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 2025-07-27 18:27:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 533 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-27 18:22:57	card stringlengths 11 1.01M
takaha202306/facelora	takaha202306	2023-06-17T17:54:59Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-17T16:17:55Z	--- license: creativeml-openrail-m --- editing # about This repository contains two Stable Diffusion LoRAs for generate "Cute MOE face" illustration, and their training data. There was trained by [kohya-ss's sd_scripts](https://github.com/kohya-ss/sd-scripts), and tested on [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui). The base model is almost AOM3, the model I used was added a little merge as: [AOM3.safetensors](https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix3/AOM3_orangemixs.safetensors) + [Hipoly 3D Model LoRA v2.0](https://civitai.com/models/8730/hipoly-3d-model-lora):0.15:0,1,1,1,1,1,1,0.1,0.1,0.1,0.1,1,1,1,0.3,0.1,0.1. Hipoly 3d LoRA was merged by [SuperMerger](https://github.com/hako-mikan/sd-webui-supermerger). They don't require "trigger word", simply changes illustration's style when applied. I recommend to set weight between 0.05 to 0.4, and use [LoRA Block Weight extension](https://github.com/hako-mikan/sd-webui-lora-block-weight). # facelora1 First LoRA is actually not made for this purpose. I attempted to make "Uchinoko"(means my daughter, favorite original character, lol) LoRA. Train data are my favorite images(those are generated above model by me), and augmentation images made by [controlnet's reference-only](https://github.com/Mikubill/sd-webui-controlnet#reference-only-control). This LoRA worked well, worked better than I intended, and could be used to generate beautiful girls in a generic way. # facelora2 I generated various beautiful girls images with above LoRA, and made second LoRA trained by them. This LoRA is more suitable for general purpose generation. # samples
Atnafu/amharic_xlmr_base	Atnafu	2023-06-17T17:36:41Z	103	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-17T02:34:11Z	--- license: mit tags: - generated_from_trainer model-index: - name: amh_base 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. --> # amh_base This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3301 ## 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: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.13.0 - Tokenizers 0.13.3
mmirmahdi/q-FrozenLake-v1-4x4-noSlippery	mmirmahdi	2023-06-17T17:22:53Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T17:22:23Z	--- 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="mmirmahdi/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"]) ```
0xghagevaibhav/meMyself	0xghagevaibhav	2023-06-17T17:11:20Z	0	0	null	[ "hi", "license:unknown", "region:us" ]	null	2023-06-17T17:09:19Z	--- license: unknown language: - hi ---
erens/mikasalast	erens	2023-06-17T17:01:46Z	30	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-17T16:46:29Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mikasaLAST Dreambooth model trained by erens with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
DionTimmer/controlnet_qrcode	DionTimmer	2023-06-17T16:33:13Z	2,526	306	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "controlnet", "en", "license:openrail++", "region:us" ]	null	2023-06-15T02:23:37Z	--- tags: - stable-diffusion - controlnet license: openrail++ language: - en --- # QR Code Conditioned ControlNet Models for Stable Diffusion 1.5 and 2.1 ![1](imgs/1.png) ## Model Description These ControlNet models have been trained on a large dataset of 150,000 QR code + QR code artwork couples. They provide a solid foundation for generating QR code-based artwork that is aesthetically pleasing, while still maintaining the integral QR code shape. The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, a 1.5 version model was also trained on the same dataset for those who are using the older version. Separate repos for usage in diffusers can be found here:<br> 1.5: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15<br> 2.1: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v11p_sd21<br> ## How to use with Diffusers ```bash pip -q install diffusers transformers accelerate torch xformers ``` ```python import torch from PIL import Image from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler from diffusers.utils import load_image controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16) pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.enable_xformers_memory_efficient_attention() pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() def resize_for_condition_image(input_image: Image, resolution: int): input_image = input_image.convert("RGB") W, H = input_image.size k = float(resolution) / min(H, W) H = k W = k H = int(round(H / 64.0)) * 64 W = int(round(W / 64.0)) * 64 img = input_image.resize((W, H), resample=Image.LANCZOS) return img # play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image # qr code image source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png") # initial image, anything init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg") condition_image = resize_for_condition_image(source_image, 768) init_image = resize_for_condition_image(init_image, 768) generator = torch.manual_seed(123121231) image = pipe(prompt="a bilboard in NYC with a qrcode", negative_prompt="ugly, disfigured, low quality, blurry, nsfw", image=init_image, control_image=condition_image, width=768, height=768, guidance_scale=20, controlnet_conditioning_scale=1.5, generator=generator, strength=0.9, num_inference_steps=150, ) image.images[0] ``` ## Performance and Limitations These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.To optimize for scanning, please generate your QR codes with correction mode 'H' (30%). To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork. ## Installation The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application. For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail. Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell. ![2](imgs/2.png) ![3](imgs/3.png) ![4](imgs/4.png)
dnjdsxor21/roberta-klue-ssm	dnjdsxor21	2023-06-17T16:32:52Z	118	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "ko", "dataset:dnjdsxor21/preprocessed-wiki-kor", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-11T13:40:11Z	--- widget: - text: 이순신은 [MASK] 중기의 무신이다. - text: 오바마는 미국의 [MASK] 이다. language: - ko pipeline_tag: fill-mask datasets: - dnjdsxor21/preprocessed-wiki-kor mask_token: '[MASK]' --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
mustika/alan3	mustika	2023-06-17T16:21:59Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-17T15:09:00Z	--- license: creativeml-openrail-m ---
vlkn/flan-t5-small-taboo-for-llms	vlkn	2023-06-17T16:20:59Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-03T13:32:22Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-taboo-for-llms 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. --> # flan-t5-small-taboo-for-llms This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4825 - Rouge1: 27.3897 - Rouge2: 9.9232 - Rougel: 24.2026 - Rougelsum: 24.6485 - Gen Len: 18.5172 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 137 \| 2.5897 \| 26.6789 \| 9.9538 \| 23.6637 \| 24.2407 \| 18.3621 \| \| No log \| 2.0 \| 274 \| 2.5560 \| 25.4162 \| 9.6277 \| 22.7084 \| 23.0883 \| 18.3966 \| \| No log \| 3.0 \| 411 \| 2.5377 \| 26.0239 \| 9.7748 \| 23.4425 \| 23.7935 \| 18.6034 \| \| 2.8204 \| 4.0 \| 548 \| 2.5241 \| 26.6294 \| 9.9168 \| 23.8023 \| 24.2756 \| 18.7241 \| \| 2.8204 \| 5.0 \| 685 \| 2.5120 \| 25.8274 \| 9.9333 \| 23.8865 \| 24.0724 \| 18.7586 \| \| 2.8204 \| 6.0 \| 822 \| 2.5031 \| 26.7774 \| 9.9651 \| 24.3654 \| 24.6102 \| 18.6034 \| \| 2.8204 \| 7.0 \| 959 \| 2.4985 \| 26.5058 \| 10.0422 \| 24.0403 \| 24.635 \| 18.4655 \| \| 2.6101 \| 8.0 \| 1096 \| 2.4934 \| 26.6953 \| 9.9536 \| 24.0293 \| 24.6809 \| 18.4655 \| \| 2.6101 \| 9.0 \| 1233 \| 2.4907 \| 26.7978 \| 9.6249 \| 23.714 \| 23.9992 \| 18.6034 \| \| 2.6101 \| 10.0 \| 1370 \| 2.4847 \| 27.2135 \| 9.878 \| 23.8398 \| 24.2389 \| 18.5 \| \| 2.4726 \| 11.0 \| 1507 \| 2.4856 \| 27.1799 \| 9.9337 \| 23.9393 \| 24.4067 \| 18.5172 \| \| 2.4726 \| 12.0 \| 1644 \| 2.4835 \| 27.4491 \| 10.1828 \| 24.0926 \| 24.4819 \| 18.5 \| \| 2.4726 \| 13.0 \| 1781 \| 2.4825 \| 27.3897 \| 9.9232 \| 24.2026 \| 24.6485 \| 18.5172 \| \| 2.4726 \| 14.0 \| 1918 \| 2.4836 \| 27.5567 \| 10.7405 \| 24.2497 \| 24.6566 \| 18.5345 \| \| 2.3731 \| 15.0 \| 2055 \| 2.4872 \| 27.7517 \| 11.0182 \| 24.1007 \| 24.7218 \| 18.4828 \| \| 2.3731 \| 16.0 \| 2192 \| 2.4852 \| 27.3461 \| 11.3381 \| 24.084 \| 24.5125 \| 18.4655 \| \| 2.3731 \| 17.0 \| 2329 \| 2.4872 \| 27.3558 \| 11.1005 \| 24.047 \| 24.4973 \| 18.4655 \| \| 2.3731 \| 18.0 \| 2466 \| 2.4841 \| 26.9427 \| 10.9288 \| 23.7324 \| 24.4298 \| 18.5345 \| \| 2.2967 \| 19.0 \| 2603 \| 2.4881 \| 27.5 \| 10.8437 \| 24.1593 \| 24.6028 \| 18.4483 \| \| 2.2967 \| 20.0 \| 2740 \| 2.4908 \| 27.517 \| 11.0039 \| 24.1049 \| 24.7111 \| 18.5 \| \| 2.2967 \| 21.0 \| 2877 \| 2.4917 \| 27.7333 \| 10.935 \| 24.4076 \| 24.9887 \| 18.4138 \| \| 2.2553 \| 22.0 \| 3014 \| 2.4926 \| 27.6275 \| 10.7562 \| 24.2295 \| 24.7476 \| 18.4138 \| \| 2.2553 \| 23.0 \| 3151 \| 2.4945 \| 27.9085 \| 10.943 \| 24.6135 \| 25.2373 \| 18.4138 \| \| 2.2553 \| 24.0 \| 3288 \| 2.4948 \| 27.5261 \| 10.7141 \| 24.2429 \| 24.816 \| 18.4138 \| \| 2.2553 \| 25.0 \| 3425 \| 2.4931 \| 27.5522 \| 10.8702 \| 24.5576 \| 25.0714 \| 18.4655 \| \| 2.213 \| 26.0 \| 3562 \| 2.4942 \| 27.4758 \| 11.0064 \| 24.5062 \| 25.05 \| 18.4655 \| \| 2.213 \| 27.0 \| 3699 \| 2.4954 \| 27.6967 \| 11.1744 \| 24.7646 \| 25.3172 \| 18.4655 \| \| 2.213 \| 28.0 \| 3836 \| 2.4951 \| 27.7428 \| 10.9365 \| 24.6427 \| 25.2432 \| 18.5172 \| \| 2.213 \| 29.0 \| 3973 \| 2.4949 \| 27.6877 \| 10.9522 \| 24.6101 \| 25.2471 \| 18.4655 \| \| 2.1865 \| 30.0 \| 4110 \| 2.4952 \| 27.7295 \| 11.0173 \| 24.6556 \| 25.2397 \| 18.4655 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
rohitp1/ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8	rohitp1	2023-06-17T15:57:16Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-02T12:05:05Z	--- tags: - generated_from_trainer metrics: - wer model-index: - name: ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8 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. --> # ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8 This model is a fine-tuned version of [rohitp1/kkkh_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_batch_16](https://huggingface.co/rohitp1/kkkh_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_batch_16) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0945 - Wer: 0.1041 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.0562 \| 2.46 \| 250 \| 0.0741 \| 0.1135 \| \| 0.0538 \| 4.92 \| 500 \| 0.0736 \| 0.1126 \| \| 0.0506 \| 7.38 \| 750 \| 0.0751 \| 0.1116 \| \| 0.0465 \| 9.84 \| 1000 \| 0.0752 \| 0.1099 \| \| 0.0424 \| 12.31 \| 1250 \| 0.0762 \| 0.1089 \| \| 0.0385 \| 14.77 \| 1500 \| 0.0790 \| 0.1078 \| \| 0.0355 \| 17.23 \| 1750 \| 0.0788 \| 0.1062 \| \| 0.0335 \| 19.69 \| 2000 \| 0.0795 \| 0.1053 \| \| 0.0314 \| 22.15 \| 2250 \| 0.0825 \| 0.1052 \| \| 0.0298 \| 24.61 \| 2500 \| 0.0837 \| 0.1055 \| \| 0.0285 \| 27.07 \| 2750 \| 0.0873 \| 0.1049 \| \| 0.0274 \| 29.53 \| 3000 \| 0.0868 \| 0.1043 \| \| 0.0266 \| 32.0 \| 3250 \| 0.0891 \| 0.1044 \| \| 0.0256 \| 34.46 \| 3500 \| 0.0902 \| 0.1044 \| \| 0.0251 \| 36.92 \| 3750 \| 0.0911 \| 0.1044 \| \| 0.0247 \| 39.38 \| 4000 \| 0.0926 \| 0.1042 \| \| 0.0242 \| 41.84 \| 4250 \| 0.0936 \| 0.1042 \| \| 0.0238 \| 44.3 \| 4500 \| 0.0940 \| 0.1042 \| \| 0.0235 \| 46.76 \| 4750 \| 0.0938 \| 0.1042 \| \| 0.0233 \| 49.22 \| 5000 \| 0.0945 \| 0.1041 \| ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1 - Datasets 2.7.1 - Tokenizers 0.11.0
