremely fast to new resolutions. So finetuning it at 2048x2048 should be computationally cheap. <img src="https://cdn-uploads.huggingface.co/production/uploads/634cb5eefb80cc6bcaf63c3e/IfVsUDcP15OY-5wyLYKnQ.jpeg" width=1000> ## How to run This pipeline should be run together with https://huggingface.co/warp-ai/wuerstchen: ```py import torch from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline from diffusers.pipelines.wuerstchen import WuerstchenPrior, default_stage_c_timesteps device = "cuda" dtype = torch.float16 num_images_per_prompt = 2 prior = WuerstchenPrior.from_pretrained("warp-ai/wuerstchen-prior-model-base", torch_dtype=dtype).to(device) prior_pipeline = WuerstchenPriorPipeline.from_pretrained( "warp-ai/wuerstchen-prior", prior=prior, torch_dtype=dtype ).to(device) decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained( "warp-ai/wuerstchen", torch_dtype=dtype ).to(device) caption = "Anthropomorphic cat dressed as a fire fighter" negative_prompt = "bad anatomy, blurry, fuzzy, extra arms, extra fingers, poorly drawn hands, disfigured, tiling, deformed, mutated, drawing" prior_output = prior_pipeline( prompt=caption, height=1024, width=1024, timesteps=default_stage_c_timesteps, negative_prompt=negative_prompt, guidance_scale=8.0, num_images_per_prompt=num_images_per_prompt, ) decoder_output = decoder_pipeline( image_embeddings=prior_output.image_embeddings, prompt=caption, negative_prompt=negative_prompt, num_images_per_prompt=num_images_per_prompt, guidance_scale=0.0, output_type="pil", ).images ``` ## Model Details - **Developed by:** Pablo Pernias, Dominic Rampas - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** MIT - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a Diffusion model in the style of Stage C from the [Würstchen paper](https://arxiv.org/abs/2306.00637) that uses a fixed, pretrained text encoder ([CLIP ViT-bigG/14](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)). - **Resources for more information:** [GitHub Repository](https://github.com/dome272/Wuerstchen), [Paper](https://arxiv.org/abs/2306.00637). - **Cite as:** @misc{pernias2023wuerstchen, title={Wuerstchen: Efficient Pretraining of Text-to-Image Models}, author={Pablo Pernias and Dominic Rampas and Marc Aubreville}, year={2023}, eprint={2306.00637}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Environmental Impact **Würstchen v2** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 24602 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 2275.68 kg CO2 eq.

warp-ai/wuerstchen-prior-model-interpolated

warp-ai

2023-09-18T07:01:48Z

23

3

diffusers

[ "diffusers", "safetensors", "arxiv:2306.00637", "arxiv:1910.09700", "license:mit", "region:us" ]

null

2023-09-03T19:45:43Z

--- license: mit --- <img src="https://cdn-uploads.huggingface.co/production/uploads/634cb5eefb80cc6bcaf63c3e/i-DYpDHw8Pwiy7QBKZVR5.jpeg" width=1500> ## Würstchen - Overview Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the [paper](https://arxiv.org/abs/2306.00637)). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, allowing also cheaper and faster inference. ## Würstchen - Prior The Prior is what we refer to as "Stage C". It is the text-conditional model, operating in the small latent space that Stage A and Stage B encode images into. During inference, its job is to generate the image latents given text. These image latents are then sent to Stages A & B to decode the latents into pixel space. ### Prior - Model - Interpolated The interpolated model is our current best Prior (Stage C) checkpoint. It is an interpolation between our [base model](https://huggingface.co/warp-ai/wuerstchen-prior-model-base) and the [finetuned model](https://huggingface.co/warp-ai/wuerstchen-prior-model-finetuned). We created this interpolation because the finetuned model became too artistic and often only generates artistic images. The base model, however, usually is very photorealistic. As a result, we combined both by interpolating their weights by 50%, so the middle between the base and finetuned model (`0.5 * base_weights + 0.5 * finetuned_weights`). You can also interpolate the [base model](https://huggingface.co/warp-ai/wuerstchen-prior-model-base) and the [finetuned model](https://huggingface.co/warp-ai/wuerstchen-prior-model-finetuned) as you want and maybe find an interpolation that fits your needs better than this checkpoint. ### Image Sizes Würstchen was trained on image resolutions between 1024x1024 & 1536x1536. We sometimes also observe good outputs at resolutions like 1024x2048. Feel free to try it out. We also observed that the Prior (Stage C) adapts extremely fast to new resolutions. So finetuning it at 2048x2048 should be computationally cheap. <img src="https://cdn-uploads.huggingface.co/production/uploads/634cb5eefb80cc6bcaf63c3e/IfVsUDcP15OY-5wyLYKnQ.jpeg" width=1000> ## How to run This pipeline should be run together with https://huggingface.co/warp-ai/wuerstchen: ```py import torch from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS device = "cuda" dtype = torch.float16 num_images_per_prompt = 2 prior_pipeline = WuerstchenPriorPipeline.from_pretrained( "warp-ai/wuerstchen-prior", torch_dtype=dtype ).to(device) decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained( "warp-ai/wuerstchen", torch_dtype=dtype ).to(device) caption = "Anthropomorphic cat dressed as a fire fighter" negative_prompt = "" prior_output = prior_pipeline( prompt=caption, height=1024, width=1536, timesteps=DEFAULT_STAGE_C_TIMESTEPS, negative_prompt=negative_prompt, guidance_scale=4.0, num_images_per_prompt=num_images_per_prompt, ) decoder_output = decoder_pipeline( image_embeddings=prior_output.image_embeddings, prompt=caption, negative_prompt=negative_prompt, guidance_scale=0.0, output_type="pil", ).images ``` ## Model Details - **Developed by:** Pablo Pernias, Dominic Rampas - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** MIT - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a Diffusion model in the style of Stage C from the [Würstchen paper](https://arxiv.org/abs/2306.00637) that uses a fixed, pretrained text encoder ([CLIP ViT-bigG/14](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)). - **Resources for more information:** [GitHub Repository](https://github.com/dome272/Wuerstchen), [Paper](https://arxiv.org/abs/2306.00637). - **Cite as:** @misc{pernias2023wuerstchen, title={Wuerstchen: Efficient Pretraining of Text-to-Image Models}, author={Pablo Pernias and Dominic Rampas and Marc Aubreville}, year={2023}, eprint={2306.00637}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Environmental Impact **Würstchen v2** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 24602 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 2275.68 kg CO2 eq.

Abhay1212/news_demo

Abhay1212

2023-09-18T06:57:11Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-18T06:52:21Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: float16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0

ailoveydovey/anyqngmxrl

ailoveydovey

2023-09-18T06:54:06Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-09-18T06:39:38Z

--- license: creativeml-openrail-m ---

etri-xainlp/polyglot-ko-12.8b-instruct

etri-xainlp

2023-09-18T06:40:24Z

2,274

2

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-12T07:48:37Z

--- license: apache-2.0 language: - ko --- # polyglot-ko-12.8b-instruct This model is a fine-tuned version of [EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) on an instruction-following dataset(260k). ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - seed: 42 - distributed_type: multi-GPU(A100 80G) - num_devices: 8 - gradient_accumulation_steps: 64 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Inference ```python import torch from transformers import pipeline, AutoModelForCausalLM MODEL = 'etri-xainlp/polyglot-ko-12.8b-instruct' model = AutoModelForCausalLM.from_pretrained( MODEL, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(device=f"cuda", non_blocking=True) model.eval() pipe = pipeline( 'text-generation', model=model, tokenizer=MODEL, device=0 ) pipe.model.config.pad_token_id = pipe.model.config.eos_token_id def ask(x, context='', is_input_full=False): ans = pipe( f"### 질문: {x}\n\n### 맥락: {context}\n\n### 답변:" if context else f"### 질문: {x}\n\n### 답변:", do_sample=True, max_new_tokens=2048, temperature=0.9, top_p=0.9, return_full_text=False, eos_token_id=2, ) return ans[0]['generated_text'] while True: quit = input('prompt?: ') if quit == 'q': break else: generation = ask(quit) print("etri_ai:", generation) ``` ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ys7yoo/sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep9_ckpt

ys7yoo

2023-09-18T06:40:01Z

107

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "base_model:finetune:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T06:06:14Z

--- base_model: ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3 tags: - generated_from_trainer datasets: - klue model-index: - name: sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep9_ckpt results: [] ---  # sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep9_ckpt This model is a fine-tuned version of [ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3](https://huggingface.co/ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3250 - Mse: 0.3250 - Mae: 0.4166 - R2: 0.8512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 1.2084 | 1.0 | 183 | 0.5071 | 0.5071 | 0.5306 | 0.7678 | | 0.1515 | 2.0 | 366 | 0.3142 | 0.3142 | 0.4149 | 0.8561 | | 0.103 | 3.0 | 549 | 0.3284 | 0.3284 | 0.4150 | 0.8496 | | 0.0779 | 4.0 | 732 | 0.3306 | 0.3306 | 0.4184 | 0.8486 | | 0.0597 | 5.0 | 915 | 0.3219 | 0.3219 | 0.4098 | 0.8526 | | 0.0497 | 6.0 | 1098 | 0.3324 | 0.3324 | 0.4175 | 0.8478 | | 0.0407 | 7.0 | 1281 | 0.3114 | 0.3114 | 0.4119 | 0.8574 | | 0.0356 | 8.0 | 1464 | 0.3305 | 0.3305 | 0.4199 | 0.8486 | | 0.0327 | 9.0 | 1647 | 0.3250 | 0.3250 | 0.4166 | 0.8512 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3

checkiejan/prefix-paraphase-25-20-auto

checkiejan

2023-09-18T06:35:16Z

0

peft

[ "peft", "region:us" ]

null

2023-09-18T06:35:14Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

Archolic/SDArchitecture

Archolic

2023-09-18T06:23:40Z

0

null

[ "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-18T06:19:56Z

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true extra_gated_prompt: |- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ### Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new

kming

2023-09-18T06:07:31Z

162

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-xvector", "generated_from_trainer", "dataset:edinburghcstr/ami", "base_model:anton-l/wav2vec2-base-superb-sv", "base_model:finetune:anton-l/wav2vec2-base-superb-sv", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2023-09-15T09:23:26Z

--- license: apache-2.0 base_model: anton-l/wav2vec2-base-superb-sv tags: - generated_from_trainer datasets: - edinburghcstr/ami model-index: - name: wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-normalized results: [] ---  # wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-normalized This model is a fine-tuned version of [anton-l/wav2vec2-base-superb-sv](https://huggingface.co/anton-l/wav2vec2-base-superb-sv) on the ami 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

TamerAbdelaziz/distilbert-base-uncased-finetuned-sst2

TamerAbdelaziz

2023-09-18T05:56:36Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T05:36:37Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: TamerAbdelaziz/distilbert-base-uncased-finetuned-sst2 results: [] ---  # TamerAbdelaziz/distilbert-base-uncased-finetuned-sst2 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: 0.0592 - Validation Loss: 0.2958 - Train Accuracy: 0.9060 - Epoch: 2 ## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 12627, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 | |:----------:|:---------------:|:--------------:|:-----:| | 0.2123 | 0.2546 | 0.9014 | 0 | | 0.1023 | 0.2641 | 0.8899 | 1 | | 0.0592 | 0.2958 | 0.9060 | 2 | ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/mizumoto_yukari_idolmastercinderellagirls

CyberHarem

2023-09-18T05:50:54Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/mizumoto_yukari_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-18T05:29:09Z

--- license: mit datasets: - CyberHarem/mizumoto_yukari_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of mizumoto_yukari_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 7280, you need to download `7280/mizumoto_yukari_idolmastercinderellagirls.pt` as the embedding and `7280/mizumoto_yukari_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 7280**, with the score of 0.975. The trigger words are: 1. `mizumoto_yukari_idolmastercinderellagirls` 2. `brown_hair, long_hair, brown_eyes, blush, smile, bangs, open_mouth, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 7800 | 0.971 | [Download](7800/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-7800](7800/previews/pattern_1.png) | ![pattern_2-7800](7800/previews/pattern_2.png) | ![pattern_3-7800](7800/previews/pattern_3.png) | ![pattern_4-7800](7800/previews/pattern_4.png) | [<NSFW, click to see>](7800/previews/pattern_5.png) | [<NSFW, click to see>](7800/previews/pattern_6.png) | ![pattern_7-7800](7800/previews/pattern_7.png) | ![pattern_8-7800](7800/previews/pattern_8.png) | ![pattern_9-7800](7800/previews/pattern_9.png) | ![pattern_10-7800](7800/previews/pattern_10.png) | ![pattern_11-7800](7800/previews/pattern_11.png) | [<NSFW, click to see>](7800/previews/pattern_12.png) | [<NSFW, click to see>](7800/previews/pattern_13.png) | ![bikini-7800](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) | ![free-7800](7800/previews/free.png) | ![maid-7800](7800/previews/maid.png) | ![miko-7800](7800/previews/miko.png) | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) | ![suit-7800](7800/previews/suit.png) | ![yukata-7800](7800/previews/yukata.png) | | **7280** | **0.975** | [**Download**](7280/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-7280](7280/previews/pattern_1.png) | ![pattern_2-7280](7280/previews/pattern_2.png) | ![pattern_3-7280](7280/previews/pattern_3.png) | ![pattern_4-7280](7280/previews/pattern_4.png) | [<NSFW, click to see>](7280/previews/pattern_5.png) | [<NSFW, click to see>](7280/previews/pattern_6.png) | ![pattern_7-7280](7280/previews/pattern_7.png) | ![pattern_8-7280](7280/previews/pattern_8.png) | ![pattern_9-7280](7280/previews/pattern_9.png) | ![pattern_10-7280](7280/previews/pattern_10.png) | ![pattern_11-7280](7280/previews/pattern_11.png) | [<NSFW, click to see>](7280/previews/pattern_12.png) | [<NSFW, click to see>](7280/previews/pattern_13.png) | ![bikini-7280](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) | ![free-7280](7280/previews/free.png) | ![maid-7280](7280/previews/maid.png) | ![miko-7280](7280/previews/miko.png) | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) | ![suit-7280](7280/previews/suit.png) | ![yukata-7280](7280/previews/yukata.png) | | 6760 | 0.965 | [Download](6760/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-6760](6760/previews/pattern_1.png) | ![pattern_2-6760](6760/previews/pattern_2.png) | ![pattern_3-6760](6760/previews/pattern_3.png) | ![pattern_4-6760](6760/previews/pattern_4.png) | [<NSFW, click to see>](6760/previews/pattern_5.png) | [<NSFW, click to see>](6760/previews/pattern_6.png) | ![pattern_7-6760](6760/previews/pattern_7.png) | ![pattern_8-6760](6760/previews/pattern_8.png) | ![pattern_9-6760](6760/previews/pattern_9.png) | ![pattern_10-6760](6760/previews/pattern_10.png) | ![pattern_11-6760](6760/previews/pattern_11.png) | [<NSFW, click to see>](6760/previews/pattern_12.png) | [<NSFW, click to see>](6760/previews/pattern_13.png) | ![bikini-6760](6760/previews/bikini.png) | [<NSFW, click to see>](6760/previews/bondage.png) | ![free-6760](6760/previews/free.png) | ![maid-6760](6760/previews/maid.png) | ![miko-6760](6760/previews/miko.png) | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) | ![suit-6760](6760/previews/suit.png) | ![yukata-6760](6760/previews/yukata.png) | | 6240 | 0.964 | [Download](6240/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-6240](6240/previews/pattern_1.png) | ![pattern_2-6240](6240/previews/pattern_2.png) | ![pattern_3-6240](6240/previews/pattern_3.png) | ![pattern_4-6240](6240/previews/pattern_4.png) | [<NSFW, click to see>](6240/previews/pattern_5.png) | [<NSFW, click to see>](6240/previews/pattern_6.png) | ![pattern_7-6240](6240/previews/pattern_7.png) | ![pattern_8-6240](6240/previews/pattern_8.png) | ![pattern_9-6240](6240/previews/pattern_9.png) | ![pattern_10-6240](6240/previews/pattern_10.png) | ![pattern_11-6240](6240/previews/pattern_11.png) | [<NSFW, click to see>](6240/previews/pattern_12.png) | [<NSFW, click to see>](6240/previews/pattern_13.png) | ![bikini-6240](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | 5720 | 0.975 | [Download](5720/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-5720](5720/previews/pattern_1.png) | ![pattern_2-5720](5720/previews/pattern_2.png) | ![pattern_3-5720](5720/previews/pattern_3.png) | ![pattern_4-5720](5720/previews/pattern_4.png) | [<NSFW, click to see>](5720/previews/pattern_5.png) | [<NSFW, click to see>](5720/previews/pattern_6.png) | ![pattern_7-5720](5720/previews/pattern_7.png) | ![pattern_8-5720](5720/previews/pattern_8.png) | ![pattern_9-5720](5720/previews/pattern_9.png) | ![pattern_10-5720](5720/previews/pattern_10.png) | ![pattern_11-5720](5720/previews/pattern_11.png) | [<NSFW, click to see>](5720/previews/pattern_12.png) | [<NSFW, click to see>](5720/previews/pattern_13.png) | ![bikini-5720](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) | ![free-5720](5720/previews/free.png) | ![maid-5720](5720/previews/maid.png) | ![miko-5720](5720/previews/miko.png) | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) | ![suit-5720](5720/previews/suit.png) | ![yukata-5720](5720/previews/yukata.png) | | 5200 | 0.972 | [Download](5200/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-5200](5200/previews/pattern_1.png) | ![pattern_2-5200](5200/previews/pattern_2.png) | ![pattern_3-5200](5200/previews/pattern_3.png) | ![pattern_4-5200](5200/previews/pattern_4.png) | [<NSFW, click to see>](5200/previews/pattern_5.png) | [<NSFW, click to see>](5200/previews/pattern_6.png) | ![pattern_7-5200](5200/previews/pattern_7.png) | ![pattern_8-5200](5200/previews/pattern_8.png) | ![pattern_9-5200](5200/previews/pattern_9.png) | ![pattern_10-5200](5200/previews/pattern_10.png) | ![pattern_11-5200](5200/previews/pattern_11.png) | [<NSFW, click to see>](5200/previews/pattern_12.png) | [<NSFW, click to see>](5200/previews/pattern_13.png) | ![bikini-5200](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) | ![free-5200](5200/previews/free.png) | ![maid-5200](5200/previews/maid.png) | ![miko-5200](5200/previews/miko.png) | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) | ![suit-5200](5200/previews/suit.png) | ![yukata-5200](5200/previews/yukata.png) | | 4680 | 0.968 | [Download](4680/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-4680](4680/previews/pattern_1.png) | ![pattern_2-4680](4680/previews/pattern_2.png) | ![pattern_3-4680](4680/previews/pattern_3.png) | ![pattern_4-4680](4680/previews/pattern_4.png) | [<NSFW, click to see>](4680/previews/pattern_5.png) | [<NSFW, click to see>](4680/previews/pattern_6.png) | ![pattern_7-4680](4680/previews/pattern_7.png) | ![pattern_8-4680](4680/previews/pattern_8.png) | ![pattern_9-4680](4680/previews/pattern_9.png) | ![pattern_10-4680](4680/previews/pattern_10.png) | ![pattern_11-4680](4680/previews/pattern_11.png) | [<NSFW, click to see>](4680/previews/pattern_12.png) | [<NSFW, click to see>](4680/previews/pattern_13.png) | ![bikini-4680](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) | ![free-4680](4680/previews/free.png) | ![maid-4680](4680/previews/maid.png) | ![miko-4680](4680/previews/miko.png) | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) | ![suit-4680](4680/previews/suit.png) | ![yukata-4680](4680/previews/yukata.png) | | 4160 | 0.966 | [Download](4160/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-4160](4160/previews/pattern_1.png) | ![pattern_2-4160](4160/previews/pattern_2.png) | ![pattern_3-4160](4160/previews/pattern_3.png) | ![pattern_4-4160](4160/previews/pattern_4.png) | [<NSFW, click to see>](4160/previews/pattern_5.png) | [<NSFW, click to see>](4160/previews/pattern_6.png) | ![pattern_7-4160](4160/previews/pattern_7.png) | ![pattern_8-4160](4160/previews/pattern_8.png) | ![pattern_9-4160](4160/previews/pattern_9.png) | ![pattern_10-4160](4160/previews/pattern_10.png) | ![pattern_11-4160](4160/previews/pattern_11.png) | [<NSFW, click to see>](4160/previews/pattern_12.png) | [<NSFW, click to see>](4160/previews/pattern_13.png) | ![bikini-4160](4160/previews/bikini.png) | [<NSFW, click to see>](4160/previews/bondage.png) | ![free-4160](4160/previews/free.png) | ![maid-4160](4160/previews/maid.png) | ![miko-4160](4160/previews/miko.png) | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) | ![suit-4160](4160/previews/suit.png) | ![yukata-4160](4160/previews/yukata.png) | | 3640 | 0.969 | [Download](3640/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-3640](3640/previews/pattern_1.png) | ![pattern_2-3640](3640/previews/pattern_2.png) | ![pattern_3-3640](3640/previews/pattern_3.png) | ![pattern_4-3640](3640/previews/pattern_4.png) | [<NSFW, click to see>](3640/previews/pattern_5.png) | [<NSFW, click to see>](3640/previews/pattern_6.png) | ![pattern_7-3640](3640/previews/pattern_7.png) | ![pattern_8-3640](3640/previews/pattern_8.png) | ![pattern_9-3640](3640/previews/pattern_9.png) | ![pattern_10-3640](3640/previews/pattern_10.png) | ![pattern_11-3640](3640/previews/pattern_11.png) | [<NSFW, click to see>](3640/previews/pattern_12.png) | [<NSFW, click to see>](3640/previews/pattern_13.png) | ![bikini-3640](3640/previews/bikini.png) | [<NSFW, click to see>](3640/previews/bondage.png) | ![free-3640](3640/previews/free.png) | ![maid-3640](3640/previews/maid.png) | ![miko-3640](3640/previews/miko.png) | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) | ![suit-3640](3640/previews/suit.png) | ![yukata-3640](3640/previews/yukata.png) | | 3120 | 0.967 | [Download](3120/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-3120](3120/previews/pattern_1.png) | ![pattern_2-3120](3120/previews/pattern_2.png) | ![pattern_3-3120](3120/previews/pattern_3.png) | ![pattern_4-3120](3120/previews/pattern_4.png) | [<NSFW, click to see>](3120/previews/pattern_5.png) | [<NSFW, click to see>](3120/previews/pattern_6.png) | ![pattern_7-3120](3120/previews/pattern_7.png) | ![pattern_8-3120](3120/previews/pattern_8.png) | ![pattern_9-3120](3120/previews/pattern_9.png) | ![pattern_10-3120](3120/previews/pattern_10.png) | ![pattern_11-3120](3120/previews/pattern_11.png) | [<NSFW, click to see>](3120/previews/pattern_12.png) | [<NSFW, click to see>](3120/previews/pattern_13.png) | ![bikini-3120](3120/previews/bikini.png) | [<NSFW, click to see>](3120/previews/bondage.png) | ![free-3120](3120/previews/free.png) | ![maid-3120](3120/previews/maid.png) | ![miko-3120](3120/previews/miko.png) | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) | ![suit-3120](3120/previews/suit.png) | ![yukata-3120](3120/previews/yukata.png) | | 2600 | 0.967 | [Download](2600/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-2600](2600/previews/pattern_1.png) | ![pattern_2-2600](2600/previews/pattern_2.png) | ![pattern_3-2600](2600/previews/pattern_3.png) | ![pattern_4-2600](2600/previews/pattern_4.png) | [<NSFW, click to see>](2600/previews/pattern_5.png) | [<NSFW, click to see>](2600/previews/pattern_6.png) | ![pattern_7-2600](2600/previews/pattern_7.png) | ![pattern_8-2600](2600/previews/pattern_8.png) | ![pattern_9-2600](2600/previews/pattern_9.png) | ![pattern_10-2600](2600/previews/pattern_10.png) | ![pattern_11-2600](2600/previews/pattern_11.png) | [<NSFW, click to see>](2600/previews/pattern_12.png) | [<NSFW, click to see>](2600/previews/pattern_13.png) | ![bikini-2600](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) | ![free-2600](2600/previews/free.png) | ![maid-2600](2600/previews/maid.png) | ![miko-2600](2600/previews/miko.png) | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) | ![suit-2600](2600/previews/suit.png) | ![yukata-2600](2600/previews/yukata.png) | | 2080 | 0.960 | [Download](2080/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-2080](2080/previews/pattern_1.png) | ![pattern_2-2080](2080/previews/pattern_2.png) | ![pattern_3-2080](2080/previews/pattern_3.png) | ![pattern_4-2080](2080/previews/pattern_4.png) | [<NSFW, click to see>](2080/previews/pattern_5.png) | [<NSFW, click to see>](2080/previews/pattern_6.png) | ![pattern_7-2080](2080/previews/pattern_7.png) | ![pattern_8-2080](2080/previews/pattern_8.png) | ![pattern_9-2080](2080/previews/pattern_9.png) | ![pattern_10-2080](2080/previews/pattern_10.png) | ![pattern_11-2080](2080/previews/pattern_11.png) | [<NSFW, click to see>](2080/previews/pattern_12.png) | [<NSFW, click to see>](2080/previews/pattern_13.png) | ![bikini-2080](2080/previews/bikini.png) | [<NSFW, click to see>](2080/previews/bondage.png) | ![free-2080](2080/previews/free.png) | ![maid-2080](2080/previews/maid.png) | ![miko-2080](2080/previews/miko.png) | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) | ![suit-2080](2080/previews/suit.png) | ![yukata-2080](2080/previews/yukata.png) | | 1560 | 0.961 | [Download](1560/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-1560](1560/previews/pattern_1.png) | ![pattern_2-1560](1560/previews/pattern_2.png) | ![pattern_3-1560](1560/previews/pattern_3.png) | ![pattern_4-1560](1560/previews/pattern_4.png) | [<NSFW, click to see>](1560/previews/pattern_5.png) | [<NSFW, click to see>](1560/previews/pattern_6.png) | ![pattern_7-1560](1560/previews/pattern_7.png) | ![pattern_8-1560](1560/previews/pattern_8.png) | ![pattern_9-1560](1560/previews/pattern_9.png) | ![pattern_10-1560](1560/previews/pattern_10.png) | ![pattern_11-1560](1560/previews/pattern_11.png) | [<NSFW, click to see>](1560/previews/pattern_12.png) | [<NSFW, click to see>](1560/previews/pattern_13.png) | ![bikini-1560](1560/previews/bikini.png) | [<NSFW, click to see>](1560/previews/bondage.png) | ![free-1560](1560/previews/free.png) | ![maid-1560](1560/previews/maid.png) | ![miko-1560](1560/previews/miko.png) | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) | ![suit-1560](1560/previews/suit.png) | ![yukata-1560](1560/previews/yukata.png) | | 1040 | 0.960 | [Download](1040/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-1040](1040/previews/pattern_1.png) | ![pattern_2-1040](1040/previews/pattern_2.png) | ![pattern_3-1040](1040/previews/pattern_3.png) | ![pattern_4-1040](1040/previews/pattern_4.png) | [<NSFW, click to see>](1040/previews/pattern_5.png) | [<NSFW, click to see>](1040/previews/pattern_6.png) | ![pattern_7-1040](1040/previews/pattern_7.png) | ![pattern_8-1040](1040/previews/pattern_8.png) | ![pattern_9-1040](1040/previews/pattern_9.png) | ![pattern_10-1040](1040/previews/pattern_10.png) | ![pattern_11-1040](1040/previews/pattern_11.png) | [<NSFW, click to see>](1040/previews/pattern_12.png) | [<NSFW, click to see>](1040/previews/pattern_13.png) | ![bikini-1040](1040/previews/bikini.png) | [<NSFW, click to see>](1040/previews/bondage.png) | ![free-1040](1040/previews/free.png) | ![maid-1040](1040/previews/maid.png) | ![miko-1040](1040/previews/miko.png) | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) | ![suit-1040](1040/previews/suit.png) | ![yukata-1040](1040/previews/yukata.png) | | 520 | 0.958 | [Download](520/mizumoto_yukari_idolmastercinderellagirls.zip) | ![pattern_1-520](520/previews/pattern_1.png) | ![pattern_2-520](520/previews/pattern_2.png) | ![pattern_3-520](520/previews/pattern_3.png) | ![pattern_4-520](520/previews/pattern_4.png) | [<NSFW, click to see>](520/previews/pattern_5.png) | [<NSFW, click to see>](520/previews/pattern_6.png) | ![pattern_7-520](520/previews/pattern_7.png) | ![pattern_8-520](520/previews/pattern_8.png) | ![pattern_9-520](520/previews/pattern_9.png) | ![pattern_10-520](520/previews/pattern_10.png) | ![pattern_11-520](520/previews/pattern_11.png) | [<NSFW, click to see>](520/previews/pattern_12.png) | [<NSFW, click to see>](520/previews/pattern_13.png) | ![bikini-520](520/previews/bikini.png) | [<NSFW, click to see>](520/previews/bondage.png) | ![free-520](520/previews/free.png) | ![maid-520](520/previews/maid.png) | ![miko-520](520/previews/miko.png) | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) | ![suit-520](520/previews/suit.png) | ![yukata-520](520/previews/yukata.png) |

xtrbase/positive-llm

xtrbase

2023-09-18T05:39:21Z

3

0

peft

[ "peft", "region:us" ]

null

2023-09-18T05:38:57Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

GlennQuagmire/ER-MIX

GlennQuagmire

2023-09-18T05:26:46Z

0

null

[ "license:other", "region:us" ]

null

2023-08-12T02:24:36Z

--- license: other --- ## I own nothing of this model, this is solely for *caching* purposes Pay a visit to [author](https://space.bilibili.com/49512651) and leave your endorsement! # GIGGITY