Elvenson/stable_diffusion_weights	Elvenson	2023-06-17T15:50:04Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-04-15T04:59:14Z	--- license: openrail --- # Stable Diffusion Model Weights This repo is mainly for storing the Keras weights for Stable Diffusion models. The model is adapted from [here](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion).
l3cube-pune/hindi-marathi-dev-albert	l3cube-pune	2023-06-17T15:34:53Z	112	0	transformers	[ "transformers", "pytorch", "albert", "fill-mask", "hi", "mr", "multilingual", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-19T19:19:20Z	--- language: - hi - mr - multilingual license: cc-by-4.0 --- ## DevAlBERT DevAlBERT is a Devanagari AlBERT model model trained on publicly available Hindi and Marathi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
l3cube-pune/kannada-bert	l3cube-pune	2023-06-17T15:32:28Z	127	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "kn", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-20T07:02:51Z	--- license: cc-by-4.0 language: kn --- ## KannadaBERT KannadaBERT is a Kannada BERT model trained on publicly available Kannada monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
l3cube-pune/hindi-albert	l3cube-pune	2023-06-17T15:32:02Z	142	1	transformers	[ "transformers", "pytorch", "albert", "fill-mask", "hi", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-19T18:36:25Z	--- license: cc-by-4.0 language: hi --- ## HindAlBERT HindAlBERT is a Hindi AlBERT model model trained on publicly available Hindi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] (<a href='http://dx.doi.org/10.13140/RG.2.2.14606.84809'> pdf </a>) ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
atrytone/scibert_uncased_claim_id	atrytone	2023-06-17T15:16:47Z	112	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-18T05:07:15Z	--- license: apache-2.0 language: - en --- Fine-tuned SciBERT uncased model [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) for claim detection from abstracts.
Hosioka/AniReal	Hosioka	2023-06-17T15:16:41Z	52	75	diffusers	[ "diffusers", "text-to-image", "stable-Diffusion", "stable-diffusion-diffusers", "safetensors", "en", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-01-20T11:45:12Z	--- license: creativeml-openrail-m thumbnail: "https://m1.afileditch.ch/uJoodjDNVWxDqhhQHeRH.png" language: - en tags: - text-to-image - stable-Diffusion - stable-diffusion-diffusers - diffusers - safetensors inference: true --- # Deprecated. Refer to this [New Version](https://huggingface.co/Hosioka/Baka-Diffusion) This Repository contains AniReal V1.0 ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # License 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. The authors 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
HoseinPanahi/finbert-lm-finetuned-news	HoseinPanahi	2023-06-17T15:14:00Z	113	1	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-17T12:01:59Z	--- tags: - generated_from_trainer model-index: - name: finbert-lm-finetuned-news 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. --> # finbert-lm-finetuned-news This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0723 ## 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: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.3271 \| 1.0 \| 2736 \| 2.2334 \| \| 2.0392 \| 2.0 \| 5472 \| 2.0723 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
gombalmukiyo/gombalmukiyo	gombalmukiyo	2023-06-17T15:11:32Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-17T15:09:52Z	--- license: creativeml-openrail-m ---
zwzhang/Accountable-Textual-Visual-Chat	zwzhang	2023-06-17T15:10:10Z	0	2	null	[ "arxiv:2303.05983", "license:apache-2.0", "region:us" ]	null	2023-06-17T14:41:30Z	--- license: apache-2.0 --- ## Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation [[Paper]](https://arxiv.org/pdf/2303.05983.pdf) [[Project Page]](https://matrix-alpha.github.io/) [[GitHub]](https://github.com/matrix-alpha/Accountable-Textual-Visual-Chat) ![ The overall framework of ATVC.](atvc.png)
Bala-A87/Huggy-DRL	Bala-A87	2023-06-17T14:35:21Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-17T14:34:51Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Bala-A87/Huggy-DRL 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Bodolaz/Unit-2	Bodolaz	2023-06-17T14:17:03Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T14:16:43Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Unit-2 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="Bodolaz/Unit-2", 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"]) ```
Bodolaz/q-FrozenLake-v1-4x4-noSlippery	Bodolaz	2023-06-17T13:46:43Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T13:46:27Z	--- 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="Bodolaz/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"]) ```
catrabbitbear/lunar-lander	catrabbitbear	2023-06-17T13:43:33Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T13:43:04Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.65 +/- 38.49 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
2tle/kobart-std-to-jeju	2tle	2023-06-17T13:41:43Z	104	1	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "ko", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-17T13:31:26Z	--- license: mit language: - ko metrics: - bleu --- # Korean Standard To Jejueo(Jeju Dialect) Translator BART Model ## Dataset - [AI Hub Korean Jejueo(Jeju Dialect) Voice data](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=121) ## Model Score - BLEU: 40%
tux/Reinforce-copter	tux	2023-06-17T13:23:28Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T11:34:21Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-copter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 18.70 +/- 15.84 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . 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
antphb/DS-Chatbox-bigscience-bloom-560m	antphb	2023-06-17T13:15:50Z	151	0	transformers	[ "transformers", "pytorch", "tensorboard", "bloom", "text-generation", "generated_from_trainer", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T11:27:42Z	--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer model-index: - name: DS-Chatbox-bigscience-bloom-560m 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. --> # DS-Chatbox-bigscience-bloom-560m This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 4.8320 - eval_runtime: 175.7948 - eval_samples_per_second: 37.402 - eval_steps_per_second: 4.676 - epoch: 0.03 - step: 500 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 3.0 ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
thiendio/ppo-from-scratch-lunar	thiendio	2023-06-17T12:26:39Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T12:26:16Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -158.29 +/- 101.80 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
samata/my_awesome_billsum_model	samata	2023-06-17T12:20:04Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-17T12:09:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1423 --- <!-- 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_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5563 - Rouge1: 0.1423 - Rouge2: 0.0518 - Rougel: 0.1171 - Rougelsum: 0.1171 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.8514 \| 0.1229 \| 0.0326 \| 0.1015 \| 0.1016 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.6361 \| 0.1308 \| 0.0421 \| 0.1066 \| 0.1068 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5725 \| 0.139 \| 0.0488 \| 0.1138 \| 0.114 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.5563 \| 0.1423 \| 0.0518 \| 0.1171 \| 0.1171 \| 19.0 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
alexanderjoossens/w2v2-libri-10min	alexanderjoossens	2023-06-17T12:16:40Z	105	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-05-22T09:09:45Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: w2v2-libri-10min 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. --> # w2v2-libri-10min This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 1.18.3 - Tokenizers 0.13.3
SikongSphere/sikong-llama-7b-chinese	SikongSphere	2023-06-17T12:01:59Z	7	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "dataset:customized", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T09:09:19Z	--- tags: - generated_from_trainer datasets: - customized model-index: - name: finetune 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. --> # finetune This model is a fine-tuned version of [/root/autodl-tmp/sikong/repo/LMFlow/output_models/Linly-Chinese-LLaMA-7b-hf](https://huggingface.co//root/autodl-tmp/sikong/repo/LMFlow/output_models/Linly-Chinese-LLaMA-7b-hf) on the customized 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: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50.0 ### Training results ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3
jalFaizy/ppo-lunar	jalFaizy	2023-06-17T11:42:59Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T11:42:28Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: trial1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.71 +/- 14.10 name: mean_reward verified: false --- # trial1 Agent playing LunarLander-v2 This is a trained model of a trial1 agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
bumstern/segmentation_model_russian_data	bumstern	2023-06-17T11:23:53Z	0	0	pyannote-audio	[ "pyannote-audio", "code", "ru", "license:mit", "region:us" ]	null	2023-06-17T11:02:51Z	--- license: mit language: - ru library_name: pyannote-audio tags: - code --- # Segmentation model This model was trained on AMI-MixHeadset and my own synthetic dataset of Russian speech. Training time: 5 hours on GTX 3060 This model can be used for diarization model from [pyannote/speaker-diarization](https://huggingface.co/pyannote/speaker-diarization) \| Benchmark \| DER% \| \| --------- \|------\| \| [AMI (headset mix,](https://groups.inf.ed.ac.uk/ami/corpus/) [only_words)](https://github.com/BUTSpeechFIT/AMI-diarization-setup) \| 38.8 \| ## Usage example ```python import yaml from yaml.loader import SafeLoader import torch from pyannote.audio import Model from pyannote.audio.pipelines import SpeakerDiarization segm_model = torch.load('model/segm_model.pth', map_location=torch.device('cpu')) embed_model = Model.from_pretrained("pyannote/embedding", use_auth_token='ACCESS_TOKEN_GOES_HERE') diar_pipeline = SpeakerDiarization( segmentation=segm_model, segmentation_batch_size=16, clustering="AgglomerativeClustering", embedding=embed_model ) with open('model/config.yaml', 'r') as f: diar_config = yaml.load(f, Loader=SafeLoader) diar_pipeline.instantiate(diar_config) annotation = diar_pipeline('audio.wav') ```
Enterprize1/q-taxi-v3	Enterprize1	2023-06-17T11:15:07Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T11:14:52Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.74 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="Enterprize1/q-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
kejolong/hdxduniform2.0	kejolong	2023-06-17T11:07:19Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-17T11:05:58Z	--- license: creativeml-openrail-m ---
antphb/DS-Chatbox-mbart-large-50	antphb	2023-06-17T11:03:57Z	117	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T07:09:44Z	--- license: mit tags: - generated_from_trainer model-index: - name: DS-Chatbox-mbart-large-50 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DS-Chatbox-mbart-large-50 This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0014 ## 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: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.977 \| 0.19 \| 100 \| 0.0001 \| \| 0.023 \| 0.38 \| 200 \| 0.0002 \| \| 0.0005 \| 0.57 \| 300 \| 0.0005 \| \| 0.0007 \| 0.76 \| 400 \| 0.0006 \| \| 0.0012 \| 0.95 \| 500 \| 0.0014 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