GlennQuagmire/DisillusionMix3

GlennQuagmire

2023-09-18T05:21:26Z

0

3

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-04-26T16:18:36Z

--- license: creativeml-openrail-m --- # I own nothing of this model, All credit goes to original author,be sure to endorse his/her models on CivitAI! [Click Me](https://civitai.com/user/Rerorerorero/models) --- I start this repo to cache this model **giggity**

Drello/Test

Drello

2023-09-18T05:20:03Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2023-09-18T05:20:03Z

--- license: bigscience-openrail-m ---

IXLFreaKz/GawrGura

IXLFreaKz

2023-09-18T05:14:24Z

0

null

[ "license:cc-by-nc-nd-4.0", "region:us" ]

null

2023-09-18T05:12:28Z

--- license: cc-by-nc-nd-4.0 ---

abhiShek1061/imdb-classification

abhiShek1061

2023-09-18T05:14:00Z

103

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T04:42:42Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: imdb-classification results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93228 ---  # imdb-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2332 - Accuracy: 0.9323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2233 | 1.0 | 1563 | 0.2479 | 0.9146 | | 0.149 | 2.0 | 3126 | 0.2332 | 0.9323 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

Panchovix/Synthia-70B-v1.2b-safetensors

Panchovix

2023-09-18T05:13:13Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-18T03:09:27Z

--- license: llama2 --- Safetensors conversion of Synthia-70B-v1.2b (https://huggingface.co/migtissera/Synthia-70B-v1.2b). Can be used directly on transformers, or to be used to convert/quant models with exllamav2.

ailabturkiye/sempatuco

ailabturkiye

2023-09-18T05:04:01Z

0

null

[ "tr", "license:openrail", "region:us" ]

null

2023-08-09T13:54:30Z

--- license: openrail language: - tr ---

ShivamMangale/XLM-Roberta-base-finetuned-squad-squad-first

ShivamMangale

2023-09-18T04:51:49Z

12

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-09-18T01:31:54Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-finetuned-squad-squad-first results: [] ---  # XLM-Roberta-base-finetuned-squad-squad-first This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

furquan/opt_2_7_b_prompt_tuned_sentiment_analysis

furquan

2023-09-18T04:51:43Z

14

0

transformers

[ "transformers", "pytorch", "opt", "feature-extraction", "text-generation", "custom_code", "dataset:SetFit/sst5", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-18T03:45:11Z

--- datasets: - SetFit/sst5 pipeline_tag: text-generation widget: - text: 'The weather is lovely today! ' ---

pkduongsu/bert-finetuned-covidqadeepset

pkduongsu

2023-09-18T04:42:30Z

116

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-09-18T04:22:57Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - covid_qa_deepset model-index: - name: bert-finetuned-covidqa results: [] ---  # bert-finetuned-covidqa This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the covid_qa_deepset 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 - 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

alayaran/bodo-pos-gpt2-fine-tune

alayaran

2023-09-18T04:37:43Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "token-classification", "br", "dataset:alayaran/bodo-pos-conll", "dataset:alayaran/bodo-monolingual-dataset", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

token-classification

2023-09-18T03:53:21Z

--- license: mit datasets: - alayaran/bodo-pos-conll - alayaran/bodo-monolingual-dataset language: - br metrics: - accuracy - seqeval widget: - text: "बर’फोरा मिथिंगा सिबियारि ।" example_title: "Example 1" - text: "गथ’फोर त्रेफिकिं खालामनायाबो भारताव मोनसे गोब्राब जेंना जागासिनो ।" example_title: "Example 2" - text: "गोबां बिबांफोरनि सोरकारनि फोरमानजों मदद होजानानै , बिमायारि आरो गथ’ देहाया देहानि मिनिसत्रिफोराव गोनांसिन जाबाय ।" example_title: "Example 3" ---

CyberHarem/yorita_yoshino_idolmastercinderellagirls

CyberHarem

2023-09-18T04:34:06Z

0

2

null

[ "art", "text-to-image", "dataset:CyberHarem/yorita_yoshino_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-18T04:11:00Z

--- license: mit datasets: - CyberHarem/yorita_yoshino_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of yorita_yoshino_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 7840, you need to download `7840/yorita_yoshino_idolmastercinderellagirls.pt` as the embedding and `7840/yorita_yoshino_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 7840**, with the score of 0.849. The trigger words are: 1. `yorita_yoshino_idolmastercinderellagirls` 2. `brown_eyes, brown_hair, long_hair, bangs, blush, bow, hair_bow, smile, very_long_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 8400 | 0.765 | [Download](8400/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-8400](8400/previews/pattern_1.png) | ![pattern_2-8400](8400/previews/pattern_2.png) | ![pattern_3-8400](8400/previews/pattern_3.png) | ![pattern_4-8400](8400/previews/pattern_4.png) | ![pattern_5-8400](8400/previews/pattern_5.png) | ![pattern_6-8400](8400/previews/pattern_6.png) | ![pattern_7-8400](8400/previews/pattern_7.png) | [<NSFW, click to see>](8400/previews/pattern_8.png) | [<NSFW, click to see>](8400/previews/pattern_9.png) | ![pattern_10-8400](8400/previews/pattern_10.png) | ![pattern_11-8400](8400/previews/pattern_11.png) | ![pattern_12-8400](8400/previews/pattern_12.png) | ![pattern_13-8400](8400/previews/pattern_13.png) | ![pattern_14-8400](8400/previews/pattern_14.png) | [<NSFW, click to see>](8400/previews/bikini.png) | [<NSFW, click to see>](8400/previews/bondage.png) | ![free-8400](8400/previews/free.png) | ![maid-8400](8400/previews/maid.png) | ![miko-8400](8400/previews/miko.png) | [<NSFW, click to see>](8400/previews/nude.png) | [<NSFW, click to see>](8400/previews/nude2.png) | ![suit-8400](8400/previews/suit.png) | ![yukata-8400](8400/previews/yukata.png) | | **7840** | **0.849** | [**Download**](7840/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-7840](7840/previews/pattern_1.png) | ![pattern_2-7840](7840/previews/pattern_2.png) | ![pattern_3-7840](7840/previews/pattern_3.png) | ![pattern_4-7840](7840/previews/pattern_4.png) | ![pattern_5-7840](7840/previews/pattern_5.png) | ![pattern_6-7840](7840/previews/pattern_6.png) | ![pattern_7-7840](7840/previews/pattern_7.png) | [<NSFW, click to see>](7840/previews/pattern_8.png) | [<NSFW, click to see>](7840/previews/pattern_9.png) | ![pattern_10-7840](7840/previews/pattern_10.png) | ![pattern_11-7840](7840/previews/pattern_11.png) | ![pattern_12-7840](7840/previews/pattern_12.png) | ![pattern_13-7840](7840/previews/pattern_13.png) | ![pattern_14-7840](7840/previews/pattern_14.png) | [<NSFW, click to see>](7840/previews/bikini.png) | [<NSFW, click to see>](7840/previews/bondage.png) | ![free-7840](7840/previews/free.png) | ![maid-7840](7840/previews/maid.png) | ![miko-7840](7840/previews/miko.png) | [<NSFW, click to see>](7840/previews/nude.png) | [<NSFW, click to see>](7840/previews/nude2.png) | ![suit-7840](7840/previews/suit.png) | ![yukata-7840](7840/previews/yukata.png) | | 7280 | 0.824 | [Download](7280/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-7280](7280/previews/pattern_1.png) | ![pattern_2-7280](7280/previews/pattern_2.png) | ![pattern_3-7280](7280/previews/pattern_3.png) | ![pattern_4-7280](7280/previews/pattern_4.png) | ![pattern_5-7280](7280/previews/pattern_5.png) | ![pattern_6-7280](7280/previews/pattern_6.png) | ![pattern_7-7280](7280/previews/pattern_7.png) | [<NSFW, click to see>](7280/previews/pattern_8.png) | [<NSFW, click to see>](7280/previews/pattern_9.png) | ![pattern_10-7280](7280/previews/pattern_10.png) | ![pattern_11-7280](7280/previews/pattern_11.png) | ![pattern_12-7280](7280/previews/pattern_12.png) | ![pattern_13-7280](7280/previews/pattern_13.png) | ![pattern_14-7280](7280/previews/pattern_14.png) | [<NSFW, click to see>](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) | ![free-7280](7280/previews/free.png) | ![maid-7280](7280/previews/maid.png) | ![miko-7280](7280/previews/miko.png) | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) | ![suit-7280](7280/previews/suit.png) | ![yukata-7280](7280/previews/yukata.png) | | 6720 | 0.779 | [Download](6720/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-6720](6720/previews/pattern_1.png) | ![pattern_2-6720](6720/previews/pattern_2.png) | ![pattern_3-6720](6720/previews/pattern_3.png) | ![pattern_4-6720](6720/previews/pattern_4.png) | ![pattern_5-6720](6720/previews/pattern_5.png) | ![pattern_6-6720](6720/previews/pattern_6.png) | ![pattern_7-6720](6720/previews/pattern_7.png) | [<NSFW, click to see>](6720/previews/pattern_8.png) | [<NSFW, click to see>](6720/previews/pattern_9.png) | ![pattern_10-6720](6720/previews/pattern_10.png) | ![pattern_11-6720](6720/previews/pattern_11.png) | ![pattern_12-6720](6720/previews/pattern_12.png) | ![pattern_13-6720](6720/previews/pattern_13.png) | ![pattern_14-6720](6720/previews/pattern_14.png) | [<NSFW, click to see>](6720/previews/bikini.png) | [<NSFW, click to see>](6720/previews/bondage.png) | ![free-6720](6720/previews/free.png) | ![maid-6720](6720/previews/maid.png) | ![miko-6720](6720/previews/miko.png) | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) | ![suit-6720](6720/previews/suit.png) | ![yukata-6720](6720/previews/yukata.png) | | 6160 | 0.773 | [Download](6160/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-6160](6160/previews/pattern_1.png) | ![pattern_2-6160](6160/previews/pattern_2.png) | ![pattern_3-6160](6160/previews/pattern_3.png) | ![pattern_4-6160](6160/previews/pattern_4.png) | ![pattern_5-6160](6160/previews/pattern_5.png) | ![pattern_6-6160](6160/previews/pattern_6.png) | ![pattern_7-6160](6160/previews/pattern_7.png) | [<NSFW, click to see>](6160/previews/pattern_8.png) | [<NSFW, click to see>](6160/previews/pattern_9.png) | ![pattern_10-6160](6160/previews/pattern_10.png) | ![pattern_11-6160](6160/previews/pattern_11.png) | ![pattern_12-6160](6160/previews/pattern_12.png) | ![pattern_13-6160](6160/previews/pattern_13.png) | ![pattern_14-6160](6160/previews/pattern_14.png) | [<NSFW, click to see>](6160/previews/bikini.png) | [<NSFW, click to see>](6160/previews/bondage.png) | ![free-6160](6160/previews/free.png) | ![maid-6160](6160/previews/maid.png) | ![miko-6160](6160/previews/miko.png) | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) | ![suit-6160](6160/previews/suit.png) | ![yukata-6160](6160/previews/yukata.png) | | 5600 | 0.808 | [Download](5600/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-5600](5600/previews/pattern_1.png) | ![pattern_2-5600](5600/previews/pattern_2.png) | ![pattern_3-5600](5600/previews/pattern_3.png) | ![pattern_4-5600](5600/previews/pattern_4.png) | ![pattern_5-5600](5600/previews/pattern_5.png) | ![pattern_6-5600](5600/previews/pattern_6.png) | ![pattern_7-5600](5600/previews/pattern_7.png) | [<NSFW, click to see>](5600/previews/pattern_8.png) | [<NSFW, click to see>](5600/previews/pattern_9.png) | ![pattern_10-5600](5600/previews/pattern_10.png) | ![pattern_11-5600](5600/previews/pattern_11.png) | ![pattern_12-5600](5600/previews/pattern_12.png) | ![pattern_13-5600](5600/previews/pattern_13.png) | ![pattern_14-5600](5600/previews/pattern_14.png) | [<NSFW, click to see>](5600/previews/bikini.png) | [<NSFW, click to see>](5600/previews/bondage.png) | ![free-5600](5600/previews/free.png) | ![maid-5600](5600/previews/maid.png) | ![miko-5600](5600/previews/miko.png) | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) | ![suit-5600](5600/previews/suit.png) | ![yukata-5600](5600/previews/yukata.png) | | 5040 | 0.822 | [Download](5040/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-5040](5040/previews/pattern_1.png) | ![pattern_2-5040](5040/previews/pattern_2.png) | ![pattern_3-5040](5040/previews/pattern_3.png) | ![pattern_4-5040](5040/previews/pattern_4.png) | ![pattern_5-5040](5040/previews/pattern_5.png) | ![pattern_6-5040](5040/previews/pattern_6.png) | ![pattern_7-5040](5040/previews/pattern_7.png) | [<NSFW, click to see>](5040/previews/pattern_8.png) | [<NSFW, click to see>](5040/previews/pattern_9.png) | ![pattern_10-5040](5040/previews/pattern_10.png) | ![pattern_11-5040](5040/previews/pattern_11.png) | ![pattern_12-5040](5040/previews/pattern_12.png) | ![pattern_13-5040](5040/previews/pattern_13.png) | ![pattern_14-5040](5040/previews/pattern_14.png) | [<NSFW, click to see>](5040/previews/bikini.png) | [<NSFW, click to see>](5040/previews/bondage.png) | ![free-5040](5040/previews/free.png) | ![maid-5040](5040/previews/maid.png) | ![miko-5040](5040/previews/miko.png) | [<NSFW, click to see>](5040/previews/nude.png) | [<NSFW, click to see>](5040/previews/nude2.png) | ![suit-5040](5040/previews/suit.png) | ![yukata-5040](5040/previews/yukata.png) | | 4480 | 0.750 | [Download](4480/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-4480](4480/previews/pattern_1.png) | ![pattern_2-4480](4480/previews/pattern_2.png) | ![pattern_3-4480](4480/previews/pattern_3.png) | ![pattern_4-4480](4480/previews/pattern_4.png) | ![pattern_5-4480](4480/previews/pattern_5.png) | ![pattern_6-4480](4480/previews/pattern_6.png) | ![pattern_7-4480](4480/previews/pattern_7.png) | [<NSFW, click to see>](4480/previews/pattern_8.png) | [<NSFW, click to see>](4480/previews/pattern_9.png) | ![pattern_10-4480](4480/previews/pattern_10.png) | ![pattern_11-4480](4480/previews/pattern_11.png) | ![pattern_12-4480](4480/previews/pattern_12.png) | ![pattern_13-4480](4480/previews/pattern_13.png) | ![pattern_14-4480](4480/previews/pattern_14.png) | [<NSFW, click to see>](4480/previews/bikini.png) | [<NSFW, click to see>](4480/previews/bondage.png) | ![free-4480](4480/previews/free.png) | ![maid-4480](4480/previews/maid.png) | ![miko-4480](4480/previews/miko.png) | [<NSFW, click to see>](4480/previews/nude.png) | [<NSFW, click to see>](4480/previews/nude2.png) | ![suit-4480](4480/previews/suit.png) | ![yukata-4480](4480/previews/yukata.png) | | 3920 | 0.777 | [Download](3920/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-3920](3920/previews/pattern_1.png) | ![pattern_2-3920](3920/previews/pattern_2.png) | ![pattern_3-3920](3920/previews/pattern_3.png) | ![pattern_4-3920](3920/previews/pattern_4.png) | ![pattern_5-3920](3920/previews/pattern_5.png) | ![pattern_6-3920](3920/previews/pattern_6.png) | ![pattern_7-3920](3920/previews/pattern_7.png) | [<NSFW, click to see>](3920/previews/pattern_8.png) | [<NSFW, click to see>](3920/previews/pattern_9.png) | ![pattern_10-3920](3920/previews/pattern_10.png) | ![pattern_11-3920](3920/previews/pattern_11.png) | ![pattern_12-3920](3920/previews/pattern_12.png) | ![pattern_13-3920](3920/previews/pattern_13.png) | ![pattern_14-3920](3920/previews/pattern_14.png) | [<NSFW, click to see>](3920/previews/bikini.png) | [<NSFW, click to see>](3920/previews/bondage.png) | ![free-3920](3920/previews/free.png) | ![maid-3920](3920/previews/maid.png) | ![miko-3920](3920/previews/miko.png) | [<NSFW, click to see>](3920/previews/nude.png) | [<NSFW, click to see>](3920/previews/nude2.png) | ![suit-3920](3920/previews/suit.png) | ![yukata-3920](3920/previews/yukata.png) | | 3360 | 0.786 | [Download](3360/yorita_yoshino_idolmastercinderellagirls.zip) | 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![pattern_12-2240](2240/previews/pattern_12.png) | ![pattern_13-2240](2240/previews/pattern_13.png) | ![pattern_14-2240](2240/previews/pattern_14.png) | [<NSFW, click to see>](2240/previews/bikini.png) | [<NSFW, click to see>](2240/previews/bondage.png) | ![free-2240](2240/previews/free.png) | ![maid-2240](2240/previews/maid.png) | ![miko-2240](2240/previews/miko.png) | [<NSFW, click to see>](2240/previews/nude.png) | [<NSFW, click to see>](2240/previews/nude2.png) | ![suit-2240](2240/previews/suit.png) | ![yukata-2240](2240/previews/yukata.png) | | 1680 | 0.719 | [Download](1680/yorita_yoshino_idolmastercinderellagirls.zip) | ![pattern_1-1680](1680/previews/pattern_1.png) | ![pattern_2-1680](1680/previews/pattern_2.png) | ![pattern_3-1680](1680/previews/pattern_3.png) | ![pattern_4-1680](1680/previews/pattern_4.png) | ![pattern_5-1680](1680/previews/pattern_5.png) | ![pattern_6-1680](1680/previews/pattern_6.png) | ![pattern_7-1680](1680/previews/pattern_7.png) | [<NSFW, click to 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huyen89/Reinforce-CartPole-v1

huyen89

2023-09-18T04:21:03Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-09-18T04:20:58Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 223.40 +/- 21.61 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

ys7yoo/sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep7_ckpt

ys7yoo

2023-09-18T04:19:15Z

105

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "base_model:finetune:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T03:57:43Z

--- base_model: ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3 tags: - generated_from_trainer datasets: - klue model-index: - name: sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep7_ckpt results: [] ---  # sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep7_ckpt This model is a fine-tuned version of [ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3](https://huggingface.co/ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3202 - Mse: 0.3202 - Mae: 0.4109 - R2: 0.8534 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 1.0857 | 1.0 | 183 | 0.4208 | 0.4208 | 0.4787 | 0.8073 | | 0.1397 | 2.0 | 366 | 0.3135 | 0.3135 | 0.4191 | 0.8565 | | 0.0989 | 3.0 | 549 | 0.3468 | 0.3468 | 0.4261 | 0.8412 | | 0.0757 | 4.0 | 732 | 0.3006 | 0.3006 | 0.3959 | 0.8623 | | 0.0601 | 5.0 | 915 | 0.4034 | 0.4034 | 0.4669 | 0.8153 | | 0.0502 | 6.0 | 1098 | 0.3357 | 0.3357 | 0.4221 | 0.8463 | | 0.0429 | 7.0 | 1281 | 0.3202 | 0.3202 | 0.4109 | 0.8534 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3

m-aliabbas1/erc_question_big_model

m-aliabbas1

2023-09-18T04:01:58Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-09-18T04:01:12Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # m-aliabbas1/erc_question_big_model This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("m-aliabbas1/erc_question_big_model") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

dsmsb/16_combo_webscrap_1709_v2_reduce_others

dsmsb

2023-09-18T04:00:21Z

7

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T01:47:02Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: 16_combo_webscrap_1709_v2_reduce_others results: [] ---  # 16_combo_webscrap_1709_v2_reduce_others This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1501 - Accuracy: 0.9636 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 363 | 1.0481 | 0.7263 | | 1.5287 | 2.0 | 726 | 0.5613 | 0.8655 | | 0.6856 | 3.0 | 1089 | 0.3666 | 0.9121 | | 0.6856 | 4.0 | 1452 | 0.2880 | 0.9284 | | 0.4313 | 5.0 | 1815 | 0.2187 | 0.9464 | | 0.3097 | 6.0 | 2178 | 0.1992 | 0.9505 | | 0.2454 | 7.0 | 2541 | 0.1627 | 0.9598 | | 0.2454 | 8.0 | 2904 | 0.1501 | 0.9636 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

axelit64/image_classification

axelit64

2023-09-18T03:56:43Z

229

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-18T03:07:32Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: image_classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.575 ---  # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3340 - Accuracy: 0.575 ## 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 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 1.5156 | 0.45 | | No log | 2.0 | 80 | 1.4200 | 0.4562 | | No log | 3.0 | 120 | 1.3790 | 0.5 | | No log | 4.0 | 160 | 1.2859 | 0.525 | | No log | 5.0 | 200 | 1.2592 | 0.5125 | | No log | 6.0 | 240 | 1.3145 | 0.55 | | No log | 7.0 | 280 | 1.3267 | 0.4813 | | No log | 8.0 | 320 | 1.3288 | 0.5 | | No log | 9.0 | 360 | 1.3073 | 0.5 | | No log | 10.0 | 400 | 1.3066 | 0.5188 | | No log | 11.0 | 440 | 1.2691 | 0.5563 | | No log | 12.0 | 480 | 1.2809 | 0.5437 | | 0.876 | 13.0 | 520 | 1.2963 | 0.5625 | | 0.876 | 14.0 | 560 | 1.2965 | 0.5312 | | 0.876 | 15.0 | 600 | 1.3542 | 0.5188 | | 0.876 | 16.0 | 640 | 1.3489 | 0.5125 | | 0.876 | 17.0 | 680 | 1.3146 | 0.5687 | | 0.876 | 18.0 | 720 | 1.2442 | 0.575 | | 0.876 | 19.0 | 760 | 1.3497 | 0.575 | | 0.876 | 20.0 | 800 | 1.3316 | 0.5437 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

handi88/FastJobs-Visual_Emotions_Analysis

handi88

2023-09-18T03:55:02Z

0

null

[ "generated_from_trainer", "dataset:FastJobs/Visual_Emotional_Analysis", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "region:us" ]

null

2023-09-18T03:42:58Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - FastJobs/Visual_Emotional_Analysis metrics: - accuracy - precision - f1 model-index: - name: emotion_classification results: - task: name: Image Classification type: image-classification dataset: name: FastJobs/Visual_Emotional_Analysis type: FastJobs/Visual_Emotional_Analysis config: FastJobs--Visual_Emotional_Analysis split: train args: FastJobs--Visual_Emotional_Analysis metrics: - name: Accuracy type: accuracy value: 0.66875 - name: Precision type: precision value: 0.7104119480438352 - name: F1 type: f1 value: 0.6712765732314218 --- # Emotion Classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset. In theory, the accuracy for a random guess on this dataset is 0.125 (8 labels and you need to choose one). It achieves the following results on the evaluation set: - Loss: 1.0511 - Accuracy: 0.6687 - Precision: 0.7104 - F1: 0.6713 ## Model description The Vision Transformer base version trained on ImageNet-21K released by Google. Further details can be found on their [repo](https://huggingface.co/google/vit-base-patch16-224-in21k). ## Training and evaluation data ### Data Split Trained on [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset. Used a 4:1 ratio for training and development sets and a random seed of 42. Also used a seed of 42 for batching the data, completely unrelated lol. ### Pre-processing Augmentation The main pre-processing phase for both training and evaluation includes: - Bilinear interpolation to resize the image to (224, 224, 3) because it uses ImageNet images to train the original model - Normalizing images using a mean and standard deviation of [0.5, 0.5, 0.5] just like the original model Other than the aforementioned pre-processing, the training set was augmented using: - Random horizontal & vertical flip - Color jitter - Random resized crop ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: cosine_with_restarts - lr_scheduler_warmup_steps: 150 - num_epochs: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| | 2.079 | 1.0 | 10 | 2.0895 | 0.0563 | 0.0604 | 0.0521 | | 2.0789 | 2.0 | 20 | 2.0851 | 0.0563 | 0.0602 | 0.0529 | | 2.0717 | 3.0 | 30 | 2.0773 | 0.0813 | 0.0858 | 0.0783 | | 2.0613 | 4.0 | 40 | 2.0658 | 0.125 | 0.1997 | 0.1333 | | 2.0445 | 5.0 | 50 | 2.0483 | 0.1875 | 0.2569 | 0.1934 | | 2.0176 | 6.0 | 60 | 2.0206 | 0.2313 | 0.2692 | 0.2384 | | 1.9894 | 7.0 | 70 | 1.9763 | 0.3063 | 0.3033 | 0.2983 | | 1.9232 | 8.0 | 80 | 1.8912 | 0.3625 | 0.3307 | 0.3194 | | 1.8256 | 9.0 | 90 | 1.7775 | 0.4062 | 0.3531 | 0.3600 | | 1.732 | 10.0 | 100 | 1.6580 | 0.4688 | 0.4158 | 0.4133 | | 1.6406 | 11.0 | 110 | 1.5597 | 0.5 | 0.4358 | 0.4370 | | 1.5584 | 12.0 | 120 | 1.4855 | 0.5125 | 0.4792 | 0.4784 | | 1.4898 | 13.0 | 130 | 1.4248 | 0.5437 | 0.5011 | 0.5098 | | 1.4216 | 14.0 | 140 | 1.3692 | 0.5687 | 0.5255 | 0.5289 | | 1.3701 | 15.0 | 150 | 1.3158 | 0.5687 | 0.5346 | 0.5360 | | 1.3438 | 16.0 | 160 | 1.2842 | 0.5437 | 0.5451 | 0.5098 | | 1.2799 | 17.0 | 170 | 1.2620 | 0.5625 | 0.5169 | 0.5194 | | 1.2481 | 18.0 | 180 | 1.2321 | 0.5938 | 0.6003 | 0.5811 | | 1.1993 | 19.0 | 190 | 1.2108 | 0.5687 | 0.5640 | 0.5412 | | 1.1599 | 20.0 | 200 | 1.1853 | 0.55 | 0.5434 | 0.5259 | | 1.1087 | 21.0 | 210 | 1.1839 | 0.5563 | 0.5670 | 0.5380 | | 1.0757 | 22.0 | 220 | 1.1905 | 0.55 | 0.5682 | 0.5308 | | 0.9985 | 23.0 | 230 | 1.1509 | 0.6375 | 0.6714 | 0.6287 | | 0.9776 | 24.0 | 240 | 1.1048 | 0.6188 | 0.6222 | 0.6127 | | 0.9331 | 25.0 | 250 | 1.1196 | 0.6125 | 0.6345 | 0.6072 | | 0.8887 | 26.0 | 260 | 1.1424 | 0.5938 | 0.6174 | 0.5867 | | 0.879 | 27.0 | 270 | 1.1232 | 0.6062 | 0.6342 | 0.5978 | | 0.8369 | 28.0 | 280 | 1.1172 | 0.6 | 0.6480 | 0.5865 | | 0.7864 | 29.0 | 290 | 1.1285 | 0.5938 | 0.6819 | 0.5763 | | 0.7775 | 30.0 | 300 | 1.0511 | 0.6687 | 0.7104 | 0.6713 | | 0.7281 | 31.0 | 310 | 1.0295 | 0.6562 | 0.6596 | 0.6514 | | 0.7348 | 32.0 | 320 | 1.0398 | 0.6375 | 0.6353 | 0.6319 | | 0.6896 | 33.0 | 330 | 1.0729 | 0.6062 | 0.6205 | 0.6062 | | 0.613 | 34.0 | 340 | 1.0505 | 0.6438 | 0.6595 | 0.6421 | | 0.6034 | 35.0 | 350 | 1.0827 | 0.6375 | 0.6593 | 0.6376 | | 0.6236 | 36.0 | 360 | 1.1271 | 0.6125 | 0.6238 | 0.6087 | | 0.5607 | 37.0 | 370 | 1.0985 | 0.6062 | 0.6254 | 0.6015 | | 0.5835 | 38.0 | 380 | 1.0791 | 0.6375 | 0.6624 | 0.6370 | | 0.5889 | 39.0 | 390 | 1.1300 | 0.6062 | 0.6529 | 0.6092 | | 0.5137 | 40.0 | 400 | 1.1062 | 0.625 | 0.6457 | 0.6226 | | 0.4804 | 41.0 | 410 | 1.1452 | 0.6188 | 0.6403 | 0.6158 | | 0.4811 | 42.0 | 420 | 1.1271 | 0.6375 | 0.6478 | 0.6347 | | 0.5179 | 43.0 | 430 | 1.1942 | 0.5875 | 0.6185 | 0.5874 | | 0.4744 | 44.0 | 440 | 1.1515 | 0.6125 | 0.6329 | 0.6160 | | 0.4327 | 45.0 | 450 | 1.1321 | 0.6375 | 0.6669 | 0.6412 | | 0.4565 | 46.0 | 460 | 1.1742 | 0.625 | 0.6478 | 0.6251 | | 0.4006 | 47.0 | 470 | 1.1675 | 0.6062 | 0.6361 | 0.6079 | | 0.4541 | 48.0 | 480 | 1.1542 | 0.6125 | 0.6404 | 0.6152 | | 0.3689 | 49.0 | 490 | 1.2190 | 0.5875 | 0.6134 | 0.5896 | | 0.3794 | 50.0 | 500 | 1.2002 | 0.6062 | 0.6155 | 0.6005 | | 0.429 | 51.0 | 510 | 1.2904 | 0.575 | 0.6207 | 0.5849 | | 0.431 | 52.0 | 520 | 1.2416 | 0.5875 | 0.6028 | 0.5794 | | 0.3813 | 53.0 | 530 | 1.2073 | 0.6125 | 0.6449 | 0.6142 | | 0.365 | 54.0 | 540 | 1.2083 | 0.6062 | 0.6454 | 0.6075 | | 0.3714 | 55.0 | 550 | 1.1627 | 0.6375 | 0.6576 | 0.6390 | | 0.3393 | 56.0 | 560 | 1.1620 | 0.6438 | 0.6505 | 0.6389 | | 0.3676 | 57.0 | 570 | 1.1501 | 0.625 | 0.6294 | 0.6258 | | 0.3371 | 58.0 | 580 | 1.2779 | 0.5875 | 0.6000 | 0.5792 | | 0.3325 | 59.0 | 590 | 1.2719 | 0.575 | 0.5843 | 0.5651 | | 0.3509 | 60.0 | 600 | 1.2956 | 0.6 | 0.6422 | 0.6059 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