nomad-ai/ppo-LunarLander-v2-1	nomad-ai	2023-06-17T11:01:03Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T11:00:26Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 281.34 +/- 18.86 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mattladewig/distilbert-base-uncased-finetuned-ner	mattladewig	2023-06-17T10:34:27Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-17T08:37:53Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mattladewig/distilbert-base-uncased-finetuned-ner 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. --> # mattladewig/distilbert-base-uncased-finetuned-ner 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: 0.0342 - Validation Loss: 0.0614 - Train Precision: 0.9248 - Train Recall: 0.9365 - Train F1: 0.9306 - Train Accuracy: 0.9833 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.1951 \| 0.0694 \| 0.9087 \| 0.9181 \| 0.9134 \| 0.9799 \| 0 \| \| 0.0530 \| 0.0621 \| 0.9246 \| 0.9301 \| 0.9273 \| 0.9823 \| 1 \| \| 0.0342 \| 0.0614 \| 0.9248 \| 0.9365 \| 0.9306 \| 0.9833 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
kevinng77/unsup_bert_L3	kevinng77	2023-06-17T10:00:06Z	107	0	transformers	[ "transformers", "pytorch", "onnx", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-17T08:47:52Z	--- license: apache-2.0 language: - en metrics: - accuracy - f1 pipeline_tag: text-classification --- ```python # transformers==4.29.1 from transformers import AutoTokenizer, pipeline from optimum.onnxruntime import ORTModelForSequenceClassification onnx_model_path = "kevinng77/unsup_bert_L3" tokenizer = AutoTokenizer.from_pretrained(onnx_model_path) onnx_model = ORTModelForSequenceClassification.from_pretrained(onnx_model_path) onnx_pipe = pipeline(task="text-classification", model=onnx_model, tokenizer=tokenizer) onnx_pipe("How many rows are there in the table?") ```
parkyunmin/beatles_lyrics	parkyunmin	2023-06-17T09:38:03Z	198	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T09:11:41Z	--- tags: - generated_from_trainer model-index: - name: beatles_lyrics 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. --> # beatles_lyrics This model is a fine-tuned version of [wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics](https://huggingface.co/wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0584 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 50 \| 1.1221 \| \| No log \| 2.0 \| 100 \| 1.0710 \| \| No log \| 3.0 \| 150 \| 1.0584 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
ganghe74/distilbert-base-uncased-finetuned-emotion	ganghe74	2023-06-17T09:34:40Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-17T09:13:51Z	--- license: apache-2.0 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: 0.9225 - name: F1 type: f1 value: 0.922469380812715 --- <!-- 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: 0.2170 - Accuracy: 0.9225 - F1: 0.9225 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8057 \| 1.0 \| 250 \| 0.3170 \| 0.905 \| 0.9023 \| \| 0.242 \| 2.0 \| 500 \| 0.2170 \| 0.9225 \| 0.9225 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.0 - Tokenizers 0.13.3
benol/Roma_Pyatifan	benol	2023-06-17T09:10:31Z	0	0	null	[ "ru", "en", "arxiv:1910.09700", "license:unknown", "region:us" ]	null	2023-06-17T08:58:04Z	--- license: unknown language: - ru - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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parkyunmin/my_awesome_eli5_clm-model	parkyunmin	2023-06-17T09:09:15Z	211	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T05:54:26Z	--- license: mit tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-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_eli5_clm-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5380 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 49 \| 1.6679 \| \| No log \| 2.0 \| 98 \| 1.5629 \| \| No log \| 3.0 \| 147 \| 1.5380 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
PabloQuant29/ppo-LunarLander-v2	PabloQuant29	2023-06-17T08:36:13Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T08:35:40Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 244.46 +/- 18.98 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mustika/alan2	mustika	2023-06-17T08:36:10Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-17T08:34:12Z	--- license: creativeml-openrail-m ---
AriesChen/GeoLLM	AriesChen	2023-06-17T08:32:06Z	195	3	transformers	[ "transformers", "pytorch", "chatglm", "feature-extraction", "custom_code", "region:us" ]	feature-extraction	2023-06-17T08:30:04Z	# GeoLLM Large Language Model for Geology Large language models are used to organize geology-related knowledge (geology, geophysics, geophysical logging, etc.). This version uses the [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) base model and fine-tunes it using P-tuning. --- ### Sedimentology Sedimentology, the study of sedimentary rocks and the processes by which they are formed, includes and is related to a large number of phenomena. Sedimentology includes the five fundamental processes defined by the term sediaentation --weathering, erosion, transportation, deposition and diagenesis. Datasets：《沉积岩石学（第四版）》朱筱敏 Model： ChatGLM-6B Fine-tuning： P-Tuning v2 Before fine-tuning ``` response, history = model.chat(tokenizer, "什么是沉积岩石学？", history=[]) response 沉积岩石学是一门研究沉积岩的形成、结构和成分的学科，主要关注地球表面上不同条件下的沉积过程和岩石形成机制，包括岩浆沉积、冰川沉积、洪水沉积、海洋沉积等。沉积岩石学在地质学、地球物理学、地球化学、材料科学等领域都有广泛应用，因为沉积岩是许多自然和人工地质工程的基础，如地质勘探、矿产资源开发、土木工程、环境科学等。沉积岩石学的研究对象包括沉积岩的地质特征、成分和构造特征，以及沉积岩与地壳、岩浆和变质岩的关系。研究方法包括沉积岩分析、岩相学分析、岩浆动力学分析等。 ``` After fine-tuning ``` response, history = model.chat(tokenizer, "什么是沉积岩石学？", history=[]) response 沉积岩石学是研究沉积岩的物质成分、结构构造、岩石类型、沉积物沉积作用和沉积物质形成环境以及沉积岩分布规律的一门科学。 ``` Error Analysis: We meticulously refined the model by approximately 500 entries from academic textbooks, subsequently applying P-Tuning v2 for optimization. Detailed control of parameters was not conducted for the time being. Given the scarcity of the training data and the fine-tuning parameters, the outcomes might exhibit some irregularities. Results Analysis: It is evident that the fine-tuned model shows enhanced reliability(more precise and concise) when providing answers within specialized knowledge domains. Moving forward, we will persist in enriching our training data and optimizing our fine-tuning methodologies in order to yield superior results. --- ### TODO 1. Geophysical Exploration 2. Geophysical logging 3. Petroleum Geology etc... --- ### Related Resources 1. [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B): ChatGLM-6B is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters.
SM16/TreeClassifier	SM16	2023-06-17T08:15:11Z	218	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-17T07:27:25Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: TreeClassifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # TreeClassifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Pepper Tree ![Pepper Tree](images/Pepper_Tree.jpg) #### Weeping Willow ![Weeping Willow](images/Weeping_Willow.jpg)
musabg/mt5-xl-tr-summarization	musabg	2023-06-17T07:25:20Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "tr", "dataset:musabg/wikipedia-tr-summarization", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-08T16:24:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - musabg/wikipedia-tr-summarization metrics: - rouge model-index: - name: mt5-xl-tr-summarization results: - task: name: Summarization type: summarization dataset: name: musabg/wikipedia-tr-summarization type: musabg/wikipedia-tr-summarization split: validation metrics: - name: Rouge1 type: rouge value: 56.4468 language: - tr --- # mT5-Xl Turkish Summarization This model is a fine-tuned version of [google/mt5-xl](https://huggingface.co/google/mt5-xl) on the musabg/wikipedia-tr-summarization dataset. This can be used with HF summarization pipeline. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Eval results It achieves the following results on the evaluation set: - Loss: 0.5676 - Rouge1: 56.4468 - Rouge2: 41.3258 - Rougel: 48.1909 - Rougelsum: 48.4284 - Gen Len: 75.9265 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 1.13.1 - Datasets 2.12.0 - Tokenizers 0.13.3
alsonlai/test	alsonlai	2023-06-17T07:23:18Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T07:22:43Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: test results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="alsonlai/test", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Irgendsoeine/FaceTheVote3	Irgendsoeine	2023-06-17T07:10:58Z	4	0	tf-keras	[ "tf-keras", "mobilenet", "image-classification", "region:us" ]	image-classification	2023-06-17T06:56:45Z	--- pipeline_tag: image-classification ---
kjiwon1222/my_awesome_eli5_clm-model	kjiwon1222	2023-06-17T06:54:34Z	217	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T06:32:49Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-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_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7506 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8621 \| 1.0 \| 1137 \| 3.7690 \| \| 3.7782 \| 2.0 \| 2274 \| 3.7533 \| \| 3.7245 \| 3.0 \| 3411 \| 3.7506 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
aga3134/poca-SoccerTwos	aga3134	2023-06-17T06:48:55Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-06-17T06:48:14Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: aga3134/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
octipuw/policy_gradient-cartpole-v1	octipuw	2023-06-17T06:40:28Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T05:35:35Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: policy_gradient-cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 166.70 +/- 18.03 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
vedu/bart-large-perturbed	vedu	2023-06-17T06:21:37Z	103	0	transformers	[ "transformers", "pytorch", "jax", "rust", "bart", "feature-extraction", "en", "arxiv:1910.13461", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2023-06-16T20:22:31Z	--- license: apache-2.0 language: en --- # BART (large-sized model) ## Model description BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). Weights shared here are effectively from facebook/bart-large but with added noise for BOS embedding to assist the finetuning. ## Intended uses & limitations There have been quite a few issues related to finetuning BART for text generation, and this repo implements solution discussed in [#15559](https://github.com/huggingface/transformers/issues/15559). Particularly adding some noise to pre-trained model's BOS embedding. This seems to solve the problem of endless BOS generation for a finetuned BART model. You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import BartTokenizer, BartModel tokenizer = BartTokenizer.from_pretrained('vedu/bart-large-perturbed') model = BartModel.from_pretrained('vedu/bart-large-perturbed') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
pigeon01/sungju-finetuned-zh-to-ko1	pigeon01	2023-06-17T05:47:05Z	228	0	transformers	[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "translation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-17T05:12:16Z	--- license: mit tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: sungju-finetuned-zh-to-ko1 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. --> # sungju-finetuned-zh-to-ko1 This model is a fine-tuned version of [alirezamsh/small100](https://huggingface.co/alirezamsh/small100) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0467 - Bleu: 10.2096 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
sunflowermarshmallows/dqn-SpaceInvadersNoFrameskip-v4	sunflowermarshmallows	2023-06-17T05:25:16Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T05:24:36Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 629.00 +/- 184.89 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sunflowermarshmallows -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sunflowermarshmallows -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sunflowermarshmallows ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
nolanaatama/rngrndrvcv800pchsrthysttylrsvrsn	nolanaatama	2023-06-17T04:33:50Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-31T04:13:59Z	--- license: creativeml-openrail-m ---
ALPHONSE28/EQUIPO06SEMANA09	ALPHONSE28	2023-06-17T04:33:00Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-16T06:38:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: EQUIPO06SEMANA09 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. --> # EQUIPO06SEMANA09 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2161 - Accuracy: 0.9233 - F1: 0.9514 ## 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 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
fgeyer/reinforce-CartPole-v1	fgeyer	2023-06-17T04:04:51Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T03:59:14Z	--- 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: 1000.00 +/- 0.00 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
aozora-esther/aozora-esther	aozora-esther	2023-06-17T04:00:52Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-06-17T04:00:52Z	--- license: bigscience-openrail-m ---
2022happy/swin-tiny-patch4-window7-224-finetuned-eurosat	2022happy	2023-06-17T03:51:48Z	245	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:cifar10", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-15T13:46:26Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cifar10 metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: cifar10 type: cifar10 config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.97 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0893 - Accuracy: 0.97 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5255 \| 1.0 \| 351 \| 0.1262 \| 0.9596 \| \| 0.3808 \| 2.0 \| 703 \| 0.1031 \| 0.9652 \| \| 0.3268 \| 2.99 \| 1053 \| 0.0893 \| 0.97 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
nolanaatama/dmnslyrkmtsnybnmstyllr	nolanaatama	2023-06-17T02:31:22Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-17T02:27:58Z	--- license: creativeml-openrail-m ---
Atnafu/amhric_xlmr-small	Atnafu	2023-06-17T02:23:50Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-17T02:17:40Z	--- license: afl-3.0 tags: - generated_from_trainer model-index: - name: amh_small 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. --> # amh_small This model is a fine-tuned version of [Davlan/afro-xlmr-small](https://huggingface.co/Davlan/afro-xlmr-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2386 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
breadlicker45/MuseRift	breadlicker45	2023-06-17T02:11:29Z	170	0	transformers	[ "transformers", "pytorch", "rwkv", "text-generation", "dataset:breadlicker45/musenet-encoders-40k", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-15T01:22:00Z	--- datasets: - breadlicker45/musenet-encoders-40k ---
paulahugging/MABEPA_2	paulahugging	2023-06-17T01:48:24Z	103	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-06-16T15:43:20Z	El objetivo Modelo parte de un modelo Bert Base con el objetivo de identificar la similitud semántica entre dos oraciones (o Semantic Textual Similarity: “STS”), es decir, medir qué tan parecidos son dos documentos. Dicho modelo será a través de una red neuronal siamesa, que implica usar la misma red, con idénticos parámetros, para procesar la premisa y la hipótesis. “La tarea STS está motivada por la observación de que modelar con precisión la similitud de significado de las oraciones es un problema fundamental de comprensión del lenguaje relevante para numerosas aplicaciones, incluyendo: traducción automática (MT), resumen, generación, pregunta respuesta (QA), calificación de respuestas cortas, semántica, sistemas de búsqueda, diálogo y conversación.” (Cera Et al, 2017, p. 1). Datos El dataset elegido fue XNLI en español. El mismo contiene los campos de 'premise', 'hypothesis' y 'label’, donde los dos primeros campos son oraciones o cadenas de texto mientras que el tercero es la similitud semántica entre ambas con la siguiente codificación: 'entailment': 0, 'neutral': 1, 'contradiction': 2 El mismo está compuesto por tres dataset: TRAINING, con 392.702 datos; TEST, con 5.010 datos; VALIDATION, con 2.490 datos. Además se utiliza un vocabulario en español que contiene alrededor de 31.000 palabras, incluyendo los siguientes caracteres especiales: "[MASK]", "[PAD]", "[EOS]","[UNK]","[CLS]","[SEP]" que se encuentran en las primeras posiciones del vocabulario. Dicho vocabulario surge del modelo de Huggigface cuyo model_name es "dccuchile/bert-base-spanish-wwm-uncased". Método Tokenización En primer lugar importamos AutoTokenizer y obtenemos el tokenizador del modelo definido anteriormente. El mismo, al tokenizar adicionalmente de convertir los tokens o palabras en su ID del vocabulario, le incorpora al inicio el id del carácter especial “CLS” y al final el “SEP”. Además fijamos como parámetro la longitud máxima del modelo (tokenizer.model_max_length) esto genera que corte las premisas y las hipotesis si son mas largas y que las complete con padding si son mas cortas hasta completar la longitud deseada (con “PAD”).. Notamos que este tokenizador contiene funciones como las del itos y el stoi ya generadas. Procedemos a tokenizar el dataset a utilizando la función map, tanto para la premisa como la hipótesis. Armado de Batches Con la tokenizacion realizada procedemos a separar los Batches, para lo cual usamos el dataloader de torch. El resultado serán batches de tamaño 32 para el dataset de train, y 16 tanto para el de validación como para el de test. Sus dimensiones son el tamaño de cada batch x la cantidad de elementos. En el caso de la premisa y la hipótesis la cantidad de elementos será el largo utilizado para la tokenización mientras que en el caso del label, al ser único, la dimension será del tamaño del batch x 1. Asimismo incorporamos a los batches el attention_mask de la premisa y de la hipótesis. Modelo base BERT es una red pre-entrenada de transformadores (...). El input de BERT consiste en las dos oraciones separadas por un token especial [SEP]. (...) yla salida se pasa a una función de regresión simple para derivar la etiqueta final. (Reimers and Gurevich, 2019, p. 2). Sobre este modelo base, se realizó el finetuning de nuestra red, basándonos en el siguiente diagrama (Reimers and Gurevich, 2019, p. 3).: Es decir que lo que haremos será pasar la premisa y la hipótesis por una BERT, obteniendo luego un pooler output para cada una de ellas (“u” y “v”). Luego se concatenan, junto con el módulo de la diferencia, y ese resultado es pasado por la capa lineal obteniendo 3 resultados, que serán las probabilidades asociadas a cada label. Se procedió a entrenar la red con el dataset de train, utilizando como función de pérdida la entropía cruzada, y luego se procedió a validar el modelo. Los resultados se exponen en la próxima sección. En este caso el modelo tiene dos epocas completas de entrenamiento
zhangjian94cn/Taxi-v3	zhangjian94cn	2023-06-17T01:33:35Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T01:33:26Z	--- 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="zhangjian94cn/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"]) ```
UofA-LINGO/text-to-triplets-explanation-v2	UofA-LINGO	2023-06-17T00:41:02Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-06-17T00:38:48Z	--- license: mit --- LoRA weights for `LLaMA-7B` Trained on 'taesiri/webnlg-triplets-explanation-v1' for 4 epochs. Command: ``` WORLD_SIZE=2 CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=1234 finetune.py --base_model='decapoda-research/llama-7b-hf' --data_path 'taesiri/webnlg-triplets-explanation-v1' --num_epochs=4 --cutoff_len=512 --group_by_length --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' --lora_r=8 --micro_batch_size=8 --batch_size=32 ```
UofA-LINGO/text-to-triplets-explanation-v1	UofA-LINGO	2023-06-17T00:39:59Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-06-16T22:26:38Z	--- license: mit --- LoRA weights for `LLaMA-7B` Trained on 'taesiri/webnlg-triplets-explanation-v1' for 2 epochs.
arsalsyed/distilgpt2-finetuned-wikitext2	arsalsyed	2023-06-17T00:14:20Z	135	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-16T23:41:59Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 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. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6420 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7596 \| 1.0 \| 2334 \| 3.6651 \| \| 3.6543 \| 2.0 \| 4668 \| 3.6468 \| \| 3.6024 \| 3.0 \| 7002 \| 3.6420 \| ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
H4nan/dqn-SpaceInvadersNoFrameskip-v4	H4nan	2023-06-16T23:54:53Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T18:30:15Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 537.00 +/- 181.32 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga H4nan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga H4nan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga H4nan ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Ioanaaaaaaa/bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay	Ioanaaaaaaa	2023-06-16T23:47:54Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-16T23:30:14Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay 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: 0.941 - name: F1 type: f1 value: 0.9411169346964399 --- <!-- 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-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2591 - Accuracy: 0.941 - F1: 0.9411 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.0799 \| 1.0 \| 250 \| 0.1898 \| 0.9375 \| 0.9377 \| \| 0.0516 \| 2.0 \| 500 \| 0.2290 \| 0.938 \| 0.9383 \| \| 0.0386 \| 3.0 \| 750 \| 0.2107 \| 0.9415 \| 0.9419 \| \| 0.0195 \| 4.0 \| 1000 \| 0.2607 \| 0.9435 \| 0.9433 \| \| 0.0149 \| 5.0 \| 1250 \| 0.2591 \| 0.941 \| 0.9411 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
sam34738/mBERT	sam34738	2023-06-16T23:44:39Z	186	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-16T20:24:12Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: mbert 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. --> # mbert This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9812 - Accuracy: 0.6583 - F1: 0.6948 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-05 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.749 \| 1.0 \| 2100 \| 0.7068 \| 0.4994 \| 0.0131 \| \| 0.7707 \| 2.0 \| 4200 \| 0.9812 \| 0.6583 \| 0.6948 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
TacLucas/Test	TacLucas	2023-06-16T23:07:19Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-06-16T23:06:14Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ghze/Taxi_v3	ghze	2023-06-16T23:00:53Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T23:00:48Z	--- 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 +/- 2.71 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="ghze/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"]) ```
ghze/Taxi	ghze	2023-06-16T22:59:16Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T22:59:09Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="ghze/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