m-aliabbas1/set_fit_practice

m-aliabbas1

2023-09-18T03:49:01Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-09-18T03:48:43Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # m-aliabbas1/set_fit_practice This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("m-aliabbas1/set_fit_practice") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

shaowenchen/chinese-alpaca-2-13b-gguf

shaowenchen

2023-09-18T03:44:45Z

100

0

null

[ "gguf", "meta", "llama", "llama-2", "alpaca", "alpaca-2", "chinese", "text-generation", "zh", "license:other", "region:us" ]

text-generation

2023-09-16T23:34:00Z

--- inference: false language: - zh license: other model_creator: ziqingyang model_link: https://huggingface.co/ziqingyang/chinese-alpaca-2-13b model_name: chinese-alpaca-2-13b model_type: llama pipeline_tag: text-generation quantized_by: shaowenchen tasks: - text2text-generation tags: - meta - gguf - llama - llama-2 - alpaca - alpaca-2 - chinese --- ## Provided files | Name | Quant method | Size | | -------------------------------- | ------------ | ------- | | chinese-alpaca-2-13b.Q2_K.gguf | Q2_K | 5.2 GB | | chinese-alpaca-2-13b.Q3_K.gguf | Q3_K | 6.0 GB | | chinese-alpaca-2-13b.Q3_K_L.gguf | Q3_K_L | 6.6 GB | | chinese-alpaca-2-13b.Q3_K_S.gguf | Q3_K_S | 5.4 GB | | chinese-alpaca-2-13b.Q4_0.gguf | Q4_0 | 7.0 GB | | chinese-alpaca-2-13b.Q4_1.gguf | Q4_1 | 7.8 GB | | chinese-alpaca-2-13b.Q4_K.gguf | Q4_K | 7.5 GB | | chinese-alpaca-2-13b.Q4_K_S.gguf | Q4_K_S | 7.1 GB | | chinese-alpaca-2-13b.Q5_0.gguf | Q5_0 | 8.5 GB | | chinese-alpaca-2-13b.Q5_1.gguf | Q5_1 | 9.3 GB | | chinese-alpaca-2-13b.Q5_K.gguf | Q5_K | 8.8 GB | | chinese-alpaca-2-13b.Q5_K_S.gguf | Q5_K_S | 8.5 GB | | chinese-alpaca-2-13b.Q6_K.gguf | Q6_K | 10.0 GB | | chinese-alpaca-2-13b.Q8_0.gguf | Q8_0 | 13.0 GB | | chinese-alpaca-2-13b.gguf | full | 25.0 GB | Usage: ``` docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/gguf-model-name.gguf hubimage/llama-cpp-python:latest ``` and you can view http://localhost:8000/docs to see the swagger UI.

Chickenfish/Dayte_dreambooth

Chickenfish

2023-09-18T03:41:11Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-08-22T07:18:04Z

--- license: creativeml-openrail-m ---

nemesis1/chlldrgnrc

nemesis1

2023-09-18T03:28:31Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-09-18T03:28:31Z

--- license: creativeml-openrail-m ---

LarryAIDraw/shana1-000008

LarryAIDraw

2023-09-18T03:26:44Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-09-18T03:21:22Z

--- license: creativeml-openrail-m --- https://civitai.com/models/47454/shana-or-character-lora-974

ys7yoo/sts_roberta-large_lr1e-05_wd1e-03_ep7_ckpt

ys7yoo

2023-09-18T03:26:33Z

108

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T03:03:05Z

--- base_model: klue/roberta-large tags: - generated_from_trainer datasets: - klue model-index: - name: sts_roberta-large_lr1e-05_wd1e-03_ep7_ckpt results: [] ---  # sts_roberta-large_lr1e-05_wd1e-03_ep7_ckpt This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3621 - Mse: 0.3621 - Mae: 0.4438 - R2: 0.8342 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 1.8712 | 1.0 | 183 | 0.5118 | 0.5118 | 0.5409 | 0.7656 | | 0.1606 | 2.0 | 366 | 0.4621 | 0.4621 | 0.5142 | 0.7884 | | 0.1111 | 3.0 | 549 | 0.4687 | 0.4687 | 0.5088 | 0.7854 | | 0.0837 | 4.0 | 732 | 0.4317 | 0.4317 | 0.4906 | 0.8023 | | 0.0681 | 5.0 | 915 | 0.4662 | 0.4662 | 0.5091 | 0.7865 | | 0.0559 | 6.0 | 1098 | 0.3742 | 0.3742 | 0.4524 | 0.8286 | | 0.0485 | 7.0 | 1281 | 0.3621 | 0.3621 | 0.4438 | 0.8342 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3

LarryAIDraw/Goddess_of_Light_Avatar

LarryAIDraw

2023-09-18T03:26:22Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-09-18T03:20:25Z

--- license: creativeml-openrail-m --- https://civitai.com/models/145816/tang-wutong-or-or-goddess-of-light-fusion-skill-avatar-or-soul-land-ii-or-douluo-dalu-ii-jueshi-tangmen-or-2-or-manhua

LarryAIDraw/schwarz_arknights

LarryAIDraw

2023-09-18T03:25:10Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-09-18T03:19:18Z

--- license: creativeml-openrail-m --- https://civitai.com/models/130905/schwarz-arknights

guydebruyn/Reinforce-Copter2

guydebruyn

2023-09-18T03:24:04Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-09-18T03:24:01Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Copter2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

zeenfts/output_dir

zeenfts

2023-09-18T03:17:42Z

28

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-16T08:08:06Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: output_dir results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6 ---  # output_dir This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2976 - Accuracy: 0.6 ## 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: 7e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: reduce_lr_on_plateau - num_epochs: 77 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 2 | 2.0706 | 0.15 | | No log | 2.0 | 5 | 2.0309 | 0.2313 | | No log | 2.8 | 7 | 1.9846 | 0.2562 | | 1.9868 | 4.0 | 10 | 1.8915 | 0.4062 | | 1.9868 | 4.8 | 12 | 1.8529 | 0.3125 | | 1.9868 | 6.0 | 15 | 1.7422 | 0.4125 | | 1.9868 | 6.8 | 17 | 1.6761 | 0.4313 | | 1.6815 | 8.0 | 20 | 1.6310 | 0.4562 | | 1.6815 | 8.8 | 22 | 1.5900 | 0.45 | | 1.6815 | 10.0 | 25 | 1.5402 | 0.4313 | | 1.6815 | 10.8 | 27 | 1.5018 | 0.5 | | 1.4233 | 12.0 | 30 | 1.4620 | 0.4875 | | 1.4233 | 12.8 | 32 | 1.4286 | 0.5062 | | 1.4233 | 14.0 | 35 | 1.4045 | 0.5125 | | 1.4233 | 14.8 | 37 | 1.3860 | 0.5312 | | 1.2127 | 16.0 | 40 | 1.3571 | 0.5 | | 1.2127 | 16.8 | 42 | 1.3293 | 0.5375 | | 1.2127 | 18.0 | 45 | 1.3742 | 0.4813 | | 1.2127 | 18.8 | 47 | 1.3151 | 0.5437 | | 1.0075 | 20.0 | 50 | 1.3053 | 0.5312 | | 1.0075 | 20.8 | 52 | 1.3266 | 0.5375 | | 1.0075 | 22.0 | 55 | 1.2964 | 0.5312 | | 1.0075 | 22.8 | 57 | 1.2278 | 0.5875 | | 0.8232 | 24.0 | 60 | 1.2501 | 0.5563 | | 0.8232 | 24.8 | 62 | 1.2330 | 0.575 | | 0.8232 | 26.0 | 65 | 1.2198 | 0.5625 | | 0.8232 | 26.8 | 67 | 1.2071 | 0.5875 | | 0.6738 | 28.0 | 70 | 1.2643 | 0.5875 | | 0.6738 | 28.8 | 72 | 1.2594 | 0.5563 | | 0.6738 | 30.0 | 75 | 1.2263 | 0.5312 | | 0.6738 | 30.8 | 77 | 1.3218 | 0.5188 | | 0.5715 | 32.0 | 80 | 1.2593 | 0.5312 | | 0.5715 | 32.8 | 82 | 1.2214 | 0.5625 | | 0.5715 | 34.0 | 85 | 1.3060 | 0.55 | | 0.5715 | 34.8 | 87 | 1.2727 | 0.5563 | | 0.4523 | 36.0 | 90 | 1.2749 | 0.5375 | | 0.4523 | 36.8 | 92 | 1.3570 | 0.5437 | | 0.4523 | 38.0 | 95 | 1.2815 | 0.5687 | | 0.4523 | 38.8 | 97 | 1.2233 | 0.6062 | | 0.3971 | 40.0 | 100 | 1.2097 | 0.6 | | 0.3971 | 40.8 | 102 | 1.2881 | 0.5813 | | 0.3971 | 42.0 | 105 | 1.2400 | 0.575 | | 0.3971 | 42.8 | 107 | 1.3140 | 0.5375 | | 0.3616 | 44.0 | 110 | 1.1525 | 0.6125 | | 0.3616 | 44.8 | 112 | 1.2725 | 0.5938 | | 0.3616 | 46.0 | 115 | 1.2634 | 0.5813 | | 0.3616 | 46.8 | 117 | 1.2299 | 0.6 | | 0.338 | 48.0 | 120 | 1.3408 | 0.5375 | | 0.338 | 48.8 | 122 | 1.1931 | 0.5938 | | 0.338 | 50.0 | 125 | 1.2806 | 0.5938 | | 0.338 | 50.8 | 127 | 1.2410 | 0.575 | | 0.3445 | 52.0 | 130 | 1.2901 | 0.5813 | | 0.3445 | 52.8 | 132 | 1.2504 | 0.6062 | | 0.3445 | 54.0 | 135 | 1.1614 | 0.5875 | | 0.3445 | 54.8 | 137 | 1.2247 | 0.6062 | | 0.3299 | 56.0 | 140 | 1.2591 | 0.5625 | | 0.3299 | 56.8 | 142 | 1.2629 | 0.5687 | | 0.3299 | 58.0 | 145 | 1.2369 | 0.5938 | | 0.3299 | 58.8 | 147 | 1.2771 | 0.575 | | 0.3292 | 60.0 | 150 | 1.3284 | 0.5875 | | 0.3292 | 60.8 | 152 | 1.2550 | 0.5625 | | 0.3292 | 61.6 | 154 | 1.3047 | 0.55 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/hayasaka_mirei_idolmastercinderellagirls

CyberHarem

2023-09-18T03:11:12Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/hayasaka_mirei_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-18T02:48:28Z