amjadfqs/finalProject	amjadfqs	2023-06-16T22:28:48Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-15T17:30:57Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision model-index: - name: finalProject 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: 0.9890023566378633 - name: Precision type: precision value: 0.9894345375382527 --- <!-- 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. --> # finalProject This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0411 - Accuracy: 0.9890 - F1 Score: 0.9892 - Precision: 0.9894 - Sensitivity: 0.9891 - Specificity: 0.9972 ## 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: 0.0001 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Score \| Precision \| Sensitivity \| Specificity \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:---------:\|:-----------:\|:-----------:\| \| 0.3384 \| 1.0 \| 30 \| 0.2387 \| 0.9144 \| 0.9163 \| 0.9197 \| 0.9146 \| 0.9781 \| \| 0.1608 \| 2.0 \| 60 \| 0.1635 \| 0.9466 \| 0.9476 \| 0.9485 \| 0.9474 \| 0.9865 \| \| 0.0953 \| 3.0 \| 90 \| 0.0915 \| 0.9698 \| 0.9703 \| 0.9706 \| 0.9706 \| 0.9924 \| \| 0.0573 \| 4.0 \| 120 \| 0.1125 \| 0.9607 \| 0.9617 \| 0.9634 \| 0.9621 \| 0.9901 \| \| 0.0335 \| 5.0 \| 150 \| 0.0536 \| 0.9827 \| 0.9831 \| 0.9837 \| 0.9826 \| 0.9957 \| \| 0.0185 \| 6.0 \| 180 \| 0.0543 \| 0.9827 \| 0.9830 \| 0.9837 \| 0.9825 \| 0.9957 \| \| 0.0226 \| 7.0 \| 210 \| 0.0478 \| 0.9859 \| 0.9861 \| 0.9866 \| 0.9856 \| 0.9965 \| \| 0.0131 \| 8.0 \| 240 \| 0.0468 \| 0.9843 \| 0.9846 \| 0.9847 \| 0.9846 \| 0.9961 \| \| 0.0087 \| 9.0 \| 270 \| 0.0411 \| 0.9890 \| 0.9892 \| 0.9894 \| 0.9891 \| 0.9972 \| \| 0.0043 \| 10.0 \| 300 \| 0.0376 \| 0.9886 \| 0.9888 \| 0.9890 \| 0.9887 \| 0.9971 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3
devonho/my_awesome_opus_books_model	devonho	2023-06-16T22:28:30Z	8	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus100", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-06T07:28:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: my_awesome_opus_books_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 config: en-ja split: test args: en-ja metrics: - name: Bleu type: bleu value: 23.8215 --- <!-- 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_opus_books_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.4506 - Bleu: 23.8215 - Gen Len: 4.6055 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-------:\|:---------------:\|:-------:\|:-------:\| \| 0.4468 \| 1.0 \| 500000 \| 0.4585 \| 23.9023 \| 4.705 \| \| 0.4397 \| 2.0 \| 1000000 \| 0.4506 \| 23.8215 \| 4.6055 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
maren-hugg/xlm-roberta-base-finetuned-panx-en-custom	maren-hugg	2023-06-16T21:56:26Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-12T06:49:39Z	--- license: mit tags: - generated_from_trainer metrics: - f1 - precision - recall - accuracy model-index: - name: xlm-roberta-base-finetuned-panx-en-custom results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en-custom This model is a fine-tuned version of [maren-hugg/xlm-roberta-base-finetuned-panx-en](https://huggingface.co/maren-hugg/xlm-roberta-base-finetuned-panx-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1045 - F1: 0.8782 - Precision: 0.8496 - Recall: 0.9088 - Accuracy: 0.9754 ## 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: 4.886597454037411e-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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Precision \| Recall \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:---------:\|:------:\|:--------:\| \| 0.128 \| 0.75 \| 24 \| 0.1087 \| 0.8514 \| 0.8299 \| 0.8740 \| 0.9713 \| \| 0.074 \| 1.5 \| 48 \| 0.1006 \| 0.8637 \| 0.8505 \| 0.8773 \| 0.9750 \| \| 0.0506 \| 2.25 \| 72 \| 0.0987 \| 0.8728 \| 0.8587 \| 0.8872 \| 0.9749 \| \| 0.0393 \| 3.0 \| 96 \| 0.1045 \| 0.8782 \| 0.8496 \| 0.9088 \| 0.9754 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
Enterprize1/ppo-LunarLander-v2	Enterprize1	2023-06-16T21:45:24Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T21:45:00Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 242.78 +/- 66.66 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
stanford-crfm/music-small-ar-inter-100k	stanford-crfm	2023-06-16T21:28:37Z	182	1	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-05T00:04:27Z	--- license: apache-2.0 --- This is a Small (112M parameter) Transformer trained for 100k steps on interarrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
stanford-crfm/music-small-ar-800k	stanford-crfm	2023-06-16T21:28:12Z	183	1	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-05T00:01:12Z	--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
FALLENSTAR/Volvo850LoRa	FALLENSTAR	2023-06-16T21:28:07Z	0	0	null	[ "region:us" ]	null	2023-06-11T17:03:34Z	![00008-2488555434.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/ttKEY5ht3tpprSQP1bh6x.png) ![00010-1641710529.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/9DeVYLdy4z_5-GheRX7mp.png) ![00012-3333952880.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/_CXajXOTGrrAWV-hwQSvX.png) ![00014-2737276302.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/WdKhjgwrb7IKVIZH70geW.png) ![00018-4039153412.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/fgMyYsKjaRvQzN30z7LwU.png) ![00024-759743265.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/iaJIqLM6wfV_7tERVKtnf.png) ![00029-2851129221.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/cnHpl9xmcr7tChZIEMxpq.png) ![00030-857339483.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/eaJLLbx10bqGTvVM5n8im.png) ![00038-414082621.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/qWRmZchGH9LpFpcuK0-7E.png) ![00040-3062714132.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/aMnkr_RvEYs-_obSmQlF5.png)
stanford-crfm/music-small-ar-100k	stanford-crfm	2023-06-16T21:27:39Z	184	0	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-04T23:58:03Z	--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 100k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
stanford-crfm/music-small-100k	stanford-crfm	2023-06-16T21:26:29Z	181	0	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-04T23:26:52Z	--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 100k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
stanford-crfm/music-medium-800k	stanford-crfm	2023-06-16T21:25:52Z	572	4	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-05T00:17:20Z	--- license: apache-2.0 --- This is a Medium (360M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
jondurbin/airoboros-65b-gpt4-1.2-peft	jondurbin	2023-06-16T21:01:26Z	0	0	null	[ "dataset:jondurbin/airoboros-gpt4-1.2", "license:other", "region:us" ]	null	2023-06-14T09:11:36Z	--- license: other datasets: - jondurbin/airoboros-gpt4-1.2 --- peft weights of https://hugginface.co/jondurbin/airoboros-65b-gpt4-1.2, see that card for details
Schnitzl/detr-resnet-50_finetuned_cppe5	Schnitzl	2023-06-16T20:54:42Z	191	0	transformers	[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-06-16T17:17:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: detr-resnet-50_finetuned_cppe5 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. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.13.0 - Tokenizers 0.13.3
crlandsc/bsrnn-bass	crlandsc	2023-06-16T20:24:33Z	0	1	null	[ "audio source separation", "music demixing", "band-split recurrent neural network", "bsrnn", "spectrogram", "bass", "region:us" ]	null	2023-06-16T20:16:53Z	--- tags: - audio source separation - music demixing - band-split recurrent neural network - bsrnn - spectrogram - bass --- # Model Card for bsrnn-bass Bass model for [Music-Demixing-with-Band-Split-RNN](https://github.com/crlandsc/Music-Demixing-with-Band-Split-RNN).
sngsfydy/resnet-50-finetuned-eurosat	sngsfydy	2023-06-16T20:17:05Z	209	0	transformers	[ "transformers", "pytorch", "tensorboard", "resnet", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-16T19:14:44Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: resnet-50-finetuned-eurosat 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. --> # resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0706 - Accuracy: 0.5152 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6069 \| 0.99 \| 20 \| 1.5839 \| 0.3879 \| \| 1.5395 \| 1.98 \| 40 \| 1.4860 \| 0.5485 \| \| 1.4321 \| 2.96 \| 60 \| 1.3500 \| 0.5364 \| \| 1.3292 \| 4.0 \| 81 \| 1.1826 \| 0.5212 \| \| 1.233 \| 4.99 \| 101 \| 1.0706 \| 0.5152 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
blevlabs/alpaca-7b	blevlabs	2023-06-16T20:16:19Z	6	0	transformers	[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]	null	2023-06-15T16:01:29Z	Found. Redirecting to https://cdn-lfs.hf.co/repos/d1/e7/d1e74b32fb5b1cbe2ba63f424b0731e786a5ae300beb41ac103060925f9becff/d17913b0f97b6c92903cbf2c6d8ff9e412644769ba8ea848fd4dc4246a38c8d0?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1739044824&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczOTA0NDgyNH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy9kMS9lNy9kMWU3NGIzMmZiNWIxY2JlMmJhNjNmNDI0YjA3MzFlNzg2YTVhZTMwMGJlYjQxYWMxMDMwNjA5MjVmOWJlY2ZmL2QxNzkxM2IwZjk3YjZjOTI5MDNjYmYyYzZkOGZmOWU0MTI2NDQ3NjliYThlYTg0OGZkNGRjNDI0NmEzOGM4ZDA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=sUzC3YAwA35JO1%7EsqsOl3QIpzizB%7EEv%7EXYdYit3C5o8V8Suda2NcVf2R2yyjugIu%7EIl4lNae7CS-CFdgqk7b%7Eo86c3g3cLEPoPSpj-7li1vD14C0df9YB6OzS26xfSPL1mxTVlK3NdDAeGXwlIcfEJHK2xJeMYL9Yw5J0IZgdyffOdyAw2GMaQdoQ2puzRSjGJyykxQ7ESbDV135z-qhCWsn1XiKFlXKM2xrx7K1nZBcEmNKd9nwVWSWoh5XB69c8dOvT91vTel6l15B1aAJzTRYyQfJEv0vI0m7Vuf8kXSHNP1Unl8bNFulxPAIaOmw5QjTnT4yUbnuwJlCImU74A__&Key-Pair-Id=K3RPWS32NSSJCE
FALLENSTAR/CedricGloriaLoRa	FALLENSTAR	2023-06-16T20:10:23Z	0	0	null	[ "region:us" ]	null	2023-06-09T20:58:56Z	### Model Description First of all, it's LoRa. It is based on my favorite Nissan Cedric/Gloria Y31 Hardtop from the years '87-91. It is a test model, so it has defects. I don't remember how many samples and epochs were used in it... But, with some of the checkpoints it turns out very similar and funny. The best images I was able to get with this LoRa were at these settings: Steps: 25 Sampler: DPM++ SDE Karras, CFG scale: 6.5 and with LoRa strength 0.8-1 ### Results ![00056-2998859893.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/Hqc-19MXbw-tOHcytmCgO.png) ![00037-1095574331.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/6d8uCXt5U2KvnqtZdl1d2.png) ![00039-89618184.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/tmzZoZQT6fK4_UPCCA5sk.png) ![00041-562450270.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/tp0QNuodfbsEkgLh8Yhht.png) ![00066-2959432492.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/FO5BR5uRtvAwm7jMCzUTa.png) ![00058-1357569836.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/yC3gSTgtQnnBTg3ibDtXw.png) ![00065-3713693640.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/xRNkGnsBb8kiW15_v7nws.png) ![00013-3313807285.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/1r1CFhjES8GncHPFudrGZ.png) ![00067-1452084223.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/8a86GW52YDweESQKp5fRR.png) ![00004-873995140.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/ZCu2ffZkEGW_DlLWYu6QC.png) ![00084-419571825.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/4PXZdvNBX-kWwMyyZ1bRm.png) ![00021-3472542211.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/_2fwDF2KUlXq7bS-P65Hi.png) ![00048-2623366528.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/m-XuUdhEXhT7-Vaa_D0pa.png)
GEMCorp/q-FrozenLake-v1-4x4-noSlippery	GEMCorp	2023-06-16T19:51:18Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T19:51:12Z	--- 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="GEMCorp/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"]) ```
ChristineCheng/my_awesome_eli5_clm-model	ChristineCheng	2023-06-16T19:49:19Z	61	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-16T19:33:04Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ChristineCheng/my_awesome_eli5_clm-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. --> # ChristineCheng/my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7347 - Validation Loss: 3.7399 - 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.9119 \| 3.7667 \| 0 \| \| 3.7942 \| 3.7493 \| 1 \| \| 3.7347 \| 3.7399 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
SSSSSSSSSSSJJJJJJJJJJJJJ/my_awesome_eli5_clm-model	SSSSSSSSSSSJJJJJJJJJJJJJ	2023-06-16T19:44:19Z	179	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-16T19:13:40Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-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_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7341 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8765 \| 1.0 \| 1120 \| 3.7555 \| \| 3.7769 \| 2.0 \| 2240 \| 3.7368 \| \| 3.7331 \| 3.0 \| 3360 \| 3.7341 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
crlandsc/tiny-audio-diffusion-snares	crlandsc	2023-06-16T19:25:10Z	3	1	null	[ "audio", "diffusion", "waveform diffusion", "audio diffusion", "unet", "region:us" ]	null	2023-06-10T15:20:00Z	--- tags: - audio - diffusion - waveform diffusion - audio diffusion - unet --- # Model Card for tiny-audio-diffusion-snares Snare drum model for tiny-audio-diffusion. Use with [tiny-audio-diffusion](https://github.com/crlandsc/tiny-audio-diffusion) repo to generate snare drum samples.
ananay/kneearch	ananay	2023-06-16T19:17:59Z	22	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-16T19:05:11Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### kneearch Dreambooth model trained by ananay with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
AustinCarthy/OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5	AustinCarthy	2023-06-16T19:17:42Z	0	0	null	[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	2023-06-16T15:49:03Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5 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. --> # OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_OnlyPhishGPT2_using_benigh_200K_top_p_0.75 dataset. It achieves the following results on the evaluation set: - Loss: 0.0192 - Accuracy: 0.9978 - F1: 0.9767 - Precision: 0.9994 - Recall: 0.955 - Roc Auc Score: 0.9775 - Tpr At Fpr 0.01: 0.9632 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc Score \| Tpr At Fpr 0.01 \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------------:\|:---------------:\| \| 0.0057 \| 1.0 \| 35625 \| 0.0113 \| 0.9979 \| 0.9779 \| 0.9954 \| 0.961 \| 0.9804 \| 0.9518 \| \| 0.0035 \| 2.0 \| 71250 \| 0.0150 \| 0.9975 \| 0.9726 \| 0.9983 \| 0.9482 \| 0.9741 \| 0.95 \| \| 0.0011 \| 3.0 \| 106875 \| 0.0175 \| 0.9975 \| 0.9727 \| 0.9994 \| 0.9474 \| 0.9737 \| 0.9554 \| \| 0.0009 \| 4.0 \| 142500 \| 0.0160 \| 0.9979 \| 0.9778 \| 0.9990 \| 0.9576 \| 0.9788 \| 0.9618 \| \| 0.0 \| 5.0 \| 178125 \| 0.0192 \| 0.9978 \| 0.9767 \| 0.9994 \| 0.955 \| 0.9775 \| 0.9632 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
YoneShiro/SpaceInvadersNoFrameskip-v4	YoneShiro	2023-06-16T19:14:05Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T19:13:20Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 708.00 +/- 250.51 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga YoneShiro -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga YoneShiro -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga YoneShiro ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Boristss/modellunarlander	Boristss	2023-06-16T19:13:23Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T19:12:58Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.44 +/- 21.50 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
JvThunder/ppo-Pyramids	JvThunder	2023-06-16T18:37:51Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-06-16T18:37:41Z	--- 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: JvThunder/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sambanovasystems/starcoder-toolbench	sambanovasystems	2023-06-16T18:23:22Z	23	4	transformers	[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "arxiv:2305.16504", "arxiv:2305.06161", "license:bigcode-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-24T04:41:14Z	--- license: bigcode-openrail-m --- # starcoder-toolbench <!-- Provide a quick summary of what the model is/does. --> starcoder-toolbench is a 15 billion parameter model used for api based action generation. It is instruction tuned from [starcoder](https://huggingface.co/bigcode/starcoder) on api based action generation datasets. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [SambaNova Systems](https://sambanova.ai/) - Model type: Language Model - Language(s): English - License: bigcode-openrail-m - Finetuned from model: [starcoder](https://huggingface.co/bigcode/starcoder) ### Basic Information <!-- Provide the basic links for the model. --> - Paper: [link](https://arxiv.org/abs/2305.16504) - Github: [link](https://github.com/sambanova/toolbench) ## Uses <details> <summary>Click to expand</summary> <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> This model is intended for commercial and research use. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> starcoder-toolbench should NOT be used for purpose other than API based action generation. </details> --- ## How to Get Started with the Model <details> <summary>Click to expand</summary> ### Loading in model with Huggingface ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/starcoder-toolbench") model = AutoModelForCausalLM.from_pretrained("sambanovasystems/starcoder-toolbench", device_map="auto", torch_dtype="auto") ``` ### Example Prompts To Try in GPU Tutorial Prompt 1: ``` I have the following set of API:\n\n# To set the maximum commute time in minute to your office location, assuming the office location is already defined\nAPI.set_max_commute_time(value: int)\n\n# To set the maximum home size in square feet\nAPI.set_max_square_feet(value: int)\n\n# To set the minimum home price in dollars\nAPI.set_min_price(value: int)\n\n# To set the number of garage(s)\nAPI.set_num_garages(value: int)\n\n# To set home types for search. For home buying, home_types choices are: \"House\", \"Townhouse\", \"Condo\", \"Land\", \"Multi-family\", \"Mobile\", \"Co-op\"; for home renting, home_types choices are: \"House\", \"Townhouse\", \"Condo\", \"Apartment\".\nAPI.select_home_type(home_types: List[str])\n\n# To set the number of balconies\nAPI.set_num_balconies(value: int)\n\n# Submit criterion to get search results. This function should be called after setting all the criterion.\nAPI.search()\n\n# To set the floor number\nAPI.set_floor_number(value: int)\n\n# To set the number of bedroom(s)\nAPI.set_num_beds(value: int)\n\n# To set the number of swimming pool(s)\nAPI.set_num_swimming_pools(value: int)\n\n# To set the maximum home price in dollars\nAPI.set_max_price(value: int)\n\n# To specify whether to search homes for buying or renting. 'value' can be chosen from ['buy', 'rent']. This function must be called after setting the location and before setting any other criteria.\nAPI.set_buy_or_rent(value: str)\n\n# To set the number of bathroom(s)\nAPI.set_num_baths(value: float)\n\n# To set the location for the search area. This function must be called before setting any criteria.\nAPI.set_location(value: string)\n\n# To set the minimum home size in square feet\nAPI.set_min_square_feet(value: int)\n\n-------------\n\nTask: Looking for homes to rent in Santa Clarita with a price range between $110000 and $1753000, a minimum of 1700 square feet, at least 2 balconies, and 3.5 bathrooms.\nAction:\n ``` Prompt 2: ``` I have the following set of API:\n\n# To set the location for hotel search, given a Loc object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_hotel_location(Loc)\n\n# To set the number of hotel rooms to book.\nAPI.set_num_rooms(value)\n\n# To set the location for departure, given a Loc object. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.set_origin(Loc)\n\n# To select the transportation type from ['flight', 'train', 'bus', 'cruise']. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.select_transportation(transportation_type)\n\n# To set the return date of the trip, given a Date object. If booking type is 'both' and this function is not called explicitly, 'return_date' will be set to 'hotel_checkout_date' implicitly.\nAPI.set_return_date(Date)\n\n# To set the hotel check-in date, given a Date object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_checkin_date(Date)\n\n# To define a date.\ndate = Date(month, day, year)\n\n# To set the departure date of the trip, given a Date object. This function must be called if booking type is 'trip tickets'. If booking type is 'both' and this function is not called explicitly, 'departure_date' will be set to 'hotel_checkin_date' implicitly.\nAPI.set_departure_date(Date)\n\n# To set the location for arrival, given a Loc object. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.set_destination(Loc)\n\n# To define a location of a given city 'City'.\nlocation = Loc('City')\n\n# To set maximum hotel room price.\nAPI.set_max_room_price(value)\n\n# To set minimum ticket price.\nAPI.set_min_ticket_price(value)\n\n# To select the booking type from ['hotels', 'trip tickets', 'both']. This function must be called before setting any criteria.\nAPI.select_booking_type(booking_type)\n\n# To set minimum hotel room price.\nAPI.set_min_room_price(value)\n\n# To set the number of child tickets to purchase.\nAPI.set_num_children(value)\n\n# To set the number of adult tickets to purchase.\nAPI.set_num_adults(value)\n\n# To select the hotel room type from ['King Bed', 'Queen Bed', 'Double', 'Luxury'].\nAPI.select_room_type(room_type)\n\n# To set maximum ticket price.\nAPI.set_max_ticket_price(value)\n\n# Submit criterion to get search results. This function should be called after setting all the criterion.\nAPI.search()\n\n# To set the hotel check-out date, given a Date object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_checkout_date(Date)\n\n-------------\n\nTask: Looking to book 2 adult and 4 child tickets from Stockton to Baltimore by cruise, on 2023-07-29.\nAction:\n ``` </details> --- ## Training Details <details> <summary>Click to expand</summary> ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> The training data is curated for the 8 tasks in ToolBench. See Appendix A of the [paper](https://arxiv.org/abs/2305.16504) for task details and Appendix C.1 for the training data curation details. In total, there are 9704 training samples, organized in all-shot format as described in Appendix C.2. Here is the [download link](https://drive.google.com/file/d/1lUatLGnSVhfy1uVIPEQ7qCoLtnCIXi2O/view?usp=sharing) to the training data. ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> We trained starcoder-toolbench on 4 80GB A100 gpu's. We started from [starcoder](https://huggingface.co/bigcode/starcoder) and finetuned it on the dataset mentioned above. ### Hyperparameters - Hardware: A100 GPU - Optimizer: AdamW - Grad accumulation: 1 - Epochs: 8 - Global Batch size: 16 - Batch tokens: 16 * 2048 = 32,768 tokens - Learning Rate: 1e-5 - Learning Rate Scheduler: Fixed LR - Weight decay: 0.1 </details> ## Acknowledgment We would like to express our gratitude to the great work done in [StarCoder: may the source be with you!](https://arxiv.org/abs/2305.06161) ## Cite starcoder-toolbench ``` @misc{xu2023tool, title={On the Tool Manipulation Capability of Open-source Large Language Models}, author={Qiantong Xu and Fenglu Hong and Bo Li and Changran Hu and Zhengyu Chen and Jian Zhang}, year={2023}, eprint={2305.16504}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