--- license: mit datasets: - CyberHarem/hayasaka_mirei_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of hayasaka_mirei_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4320, you need to download `4320/hayasaka_mirei_idolmastercinderellagirls.pt` as the embedding and `4320/hayasaka_mirei_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4320**, with the score of 0.973. The trigger words are: 1. `hayasaka_mirei_idolmastercinderellagirls` 2. `purple_hair, eyepatch, multicolored_hair, brown_eyes, short_hair, blush, red_hair, streaked_hair, open_mouth, fang, heart, hair_between_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 8100 | 0.966 | [Download](8100/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-8100](8100/previews/pattern_1.png) | ![pattern_2-8100](8100/previews/pattern_2.png) | ![pattern_3-8100](8100/previews/pattern_3.png) | ![pattern_4-8100](8100/previews/pattern_4.png) | ![pattern_5-8100](8100/previews/pattern_5.png) | ![pattern_6-8100](8100/previews/pattern_6.png) | ![pattern_7-8100](8100/previews/pattern_7.png) | ![pattern_8-8100](8100/previews/pattern_8.png) | ![pattern_9-8100](8100/previews/pattern_9.png) | ![pattern_10-8100](8100/previews/pattern_10.png) | ![pattern_11-8100](8100/previews/pattern_11.png) | ![pattern_12-8100](8100/previews/pattern_12.png) | ![pattern_13-8100](8100/previews/pattern_13.png) | ![pattern_14-8100](8100/previews/pattern_14.png) | ![pattern_15-8100](8100/previews/pattern_15.png) | ![pattern_16-8100](8100/previews/pattern_16.png) | ![bikini-8100](8100/previews/bikini.png) | [<NSFW, click to see>](8100/previews/bondage.png) | ![free-8100](8100/previews/free.png) | ![maid-8100](8100/previews/maid.png) | ![miko-8100](8100/previews/miko.png) | [<NSFW, click to see>](8100/previews/nude.png) | [<NSFW, click to see>](8100/previews/nude2.png) | ![suit-8100](8100/previews/suit.png) | ![yukata-8100](8100/previews/yukata.png) | | 7560 | 0.969 | [Download](7560/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-7560](7560/previews/pattern_1.png) | ![pattern_2-7560](7560/previews/pattern_2.png) | ![pattern_3-7560](7560/previews/pattern_3.png) | ![pattern_4-7560](7560/previews/pattern_4.png) | ![pattern_5-7560](7560/previews/pattern_5.png) | ![pattern_6-7560](7560/previews/pattern_6.png) | ![pattern_7-7560](7560/previews/pattern_7.png) | ![pattern_8-7560](7560/previews/pattern_8.png) | ![pattern_9-7560](7560/previews/pattern_9.png) | ![pattern_10-7560](7560/previews/pattern_10.png) | ![pattern_11-7560](7560/previews/pattern_11.png) | ![pattern_12-7560](7560/previews/pattern_12.png) | ![pattern_13-7560](7560/previews/pattern_13.png) | ![pattern_14-7560](7560/previews/pattern_14.png) | ![pattern_15-7560](7560/previews/pattern_15.png) | ![pattern_16-7560](7560/previews/pattern_16.png) | ![bikini-7560](7560/previews/bikini.png) | [<NSFW, click to see>](7560/previews/bondage.png) | ![free-7560](7560/previews/free.png) | ![maid-7560](7560/previews/maid.png) | ![miko-7560](7560/previews/miko.png) | [<NSFW, click to see>](7560/previews/nude.png) | [<NSFW, click to see>](7560/previews/nude2.png) | ![suit-7560](7560/previews/suit.png) | ![yukata-7560](7560/previews/yukata.png) | | 7020 | 0.957 | [Download](7020/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-7020](7020/previews/pattern_1.png) | ![pattern_2-7020](7020/previews/pattern_2.png) | ![pattern_3-7020](7020/previews/pattern_3.png) | ![pattern_4-7020](7020/previews/pattern_4.png) | ![pattern_5-7020](7020/previews/pattern_5.png) | ![pattern_6-7020](7020/previews/pattern_6.png) | ![pattern_7-7020](7020/previews/pattern_7.png) | ![pattern_8-7020](7020/previews/pattern_8.png) | ![pattern_9-7020](7020/previews/pattern_9.png) | ![pattern_10-7020](7020/previews/pattern_10.png) | ![pattern_11-7020](7020/previews/pattern_11.png) | ![pattern_12-7020](7020/previews/pattern_12.png) | ![pattern_13-7020](7020/previews/pattern_13.png) | ![pattern_14-7020](7020/previews/pattern_14.png) | ![pattern_15-7020](7020/previews/pattern_15.png) | ![pattern_16-7020](7020/previews/pattern_16.png) | ![bikini-7020](7020/previews/bikini.png) | [<NSFW, click to see>](7020/previews/bondage.png) | ![free-7020](7020/previews/free.png) | ![maid-7020](7020/previews/maid.png) | ![miko-7020](7020/previews/miko.png) | [<NSFW, click to see>](7020/previews/nude.png) | [<NSFW, click to see>](7020/previews/nude2.png) | ![suit-7020](7020/previews/suit.png) | ![yukata-7020](7020/previews/yukata.png) | | 6480 | 0.968 | [Download](6480/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-6480](6480/previews/pattern_1.png) | ![pattern_2-6480](6480/previews/pattern_2.png) | ![pattern_3-6480](6480/previews/pattern_3.png) | ![pattern_4-6480](6480/previews/pattern_4.png) | ![pattern_5-6480](6480/previews/pattern_5.png) | ![pattern_6-6480](6480/previews/pattern_6.png) | ![pattern_7-6480](6480/previews/pattern_7.png) | ![pattern_8-6480](6480/previews/pattern_8.png) | ![pattern_9-6480](6480/previews/pattern_9.png) | ![pattern_10-6480](6480/previews/pattern_10.png) | ![pattern_11-6480](6480/previews/pattern_11.png) | ![pattern_12-6480](6480/previews/pattern_12.png) | ![pattern_13-6480](6480/previews/pattern_13.png) | ![pattern_14-6480](6480/previews/pattern_14.png) | ![pattern_15-6480](6480/previews/pattern_15.png) | ![pattern_16-6480](6480/previews/pattern_16.png) | ![bikini-6480](6480/previews/bikini.png) | [<NSFW, click to see>](6480/previews/bondage.png) | ![free-6480](6480/previews/free.png) | ![maid-6480](6480/previews/maid.png) | ![miko-6480](6480/previews/miko.png) | [<NSFW, click to see>](6480/previews/nude.png) | [<NSFW, click to see>](6480/previews/nude2.png) | ![suit-6480](6480/previews/suit.png) | ![yukata-6480](6480/previews/yukata.png) | | 5940 | 0.970 | [Download](5940/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-5940](5940/previews/pattern_1.png) | ![pattern_2-5940](5940/previews/pattern_2.png) | ![pattern_3-5940](5940/previews/pattern_3.png) | ![pattern_4-5940](5940/previews/pattern_4.png) | ![pattern_5-5940](5940/previews/pattern_5.png) | ![pattern_6-5940](5940/previews/pattern_6.png) | ![pattern_7-5940](5940/previews/pattern_7.png) | ![pattern_8-5940](5940/previews/pattern_8.png) | ![pattern_9-5940](5940/previews/pattern_9.png) | ![pattern_10-5940](5940/previews/pattern_10.png) | ![pattern_11-5940](5940/previews/pattern_11.png) | ![pattern_12-5940](5940/previews/pattern_12.png) | ![pattern_13-5940](5940/previews/pattern_13.png) | ![pattern_14-5940](5940/previews/pattern_14.png) | ![pattern_15-5940](5940/previews/pattern_15.png) | ![pattern_16-5940](5940/previews/pattern_16.png) | ![bikini-5940](5940/previews/bikini.png) | [<NSFW, click to see>](5940/previews/bondage.png) | ![free-5940](5940/previews/free.png) | ![maid-5940](5940/previews/maid.png) | ![miko-5940](5940/previews/miko.png) | [<NSFW, click to see>](5940/previews/nude.png) | [<NSFW, click to see>](5940/previews/nude2.png) | ![suit-5940](5940/previews/suit.png) | ![yukata-5940](5940/previews/yukata.png) | | 5400 | 0.907 | [Download](5400/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-5400](5400/previews/pattern_1.png) | ![pattern_2-5400](5400/previews/pattern_2.png) | ![pattern_3-5400](5400/previews/pattern_3.png) | ![pattern_4-5400](5400/previews/pattern_4.png) | ![pattern_5-5400](5400/previews/pattern_5.png) | ![pattern_6-5400](5400/previews/pattern_6.png) | ![pattern_7-5400](5400/previews/pattern_7.png) | ![pattern_8-5400](5400/previews/pattern_8.png) | ![pattern_9-5400](5400/previews/pattern_9.png) | ![pattern_10-5400](5400/previews/pattern_10.png) | ![pattern_11-5400](5400/previews/pattern_11.png) | ![pattern_12-5400](5400/previews/pattern_12.png) | ![pattern_13-5400](5400/previews/pattern_13.png) | ![pattern_14-5400](5400/previews/pattern_14.png) | ![pattern_15-5400](5400/previews/pattern_15.png) | ![pattern_16-5400](5400/previews/pattern_16.png) | ![bikini-5400](5400/previews/bikini.png) | [<NSFW, click to see>](5400/previews/bondage.png) | ![free-5400](5400/previews/free.png) | ![maid-5400](5400/previews/maid.png) | ![miko-5400](5400/previews/miko.png) | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) | ![suit-5400](5400/previews/suit.png) | ![yukata-5400](5400/previews/yukata.png) | | 4860 | 0.972 | [Download](4860/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-4860](4860/previews/pattern_1.png) | ![pattern_2-4860](4860/previews/pattern_2.png) | ![pattern_3-4860](4860/previews/pattern_3.png) | ![pattern_4-4860](4860/previews/pattern_4.png) | ![pattern_5-4860](4860/previews/pattern_5.png) | ![pattern_6-4860](4860/previews/pattern_6.png) | ![pattern_7-4860](4860/previews/pattern_7.png) | ![pattern_8-4860](4860/previews/pattern_8.png) | ![pattern_9-4860](4860/previews/pattern_9.png) | ![pattern_10-4860](4860/previews/pattern_10.png) | ![pattern_11-4860](4860/previews/pattern_11.png) | ![pattern_12-4860](4860/previews/pattern_12.png) | ![pattern_13-4860](4860/previews/pattern_13.png) | ![pattern_14-4860](4860/previews/pattern_14.png) | ![pattern_15-4860](4860/previews/pattern_15.png) | ![pattern_16-4860](4860/previews/pattern_16.png) | ![bikini-4860](4860/previews/bikini.png) | [<NSFW, click to see>](4860/previews/bondage.png) | ![free-4860](4860/previews/free.png) | ![maid-4860](4860/previews/maid.png) | ![miko-4860](4860/previews/miko.png) | [<NSFW, click to see>](4860/previews/nude.png) | [<NSFW, click to see>](4860/previews/nude2.png) | ![suit-4860](4860/previews/suit.png) | ![yukata-4860](4860/previews/yukata.png) | | **4320** | **0.973** | [**Download**](4320/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | ![pattern_2-4320](4320/previews/pattern_2.png) | ![pattern_3-4320](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | ![pattern_5-4320](4320/previews/pattern_5.png) | ![pattern_6-4320](4320/previews/pattern_6.png) | ![pattern_7-4320](4320/previews/pattern_7.png) | ![pattern_8-4320](4320/previews/pattern_8.png) | ![pattern_9-4320](4320/previews/pattern_9.png) | ![pattern_10-4320](4320/previews/pattern_10.png) | ![pattern_11-4320](4320/previews/pattern_11.png) | ![pattern_12-4320](4320/previews/pattern_12.png) | ![pattern_13-4320](4320/previews/pattern_13.png) | ![pattern_14-4320](4320/previews/pattern_14.png) | ![pattern_15-4320](4320/previews/pattern_15.png) | ![pattern_16-4320](4320/previews/pattern_16.png) | ![bikini-4320](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | ![free-4320](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | 3780 | 0.964 | [Download](3780/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-3780](3780/previews/pattern_1.png) | ![pattern_2-3780](3780/previews/pattern_2.png) | ![pattern_3-3780](3780/previews/pattern_3.png) | ![pattern_4-3780](3780/previews/pattern_4.png) | ![pattern_5-3780](3780/previews/pattern_5.png) | ![pattern_6-3780](3780/previews/pattern_6.png) | ![pattern_7-3780](3780/previews/pattern_7.png) | ![pattern_8-3780](3780/previews/pattern_8.png) | ![pattern_9-3780](3780/previews/pattern_9.png) | ![pattern_10-3780](3780/previews/pattern_10.png) | ![pattern_11-3780](3780/previews/pattern_11.png) | ![pattern_12-3780](3780/previews/pattern_12.png) | ![pattern_13-3780](3780/previews/pattern_13.png) | ![pattern_14-3780](3780/previews/pattern_14.png) | ![pattern_15-3780](3780/previews/pattern_15.png) | ![pattern_16-3780](3780/previews/pattern_16.png) | ![bikini-3780](3780/previews/bikini.png) | [<NSFW, click to see>](3780/previews/bondage.png) | ![free-3780](3780/previews/free.png) | ![maid-3780](3780/previews/maid.png) | ![miko-3780](3780/previews/miko.png) | [<NSFW, click to see>](3780/previews/nude.png) | [<NSFW, click to see>](3780/previews/nude2.png) | ![suit-3780](3780/previews/suit.png) | ![yukata-3780](3780/previews/yukata.png) | | 3240 | 0.961 | [Download](3240/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-3240](3240/previews/pattern_1.png) | ![pattern_2-3240](3240/previews/pattern_2.png) | ![pattern_3-3240](3240/previews/pattern_3.png) | ![pattern_4-3240](3240/previews/pattern_4.png) | ![pattern_5-3240](3240/previews/pattern_5.png) | ![pattern_6-3240](3240/previews/pattern_6.png) | ![pattern_7-3240](3240/previews/pattern_7.png) | ![pattern_8-3240](3240/previews/pattern_8.png) | ![pattern_9-3240](3240/previews/pattern_9.png) | ![pattern_10-3240](3240/previews/pattern_10.png) | ![pattern_11-3240](3240/previews/pattern_11.png) | ![pattern_12-3240](3240/previews/pattern_12.png) | ![pattern_13-3240](3240/previews/pattern_13.png) | ![pattern_14-3240](3240/previews/pattern_14.png) | ![pattern_15-3240](3240/previews/pattern_15.png) | ![pattern_16-3240](3240/previews/pattern_16.png) | ![bikini-3240](3240/previews/bikini.png) | [<NSFW, click to see>](3240/previews/bondage.png) | ![free-3240](3240/previews/free.png) | ![maid-3240](3240/previews/maid.png) | ![miko-3240](3240/previews/miko.png) | [<NSFW, click to see>](3240/previews/nude.png) | [<NSFW, click to see>](3240/previews/nude2.png) | ![suit-3240](3240/previews/suit.png) | ![yukata-3240](3240/previews/yukata.png) | | 2700 | 0.958 | [Download](2700/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-2700](2700/previews/pattern_1.png) | ![pattern_2-2700](2700/previews/pattern_2.png) | ![pattern_3-2700](2700/previews/pattern_3.png) | ![pattern_4-2700](2700/previews/pattern_4.png) | ![pattern_5-2700](2700/previews/pattern_5.png) | ![pattern_6-2700](2700/previews/pattern_6.png) | ![pattern_7-2700](2700/previews/pattern_7.png) | ![pattern_8-2700](2700/previews/pattern_8.png) | ![pattern_9-2700](2700/previews/pattern_9.png) | ![pattern_10-2700](2700/previews/pattern_10.png) | ![pattern_11-2700](2700/previews/pattern_11.png) | ![pattern_12-2700](2700/previews/pattern_12.png) | ![pattern_13-2700](2700/previews/pattern_13.png) | ![pattern_14-2700](2700/previews/pattern_14.png) | ![pattern_15-2700](2700/previews/pattern_15.png) | ![pattern_16-2700](2700/previews/pattern_16.png) | ![bikini-2700](2700/previews/bikini.png) | [<NSFW, click to see>](2700/previews/bondage.png) | ![free-2700](2700/previews/free.png) | ![maid-2700](2700/previews/maid.png) | ![miko-2700](2700/previews/miko.png) | [<NSFW, click to see>](2700/previews/nude.png) | [<NSFW, click to see>](2700/previews/nude2.png) | ![suit-2700](2700/previews/suit.png) | ![yukata-2700](2700/previews/yukata.png) | | 2160 | 0.968 | [Download](2160/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-2160](2160/previews/pattern_1.png) | ![pattern_2-2160](2160/previews/pattern_2.png) | ![pattern_3-2160](2160/previews/pattern_3.png) | ![pattern_4-2160](2160/previews/pattern_4.png) | ![pattern_5-2160](2160/previews/pattern_5.png) | ![pattern_6-2160](2160/previews/pattern_6.png) | ![pattern_7-2160](2160/previews/pattern_7.png) | ![pattern_8-2160](2160/previews/pattern_8.png) | ![pattern_9-2160](2160/previews/pattern_9.png) | ![pattern_10-2160](2160/previews/pattern_10.png) | ![pattern_11-2160](2160/previews/pattern_11.png) | ![pattern_12-2160](2160/previews/pattern_12.png) | ![pattern_13-2160](2160/previews/pattern_13.png) | ![pattern_14-2160](2160/previews/pattern_14.png) | ![pattern_15-2160](2160/previews/pattern_15.png) | ![pattern_16-2160](2160/previews/pattern_16.png) | ![bikini-2160](2160/previews/bikini.png) | [<NSFW, click to see>](2160/previews/bondage.png) | ![free-2160](2160/previews/free.png) | ![maid-2160](2160/previews/maid.png) | ![miko-2160](2160/previews/miko.png) | [<NSFW, click to see>](2160/previews/nude.png) | [<NSFW, click to see>](2160/previews/nude2.png) | ![suit-2160](2160/previews/suit.png) | ![yukata-2160](2160/previews/yukata.png) | | 1620 | 0.972 | [Download](1620/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-1620](1620/previews/pattern_1.png) | ![pattern_2-1620](1620/previews/pattern_2.png) | ![pattern_3-1620](1620/previews/pattern_3.png) | ![pattern_4-1620](1620/previews/pattern_4.png) | ![pattern_5-1620](1620/previews/pattern_5.png) | ![pattern_6-1620](1620/previews/pattern_6.png) | ![pattern_7-1620](1620/previews/pattern_7.png) | ![pattern_8-1620](1620/previews/pattern_8.png) | ![pattern_9-1620](1620/previews/pattern_9.png) | ![pattern_10-1620](1620/previews/pattern_10.png) | ![pattern_11-1620](1620/previews/pattern_11.png) | ![pattern_12-1620](1620/previews/pattern_12.png) | ![pattern_13-1620](1620/previews/pattern_13.png) | ![pattern_14-1620](1620/previews/pattern_14.png) | ![pattern_15-1620](1620/previews/pattern_15.png) | ![pattern_16-1620](1620/previews/pattern_16.png) | ![bikini-1620](1620/previews/bikini.png) | [<NSFW, click to see>](1620/previews/bondage.png) | ![free-1620](1620/previews/free.png) | ![maid-1620](1620/previews/maid.png) | ![miko-1620](1620/previews/miko.png) | [<NSFW, click to see>](1620/previews/nude.png) | [<NSFW, click to see>](1620/previews/nude2.png) | ![suit-1620](1620/previews/suit.png) | ![yukata-1620](1620/previews/yukata.png) | | 1080 | 0.959 | [Download](1080/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-1080](1080/previews/pattern_1.png) | ![pattern_2-1080](1080/previews/pattern_2.png) | ![pattern_3-1080](1080/previews/pattern_3.png) | ![pattern_4-1080](1080/previews/pattern_4.png) | ![pattern_5-1080](1080/previews/pattern_5.png) | ![pattern_6-1080](1080/previews/pattern_6.png) | ![pattern_7-1080](1080/previews/pattern_7.png) | ![pattern_8-1080](1080/previews/pattern_8.png) | ![pattern_9-1080](1080/previews/pattern_9.png) | ![pattern_10-1080](1080/previews/pattern_10.png) | ![pattern_11-1080](1080/previews/pattern_11.png) | ![pattern_12-1080](1080/previews/pattern_12.png) | ![pattern_13-1080](1080/previews/pattern_13.png) | ![pattern_14-1080](1080/previews/pattern_14.png) | ![pattern_15-1080](1080/previews/pattern_15.png) | ![pattern_16-1080](1080/previews/pattern_16.png) | ![bikini-1080](1080/previews/bikini.png) | [<NSFW, click to see>](1080/previews/bondage.png) | ![free-1080](1080/previews/free.png) | ![maid-1080](1080/previews/maid.png) | ![miko-1080](1080/previews/miko.png) | [<NSFW, click to see>](1080/previews/nude.png) | [<NSFW, click to see>](1080/previews/nude2.png) | ![suit-1080](1080/previews/suit.png) | ![yukata-1080](1080/previews/yukata.png) | | 540 | 0.929 | [Download](540/hayasaka_mirei_idolmastercinderellagirls.zip) | ![pattern_1-540](540/previews/pattern_1.png) | ![pattern_2-540](540/previews/pattern_2.png) | ![pattern_3-540](540/previews/pattern_3.png) | ![pattern_4-540](540/previews/pattern_4.png) | ![pattern_5-540](540/previews/pattern_5.png) | ![pattern_6-540](540/previews/pattern_6.png) | ![pattern_7-540](540/previews/pattern_7.png) | ![pattern_8-540](540/previews/pattern_8.png) | ![pattern_9-540](540/previews/pattern_9.png) | ![pattern_10-540](540/previews/pattern_10.png) | ![pattern_11-540](540/previews/pattern_11.png) | ![pattern_12-540](540/previews/pattern_12.png) | ![pattern_13-540](540/previews/pattern_13.png) | ![pattern_14-540](540/previews/pattern_14.png) | ![pattern_15-540](540/previews/pattern_15.png) | ![pattern_16-540](540/previews/pattern_16.png) | ![bikini-540](540/previews/bikini.png) | [<NSFW, click to see>](540/previews/bondage.png) | ![free-540](540/previews/free.png) | ![maid-540](540/previews/maid.png) | ![miko-540](540/previews/miko.png) | [<NSFW, click to see>](540/previews/nude.png) | [<NSFW, click to see>](540/previews/nude2.png) | ![suit-540](540/previews/suit.png) | ![yukata-540](540/previews/yukata.png) |

EldritchAdam/LaxpeintXL

EldritchAdam

2023-09-18T03:10:49Z

0

null

[ "license:openrail", "region:us" ]

null

2023-09-04T19:16:06Z

--- license: openrail --- <div>LaxpeintXL - tentatively final version for SDXL 1.0 This model is a companion to <a target="_blank" rel="ugc" href="https://huggingface.co/EldritchAdam/ClassipeintXL">ClassipeintXL</a>. Although I see ClassipeintXL as really crucial to SDXL (and how I use it) LaxpeintXL is not so obviously necessary. You can get much of this style with the right combination of artist names and aesthetic terms. So why use a LoRA? As much as SDXL is a huge leap forward from SD2, it shares a failing - albeit to a much lesser extent - that keeping an aesthetic consistent is very difficult. The same terms and artist names will not have the same effect for a portrait as for a landscape or a sci-fi scene etc. This LoRA helps you to more consistently get that slick digital paint style in every image. Prompt for whatever you want, it's going to be beautiful. Recommended settings for use:<a target="_blank" rel="ugc" href="https://pastebin.com/tXKwTkxC">You can go here (pastebin) to download a ComfyUI workflow</a> like what I used, but without custom nodes that are embedded in my image uploads on CivitAI. <ul> <li> Start with a full 1.0 LoRA strength and adjust down to 0.7 or 0.8 for a subtler painterly effect. You can adjust upward (to 1.2 or maybe a little more) to maximize the painterly appearance, but it can start to introduce some quirks </li> <li> Use the LoRA with your preferred SDXL model with no refiner. I have so far just stuck with base SDXL1.0 but other finetunes work great as well. </li> <li> I recommend the DPM samplers, but use your favorite. Some may produce softer painting styles that don't suit my taste as much but whatever you prefer is great. </li> <li> Don't do anything special for your prompt - just describe what you want to see. You don't really need to use any keywords unless some subject matter seems to override the LoRA's style, then you can bring it back in line by using the terms "digital painting of..." and "by LaxpeintXL". </li> </ul> </div> <div style="max-width:500px"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/0B4gg9e6HNzYI-2dJzIZH.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/gH9bA1TDD2S_bJzheUXr_.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/cu0EyW4eOqr9iVhTN2Cgc.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/o0El5-8ms0J-Ae1gqNi71.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/CbnMKPkqAXM4st88RqXmj.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/mCmmJXYUmD8QamftYjWuQ.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/z4DXgHzHjKbh1mkfW7ur_.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/YdvSPWp38oa-JZgEqnEfp.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/zR1huUXvEl7b6kFdbuxRg.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/jiFLLFahcoE72BcjFKuws.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/8JB6sAgRnaHJ5jsgTpHki.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/LJQJw0V1E3NCdVEMUwgW7.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63169de2f5e32157c5226974/LyZL9NLV2mSxQtQae4trO.png"> </div>

GAS17/fgdpersn

GAS17

2023-09-18T03:07:47Z

1

0

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:GAS17/fgdperson", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-09-18T02:33:17Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: fgd person tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: false datasets: - GAS17/fgdperson --- # LoRA DreamBooth - GAS17/fgdpersn These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on the concept prompt: `fgd person` Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```python import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) # This is where you load your trained weights pipe.load_lora_weights('GAS17/fgdpersn') pipe.to("cuda") prompt = "A majestic fgd person jumping from a big stone at night" image = pipe(prompt=prompt, num_inference_steps=50).images[0] ```

ys7yoo/sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep5_ckpt

ys7yoo

2023-09-18T02:54:33Z

106

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "base_model:finetune:ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T02:25:10Z

--- base_model: ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3 tags: - generated_from_trainer datasets: - klue model-index: - name: sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep5_ckpt results: [] ---  # sts_nli_roberta-large_lr1e-05_wd1e-03_ep3_lr1e-05_wd1e-03_ep5_ckpt This model is a fine-tuned version of [ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3](https://huggingface.co/ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3191 - Mse: 0.3191 - Mae: 0.4161 - R2: 0.8539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 1.0641 | 1.0 | 183 | 0.5074 | 0.5074 | 0.5341 | 0.7676 | | 0.1359 | 2.0 | 366 | 0.3199 | 0.3199 | 0.4232 | 0.8535 | | 0.0958 | 3.0 | 549 | 0.3589 | 0.3589 | 0.4349 | 0.8356 | | 0.0748 | 4.0 | 732 | 0.3385 | 0.3385 | 0.4284 | 0.8450 | | 0.0617 | 5.0 | 915 | 0.3191 | 0.3191 | 0.4161 | 0.8539 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3

AyanKumarBhunia/textual_inversion_cat

AyanKumarBhunia

2023-09-18T02:49:48Z

30

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-18T02:21:58Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - AyanKumarBhunia/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.

huyen89/taxi-v3

huyen89

2023-09-18T02:33:55Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-08-23T01:57:15Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 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="huyen89/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"]) ```

hegelty/KcBERT-Base-finetuned-hate

hegelty

2023-09-18T02:30:55Z

113

1

transformers

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "ko", "license:bsd", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-07T04:23:36Z

--- license: bsd language: - ko library_name: transformers --- # 혐오표현 분류 tag 0: 혐오 tag 1: 일반 # 소스코드 https://github.com/hegelty/hate-classifier # 데이터셋 https://github.com/smilegate-ai/korean_unsmile_dataset

ShivamMangale/XLM-Roberta-base-finetuned-squad-syn-first

ShivamMangale

2023-09-18T02:21:07Z

20

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-09-18T01:29:23Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-finetuned-squad-syn-first results: [] ---  # XLM-Roberta-base-finetuned-squad-syn-first This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3

wu981526092/Sentence-Level-Stereotype-Detector

wu981526092

2023-09-18T01:49:58Z

15,593

4

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "en", "dataset:stereoset", "dataset:crows_pairs", "dataset:wu981526092/MGSD", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-29T16:02:37Z

--- license: mit datasets: - stereoset - crows_pairs - wu981526092/MGSD language: - en metrics: - f1 - recall - precision - accuracy --- # Sentence-Level Stereotype Classifier The Sentence-Level Stereotype Classifier is a transformer-based model developed to detect and classify different types of stereotypes present in the text at the sentence level. It is designed to recognize stereotypical and anti-stereotypical stereotypes towards gender, race, profession, and religion. The model can help in developing applications aimed at mitigating Stereotypical language use and promoting fairness and inclusivity in natural language processing tasks. ## Model Architecture The model is built using the pre-trained Distilbert model. It is fine-tuned on MGS Dataset for the task of sentence-level stereotype classification. ## Classes The model identifies nine classes, including: 0. unrelated: The token does not indicate any stereotype. 1. stereotype_gender: The token indicates a gender stereotype. 2. anti-stereotype_gender: The token indicates an anti-gender stereotype. 3. stereotype_race: The token indicates a racial stereotype. 4. anti-stereotype_race: The token indicates an anti-racial stereotype. 5. stereotype_profession: The token indicates a professional stereotype. 6. anti-stereotype_profession: The token indicates an anti-professional stereotype. 7. stereotype_religion: The token indicates a religious stereotype. 8. anti-stereotype_religion: The token indicates an anti-religious stereotype. ## Usage The model can be used as a part of the Hugging Face's pipeline for Text Classification. ```python from transformers import pipeline nlp = pipeline("text-classification", model="wu981526092/Sentence-Level-Stereotype-Detector", tokenizer="wu981526092/Sentence-Level-Stereotype-Detector") result = nlp("Text containing potential stereotype...") print(result) ```

wu981526092/Token-Level-Stereotype-Detector

wu981526092

2023-09-18T01:48:45Z

110

2

transformers

[ "transformers", "pytorch", "distilbert", "token-classification", "en", "dataset:stereoset", "dataset:crows_pairs", "dataset:wu981526092/MGSD", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-24T10:21:27Z