takaha202306/facelora

takaha202306

2023-06-17T17:54:59Z

0

1

null

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

null

2023-06-17T16:17:55Z

--- license: creativeml-openrail-m --- editing # about This repository contains two Stable Diffusion LoRAs for generate "Cute MOE face" illustration, and their training data. There was trained by [kohya-ss's sd_scripts](https://github.com/kohya-ss/sd-scripts), and tested on [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui). The base model is almost AOM3, the model I used was added a little merge as: [AOM3.safetensors](https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix3/AOM3_orangemixs.safetensors) + [Hipoly 3D Model LoRA v2.0](https://civitai.com/models/8730/hipoly-3d-model-lora):0.15:0,1,1,1,1,1,1,0.1,0.1,0.1,0.1,1,1,1,0.3,0.1,0.1. Hipoly 3d LoRA was merged by [SuperMerger](https://github.com/hako-mikan/sd-webui-supermerger). They don't require "trigger word", simply changes illustration's style when applied. I recommend to set weight between 0.05 to 0.4, and use [LoRA Block Weight extension](https://github.com/hako-mikan/sd-webui-lora-block-weight). # facelora1 First LoRA is actually not made for this purpose. I attempted to make "Uchinoko"(means my daughter, favorite original character, lol) LoRA. Train data are my favorite images(those are generated above model by me), and augmentation images made by [controlnet's reference-only](https://github.com/Mikubill/sd-webui-controlnet#reference-only-control). This LoRA worked well, worked better than I intended, and could be used to generate beautiful girls in a generic way. # facelora2 I generated various beautiful girls images with above LoRA, and made second LoRA trained by them. This LoRA is more suitable for general purpose generation. # samples

Atnafu/amharic_xlmr_base

Atnafu

2023-06-17T17:36:41Z

103

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-17T02:34:11Z

--- license: mit tags: - generated_from_trainer model-index: - name: amh_base results: [] ---  # amh_base This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3301 ## 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: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.13.0 - Tokenizers 0.13.3

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

mmirmahdi

2023-06-17T17:22:53Z

0

null

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

reinforcement-learning

2023-06-17T17:22:23Z

--- 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="mmirmahdi/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"]) ```

0xghagevaibhav/meMyself

0xghagevaibhav

2023-06-17T17:11:20Z

0

null

[ "hi", "license:unknown", "region:us" ]

null

2023-06-17T17:09:19Z

--- license: unknown language: - hi ---

erens/mikasalast

erens

2023-06-17T17:01:46Z

30

0

diffusers

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

text-to-image

2023-06-17T16:46:29Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mikasaLAST Dreambooth model trained by erens with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

DionTimmer/controlnet_qrcode

DionTimmer

2023-06-17T16:33:13Z

2,526

306

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "controlnet", "en", "license:openrail++", "region:us" ]

null

2023-06-15T02:23:37Z

--- tags: - stable-diffusion - controlnet license: openrail++ language: - en --- # QR Code Conditioned ControlNet Models for Stable Diffusion 1.5 and 2.1 ![1](imgs/1.png) ## Model Description These ControlNet models have been trained on a large dataset of 150,000 QR code + QR code artwork couples. They provide a solid foundation for generating QR code-based artwork that is aesthetically pleasing, while still maintaining the integral QR code shape. The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, a 1.5 version model was also trained on the same dataset for those who are using the older version. Separate repos for usage in diffusers can be found here: 1.5: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15 2.1: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v11p_sd21 ## How to use with Diffusers ```bash pip -q install diffusers transformers accelerate torch xformers ``` ```python import torch from PIL import Image from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler from diffusers.utils import load_image controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16) pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.enable_xformers_memory_efficient_attention() pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() def resize_for_condition_image(input_image: Image, resolution: int): input_image = input_image.convert("RGB") W, H = input_image.size k = float(resolution) / min(H, W) H *= k W *= k H = int(round(H / 64.0)) * 64 W = int(round(W / 64.0)) * 64 img = input_image.resize((W, H), resample=Image.LANCZOS) return img # play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image # qr code image source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png") # initial image, anything init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg") condition_image = resize_for_condition_image(source_image, 768) init_image = resize_for_condition_image(init_image, 768) generator = torch.manual_seed(123121231) image = pipe(prompt="a bilboard in NYC with a qrcode", negative_prompt="ugly, disfigured, low quality, blurry, nsfw", image=init_image, control_image=condition_image, width=768, height=768, guidance_scale=20, controlnet_conditioning_scale=1.5, generator=generator, strength=0.9, num_inference_steps=150, ) image.images[0] ``` ## Performance and Limitations These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).** To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork. ## Installation The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application. For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail. Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell. ![2](imgs/2.png) ![3](imgs/3.png) ![4](imgs/4.png)

dnjdsxor21/roberta-klue-ssm

dnjdsxor21

2023-06-17T16:32:52Z

118

0

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "ko", "dataset:dnjdsxor21/preprocessed-wiki-kor", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-11T13:40:11Z

--- widget: - text: 이순신은 [MASK] 중기의 무신이다. - text: 오바마는 미국의 [MASK] 이다. language: - ko pipeline_tag: fill-mask datasets: - dnjdsxor21/preprocessed-wiki-kor mask_token: '[MASK]' --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use