--- license: mit datasets: - stereoset - crows_pairs - wu981526092/MGSD language: - en metrics: - f1 - recall - precision - accuracy --- # Token-Level Stereotype Classifier The Token-Level Stereotype Classifier is a transformer-based model developed to detect and classify different types of stereotypes present in the text at the token level. It is designed to recognize stereotypical and anti-stereotypical stereotypes towards gender, race, profession, and religion. The model can help in developing applications aimed at mitigating stereotypical language use and promoting fairness and inclusivity in natural language processing tasks. ## Model Architecture The model is built using the pretrained Distilbert model. It is fine-tuned on MGS Dataset for the task of token-level classification. ## Classes The model identifies nine classes, including: 1. unrelated: The token does not indicate any stereotype. 2. stereotype_gender: The token indicates a gender stereotype. 3. anti-stereotype_gender: The token indicates an anti-gender stereotype. 4. stereotype_race: The token indicates a racial stereotype. 5. anti-stereotype_race: The token indicates an anti-racial stereotype. 6. stereotype_profession: The token indicates a professional stereotype. 7. anti-stereotype_profession: The token indicates an anti-professional stereotype. 8. stereotype_religion: The token indicates a religious stereotype. 9. anti-stereotype_religion: The token indicates an anti-religious stereotype. ## Usage The model can be used as a part of the Hugging Face's pipeline for Named Entity Recognition (NER). ```python from transformers import pipeline nlp = pipeline("ner", model="wu981526092/Token-Level-Stereotype-Detector", tokenizer="wu981526092/Token-Level-Stereotype-Detector") result = nlp("Text containing potential stereotype...") print(result) ```

kiranahp/indobert_qa_skripsi_big

kiranahp

2023-09-18T01:32:54Z

122

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-09-18T01:20:42Z

--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: indobert_qa_skripsi_big results: [] ---  # indobert_qa_skripsi_big This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8011 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6607 | 1.0 | 4101 | 1.7878 | | 1.3687 | 2.0 | 8202 | 1.7563 | | 1.1822 | 3.0 | 12303 | 1.8011 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

KETI-AIR-Downstream/long-ke-t5-base-summarization_e10

KETI-AIR-Downstream

2023-09-18T01:28:33Z

119

0

transformers

[ "transformers", "pytorch", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "dataset:jsonl_dataset_sum.py", "base_model:KETI-AIR/long-ke-t5-base", "base_model:finetune:KETI-AIR/long-ke-t5-base", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-05T04:24:59Z

--- tags: - generated_from_trainer datasets: - jsonl_dataset_sum.py metrics: - rouge widget: - text: 'summarization-num_lines-1: 현대자동차는 18일(현지 시간) 이탈리아 레이크 코모에서 개최된 ''현대 리유니온'' 행사에서 ''포니 쿠페 콘셉트'' 복원 모델을 세계에 첫 공개했습니다. 이 프로젝트는 현대차의 창업자인 정주영 선대 회장의 수출보국(輸出報國) 정신과 포니 쿠페를 통한 글로벌 브랜드 정립에 대한 끊임없는 열정과 도전 정신을 재조명하기 위한 것입니다. 현대차에 따르면, 이번 현대 리유니온 행사는 회사의 역사를 다시 돌아보며 변하지 않는 미래 지향적인 비전과 방향성을 공유하는 브랜드 유산 행사입니다.' example_title: sample 1 base_model: KETI-AIR/long-ke-t5-base model-index: - name: summarization_all results: - task: type: summarization name: Summarization dataset: name: jsonl_dataset_sum.py type: jsonl_dataset_sum.py config: 'null' split: None metrics: - type: rouge value: 21.9857 name: Rouge1 ---  # summarization_all This model is a fine-tuned version of [KETI-AIR/long-ke-t5-base](https://huggingface.co/KETI-AIR/long-ke-t5-base) on the jsonl_dataset_sum.py dataset. It achieves the following results on the evaluation set: - Loss: 1.1442 - Rouge1: 21.9857 - Rouge2: 10.2876 - Rougel: 21.4026 - Rougelsum: 21.4278 - Gen Len: 86.2560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.2503 | 1.0 | 184670 | 1.2439 | 20.2525 | 9.1467 | 19.7454 | 19.771 | 87.1766 | | 1.1629 | 2.0 | 369340 | 1.1773 | 21.0068 | 9.6691 | 20.4565 | 20.4888 | 89.6074 | | 1.1087 | 3.0 | 554010 | 1.1431 | 21.0216 | 9.6545 | 20.489 | 20.5108 | 85.5895 | | 1.056 | 4.0 | 738680 | 1.1247 | 21.6776 | 10.1424 | 21.09 | 21.1168 | 89.6576 | | 1.0199 | 5.0 | 923350 | 1.1179 | 21.6563 | 10.0965 | 21.0814 | 21.1056 | 89.2454 | | 0.9652 | 6.0 | 1108020 | 1.1122 | 21.6209 | 10.0725 | 21.0623 | 21.0864 | 86.7079 | | 0.92 | 7.0 | 1292690 | 1.1136 | 21.9396 | 10.2734 | 21.3465 | 21.3745 | 86.5547 | | 0.8804 | 8.0 | 1477360 | 1.1228 | 21.8457 | 10.1858 | 21.2552 | 21.278 | 87.6413 | | 0.8447 | 9.0 | 1662030 | 1.1327 | 21.92 | 10.2635 | 21.3415 | 21.3633 | 86.4453 | | 0.7678 | 10.0 | 1846700 | 1.1442 | 21.9857 | 10.2876 | 21.4026 | 21.4278 | 86.2560 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.0 - Datasets 2.8.0 - Tokenizers 0.13.2

KETI-AIR-Downstream/long-ke-t5-base-translation-aihub-en2ko

KETI-AIR-Downstream

2023-09-18T01:27:39Z

159

3

transformers

[ "transformers", "pytorch", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "translation", "en", "ko", "base_model:KETI-AIR/long-ke-t5-base", "base_model:finetune:KETI-AIR/long-ke-t5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-04-28T14:19:27Z

--- language: - en - ko license: apache-2.0 tags: - generated_from_trainer datasets: - KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation metrics: - bleu pipeline_tag: translation widget: - text: 'translate_en2ko: The Seoul Metropolitan Government said Wednesday that it would develop an AI-based congestion monitoring system to provide better information to passengers about crowd density at each subway station.' example_title: Sample 1 - text: 'translate_en2ko: According to Seoul Metro, the operator of the subway service in Seoul, the new service will help analyze the real-time flow of passengers and crowd levels in subway compartments, improving operational efficiency.' example_title: Sample 2 base_model: KETI-AIR/long-ke-t5-base model-index: - name: en2ko results: - task: type: translation name: Translation dataset: name: KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation koen,none,none,none,none type: KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation args: koen,none,none,none,none metrics: - type: bleu value: 42.463 name: Bleu ---  # en2ko This model is a fine-tuned version of [KETI-AIR/long-ke-t5-base](https://huggingface.co/KETI-AIR/long-ke-t5-base) on the KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation koen,none,none,none,none dataset. It achieves the following results on the evaluation set: - Loss: 0.6000 - Bleu: 42.463 - Gen Len: 30.6512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| | 0.6989 | 1.0 | 93762 | 0.6666 | 20.3697 | 18.1258 | | 0.6143 | 2.0 | 187524 | 0.6181 | 21.2903 | 18.1428 | | 0.5544 | 3.0 | 281286 | 0.6000 | 21.9763 | 18.1424 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.0 - Datasets 2.8.0 - Tokenizers 0.13.2

KETI-AIR/ke-t5-large

KETI-AIR

2023-09-18T01:24:55Z

102

8

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "en", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:04Z

--- license: apache-2.0 language: [en, ko] tags: - t5 eos_token: "</s>" widget: - text: 아버지가 방에 들어가신다.</s> --- # ke-t5 base Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details. ## How to use ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("KETI-AIR/ke-t5-large") tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-large") ``` ## BibTeX entry and citation info ```bibtex @inproceedings{kim-etal-2021-model-cross, title = "A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems", author = "Kim, San and Jang, Jin Yea and Jung, Minyoung and Shin, Saim", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.33", doi = "10.18653/v1/2021.findings-emnlp.33", pages = "352--365", abstract = "Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.", } ```

KETI-AIR/ke-t5-base-ko

KETI-AIR

2023-09-18T01:24:34Z

378

7

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "ko", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:04Z

--- language: ko license: apache-2.0 tags: - t5 eos_token: </s> widget: - text: 아버지가 방에 들어가신다.</s> --- # Model Card for ke-t5-base-ko # Model Details ## Model Description - **Developed by:** Korea Electronics Technology Institute Artificial Intelligence Research Center - **Shared by [Optional]:** More information needed - **Model type:** Text2Text Generation - **Language(s) (NLP):** More information needed - **License:** More information needed - **Related Models:** - **Parent Model:** T5 - **Resources for more information:** - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints) - [KE-T5 Github Repo](https://github.com/AIRC-KETI/ke-t5) - [Paper](https://aclanthology.org/2021.findings-emnlp.33/) - [Associated Paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) - [Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) # Uses ## Direct Use This model can be used for the task of Text2Text Generation ## Downstream Use [Optional] More information needed ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5. The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**. See the [t5-base model card](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) for further information. ## Training Procedure ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed # Citation **BibTeX:** ```bibtex @inproceedings{kim-etal-2021-model-cross, title = "A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems", author = "Kim, San and Jang, Jin Yea and Jung, Minyoung and Shin, Saim", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.33", doi = "10.18653/v1/2021.findings-emnlp.33", pages = "352--365", abstract = "Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.", } ``` ```bibtex @article{2020t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} } ``` **APA:** ``` - Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. ``` # Glossary [optional] More information needed # More Information [optional] More information needed # Model Card Authors [optional] Korea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-base-ko") model = AutoModelForSeq2SeqLM.from_pretrained("KETI-AIR/ke-t5-base-ko") ``` </details>

Navu45/neon_sd_model

Navu45

2023-09-18T01:14:45Z

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-18T00:02:58Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - Navu45/neon_sd_model These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Navu45/neon_dreambooth dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

ys7yoo/nli_roberta-large_lr1e-05_wd1e-03_ep3_ckpt

ys7yoo

2023-09-18T01:08:41Z

109

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-18T00:46:19Z

--- base_model: klue/roberta-large tags: - generated_from_trainer datasets: - klue metrics: - accuracy - f1 model-index: - name: nli_roberta-large_lr1e-05_wd1e-03_ep3_ckpt results: - task: name: Text Classification type: text-classification dataset: name: klue type: klue config: nli split: validation args: nli metrics: - name: Accuracy type: accuracy value: 0.9026666666666666 - name: F1 type: f1 value: 0.9025716877431428 ---  # nli_roberta-large_lr1e-05_wd1e-03_ep3_ckpt This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3425 - Accuracy: 0.9027 - F1: 0.9026 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5725 | 1.0 | 391 | 0.3381 | 0.8813 | 0.8811 | | 0.2182 | 2.0 | 782 | 0.3055 | 0.898 | 0.8979 | | 0.112 | 3.0 | 1173 | 0.3425 | 0.9027 | 0.9026 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3

natsusakiyomi/momijimix-xl

natsusakiyomi

2023-09-18T00:51:44Z

0

2

null

[ "license:openrail++", "region:us" ]

null

2023-09-17T21:06:41Z

--- license: openrail++ --- License [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) 〇Use the model without crediting the creator 〇Sell images they generate 〇Run on services that generate images for money 〇Share merges using this model ×Sell this model or merges using this model ×Have different permissions when sharing merges

TrevorJS/mtg-phi-1_5-dpo-qlora

TrevorJS

2023-09-18T00:31:30Z

0

null

[ "generated_from_trainer", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "region:us" ]

null

2023-09-18T00:20:06Z

--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Rewards/chosen: -7.5874 - Rewards/rejected: -24.0497 - Rewards/accuracies: 1.0 - Rewards/margins: 16.4623 - Logps/rejected: -274.3435 - Logps/chosen: -143.2090 - Logits/rejected: -1.8100 - Logits/chosen: -1.4786 ## 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: 4 - eval_batch_size: 1 - 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: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.0417 | 0.07 | 100 | 0.0418 | -0.3892 | -8.0118 | 0.9792 | 7.6226 | -113.9640 | -71.2264 | 1.8258 | 1.7898 | | 0.0221 | 0.15 | 200 | 0.0303 | -2.5657 | -10.9212 | 0.9896 | 8.3555 | -143.0585 | -92.9920 | 1.9704 | 2.1047 | | 0.0107 | 0.22 | 300 | 0.0131 | -1.7388 | -11.6047 | 0.9965 | 9.8659 | -149.8935 | -84.7232 | 1.0731 | 0.9750 | | 0.0204 | 0.29 | 400 | 0.0108 | -2.0131 | -11.9647 | 0.9965 | 9.9516 | -153.4932 | -87.4658 | 1.3610 | 1.6740 | | 0.0067 | 0.36 | 500 | 0.0080 | -5.9488 | -19.6561 | 0.9974 | 13.7073 | -230.4076 | -126.8228 | -0.4464 | -0.2114 | | 0.0 | 0.44 | 600 | 0.0047 | -5.6456 | -20.2381 | 0.9983 | 14.5924 | -236.2268 | -123.7909 | -0.4142 | -0.0244 | | 0.0003 | 0.51 | 700 | 0.0018 | -7.2250 | -21.3351 | 0.9991 | 14.1101 | -247.1974 | -139.5853 | -0.3510 | -0.0203 | | 0.0005 | 0.58 | 800 | 0.0008 | -7.2263 | -21.2475 | 0.9991 | 14.0211 | -246.3209 | -139.5981 | -0.8673 | -0.7010 | | 0.0 | 0.66 | 900 | 0.0009 | -10.2371 | -26.0402 | 0.9991 | 15.8031 | -294.2486 | -169.7062 | -1.9784 | -1.7799 | | 0.0 | 0.73 | 1000 | 0.0008 | -5.9544 | -22.0767 | 0.9991 | 16.1223 | -254.6137 | -126.8789 | -1.0623 | -0.6039 | | 0.0 | 0.8 | 1100 | 0.0007 | -7.3374 | -23.8700 | 0.9991 | 16.5327 | -272.5467 | -140.7083 | -1.5517 | -1.1710 | | 0.0 | 0.87 | 1200 | 0.0007 | -7.6398 | -24.1605 | 0.9991 | 16.5207 | -275.4509 | -143.7327 | -1.8124 | -1.4901 | | 0.0 | 0.95 | 1300 | 0.0001 | -7.5920 | -24.0476 | 1.0 | 16.4556 | -274.3220 | -143.2550 | -1.8115 | -1.4816 | | 0.0001 | 1.02 | 1400 | 0.0001 | -7.5872 | -24.0480 | 1.0 | 16.4608 | -274.3262 | -143.2065 | -1.8102 | -1.4791 | | 0.0 | 1.09 | 1500 | 0.0001 | -7.5874 | -24.0497 | 1.0 | 16.4623 | -274.3435 | -143.2090 | -1.8100 | -1.4786 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Evan-Lin/yelp-attractive-keyword-1

Evan-Lin

2023-09-18T00:07:04Z

47

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-09-17T10:03:06Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpypedqoes/Evan-Lin/yelp-attractive-keyword-1") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpypedqoes/Evan-Lin/yelp-attractive-keyword-1") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpypedqoes/Evan-Lin/yelp-attractive-keyword-1") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```