mustika/alan3

mustika

2023-06-17T16:21:59Z

0

null

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

null

2023-06-17T15:09:00Z

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

vlkn/flan-t5-small-taboo-for-llms

vlkn

2023-06-17T16:20:59Z

15

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-03T13:32:22Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-taboo-for-llms results: [] ---  # flan-t5-small-taboo-for-llms This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4825 - Rouge1: 27.3897 - Rouge2: 9.9232 - Rougel: 24.2026 - Rougelsum: 24.6485 - Gen Len: 18.5172 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 137 | 2.5897 | 26.6789 | 9.9538 | 23.6637 | 24.2407 | 18.3621 | | No log | 2.0 | 274 | 2.5560 | 25.4162 | 9.6277 | 22.7084 | 23.0883 | 18.3966 | | No log | 3.0 | 411 | 2.5377 | 26.0239 | 9.7748 | 23.4425 | 23.7935 | 18.6034 | | 2.8204 | 4.0 | 548 | 2.5241 | 26.6294 | 9.9168 | 23.8023 | 24.2756 | 18.7241 | | 2.8204 | 5.0 | 685 | 2.5120 | 25.8274 | 9.9333 | 23.8865 | 24.0724 | 18.7586 | | 2.8204 | 6.0 | 822 | 2.5031 | 26.7774 | 9.9651 | 24.3654 | 24.6102 | 18.6034 | | 2.8204 | 7.0 | 959 | 2.4985 | 26.5058 | 10.0422 | 24.0403 | 24.635 | 18.4655 | | 2.6101 | 8.0 | 1096 | 2.4934 | 26.6953 | 9.9536 | 24.0293 | 24.6809 | 18.4655 | | 2.6101 | 9.0 | 1233 | 2.4907 | 26.7978 | 9.6249 | 23.714 | 23.9992 | 18.6034 | | 2.6101 | 10.0 | 1370 | 2.4847 | 27.2135 | 9.878 | 23.8398 | 24.2389 | 18.5 | | 2.4726 | 11.0 | 1507 | 2.4856 | 27.1799 | 9.9337 | 23.9393 | 24.4067 | 18.5172 | | 2.4726 | 12.0 | 1644 | 2.4835 | 27.4491 | 10.1828 | 24.0926 | 24.4819 | 18.5 | | 2.4726 | 13.0 | 1781 | 2.4825 | 27.3897 | 9.9232 | 24.2026 | 24.6485 | 18.5172 | | 2.4726 | 14.0 | 1918 | 2.4836 | 27.5567 | 10.7405 | 24.2497 | 24.6566 | 18.5345 | | 2.3731 | 15.0 | 2055 | 2.4872 | 27.7517 | 11.0182 | 24.1007 | 24.7218 | 18.4828 | | 2.3731 | 16.0 | 2192 | 2.4852 | 27.3461 | 11.3381 | 24.084 | 24.5125 | 18.4655 | | 2.3731 | 17.0 | 2329 | 2.4872 | 27.3558 | 11.1005 | 24.047 | 24.4973 | 18.4655 | | 2.3731 | 18.0 | 2466 | 2.4841 | 26.9427 | 10.9288 | 23.7324 | 24.4298 | 18.5345 | | 2.2967 | 19.0 | 2603 | 2.4881 | 27.5 | 10.8437 | 24.1593 | 24.6028 | 18.4483 | | 2.2967 | 20.0 | 2740 | 2.4908 | 27.517 | 11.0039 | 24.1049 | 24.7111 | 18.5 | | 2.2967 | 21.0 | 2877 | 2.4917 | 27.7333 | 10.935 | 24.4076 | 24.9887 | 18.4138 | | 2.2553 | 22.0 | 3014 | 2.4926 | 27.6275 | 10.7562 | 24.2295 | 24.7476 | 18.4138 | | 2.2553 | 23.0 | 3151 | 2.4945 | 27.9085 | 10.943 | 24.6135 | 25.2373 | 18.4138 | | 2.2553 | 24.0 | 3288 | 2.4948 | 27.5261 | 10.7141 | 24.2429 | 24.816 | 18.4138 | | 2.2553 | 25.0 | 3425 | 2.4931 | 27.5522 | 10.8702 | 24.5576 | 25.0714 | 18.4655 | | 2.213 | 26.0 | 3562 | 2.4942 | 27.4758 | 11.0064 | 24.5062 | 25.05 | 18.4655 | | 2.213 | 27.0 | 3699 | 2.4954 | 27.6967 | 11.1744 | 24.7646 | 25.3172 | 18.4655 | | 2.213 | 28.0 | 3836 | 2.4951 | 27.7428 | 10.9365 | 24.6427 | 25.2432 | 18.5172 | | 2.213 | 29.0 | 3973 | 2.4949 | 27.6877 | 10.9522 | 24.6101 | 25.2471 | 18.4655 | | 2.1865 | 30.0 | 4110 | 2.4952 | 27.7295 | 11.0173 | 24.6556 | 25.2397 | 18.4655 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

rohitp1/ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8

rohitp1

2023-06-17T15:57:16Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-06-02T12:05:05Z

--- tags: - generated_from_trainer metrics: - wer model-index: - name: ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8 results: [] ---  # ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8 This model is a fine-tuned version of [rohitp1/kkkh_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_batch_16](https://huggingface.co/rohitp1/kkkh_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_batch_16) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0945 - Wer: 0.1041 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0562 | 2.46 | 250 | 0.0741 | 0.1135 | | 0.0538 | 4.92 | 500 | 0.0736 | 0.1126 | | 0.0506 | 7.38 | 750 | 0.0751 | 0.1116 | | 0.0465 | 9.84 | 1000 | 0.0752 | 0.1099 | | 0.0424 | 12.31 | 1250 | 0.0762 | 0.1089 | | 0.0385 | 14.77 | 1500 | 0.0790 | 0.1078 | | 0.0355 | 17.23 | 1750 | 0.0788 | 0.1062 | | 0.0335 | 19.69 | 2000 | 0.0795 | 0.1053 | | 0.0314 | 22.15 | 2250 | 0.0825 | 0.1052 | | 0.0298 | 24.61 | 2500 | 0.0837 | 0.1055 | | 0.0285 | 27.07 | 2750 | 0.0873 | 0.1049 | | 0.0274 | 29.53 | 3000 | 0.0868 | 0.1043 | | 0.0266 | 32.0 | 3250 | 0.0891 | 0.1044 | | 0.0256 | 34.46 | 3500 | 0.0902 | 0.1044 | | 0.0251 | 36.92 | 3750 | 0.0911 | 0.1044 | | 0.0247 | 39.38 | 4000 | 0.0926 | 0.1042 | | 0.0242 | 41.84 | 4250 | 0.0936 | 0.1042 | | 0.0238 | 44.3 | 4500 | 0.0940 | 0.1042 | | 0.0235 | 46.76 | 4750 | 0.0938 | 0.1042 | | 0.0233 | 49.22 | 5000 | 0.0945 | 0.1041 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1 - Datasets 2.7.1 - Tokenizers 0.11.0

Elvenson/stable_diffusion_weights

Elvenson

2023-06-17T15:50:04Z

0

null

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

null

2023-04-15T04:59:14Z

--- license: openrail --- # Stable Diffusion Model Weights This repo is mainly for storing the Keras weights for Stable Diffusion models. The model is adapted from [here](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion).

l3cube-pune/hindi-marathi-dev-albert

l3cube-pune

2023-06-17T15:34:53Z

112

0

transformers

[ "transformers", "pytorch", "albert", "fill-mask", "hi", "mr", "multilingual", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-19T19:19:20Z

--- language: - hi - mr - multilingual license: cc-by-4.0 --- ## DevAlBERT DevAlBERT is a Devanagari AlBERT model model trained on publicly available Hindi and Marathi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a>

l3cube-pune/kannada-bert

l3cube-pune

2023-06-17T15:32:28Z

127

0

transformers

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "kn", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-20T07:02:51Z

--- license: cc-by-4.0 language: kn --- ## KannadaBERT KannadaBERT is a Kannada BERT model trained on publicly available Kannada monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a>

l3cube-pune/hindi-albert

l3cube-pune

2023-06-17T15:32:02Z

142

1

transformers

[ "transformers", "pytorch", "albert", "fill-mask", "hi", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-19T18:36:25Z

--- license: cc-by-4.0 language: hi --- ## HindAlBERT HindAlBERT is a Hindi AlBERT model model trained on publicly available Hindi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] (<a href='http://dx.doi.org/10.13140/RG.2.2.14606.84809'> pdf </a>) ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a>

atrytone/scibert_uncased_claim_id

atrytone

2023-06-17T15:16:47Z

112

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-18T05:07:15Z

--- license: apache-2.0 language: - en --- Fine-tuned SciBERT uncased model [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) for claim detection from abstracts.

Hosioka/AniReal

Hosioka

2023-06-17T15:16:41Z

52

75

diffusers

[ "diffusers", "text-to-image", "stable-Diffusion", "stable-diffusion-diffusers", "safetensors", "en", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-01-20T11:45:12Z

--- license: creativeml-openrail-m thumbnail: "https://m1.afileditch.ch/uJoodjDNVWxDqhhQHeRH.png" language: - en tags: - text-to-image - stable-Diffusion - stable-diffusion-diffusers - diffusers - safetensors inference: true --- # Deprecated. Refer to this [New Version](https://huggingface.co/Hosioka/Baka-Diffusion) This Repository contains AniReal V1.0 ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # License 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. The authors 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

HoseinPanahi/finbert-lm-finetuned-news

HoseinPanahi

2023-06-17T15:14:00Z

113

1

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-17T12:01:59Z

--- tags: - generated_from_trainer model-index: - name: finbert-lm-finetuned-news results: [] ---  # finbert-lm-finetuned-news This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0723 ## 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: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3271 | 1.0 | 2736 | 2.2334 | | 2.0392 | 2.0 | 5472 | 2.0723 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

gombalmukiyo/gombalmukiyo

gombalmukiyo

2023-06-17T15:11:32Z

0

null

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

null

2023-06-17T15:09:52Z

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

zwzhang/Accountable-Textual-Visual-Chat

zwzhang

2023-06-17T15:10:10Z

0

2

null

[ "arxiv:2303.05983", "license:apache-2.0", "region:us" ]

null

2023-06-17T14:41:30Z

--- license: apache-2.0 --- ## Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation [[Paper]](https://arxiv.org/pdf/2303.05983.pdf) [[Project Page]](https://matrix-alpha.github.io/) [[GitHub]](https://github.com/matrix-alpha/Accountable-Textual-Visual-Chat) ![ The overall framework of ATVC.](atvc.png)

Bala-A87/Huggy-DRL

Bala-A87

2023-06-17T14:35:21Z

2

0

ml-agents

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

reinforcement-learning

2023-06-17T14:34:51Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Bala-A87/Huggy-DRL 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Bodolaz/Unit-2

Bodolaz

2023-06-17T14:17:03Z

0

null

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

reinforcement-learning

2023-06-17T14:16:43Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Unit-2 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="Bodolaz/Unit-2", 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"]) ```

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

Bodolaz

2023-06-17T13:46:43Z

0

null

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

reinforcement-learning

2023-06-17T13:46:27Z

--- 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="Bodolaz/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"]) ```

catrabbitbear/lunar-lander

catrabbitbear

2023-06-17T13:43:33Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T13:43:04Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.65 +/- 38.49 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

2tle/kobart-std-to-jeju

2tle

2023-06-17T13:41:43Z

104

1

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "ko", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-17T13:31:26Z

--- license: mit language: - ko metrics: - bleu --- # Korean Standard To Jejueo(Jeju Dialect) Translator BART Model ## Dataset - [AI Hub Korean Jejueo(Jeju Dialect) Voice data](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=121) ## Model Score - BLEU: 40%

tux/Reinforce-copter

tux

2023-06-17T13:23:28Z

0

null

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

reinforcement-learning

2023-06-17T11:34:21Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-copter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 18.70 +/- 15.84 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . 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

antphb/DS-Chatbox-bigscience-bloom-560m

antphb

2023-06-17T13:15:50Z

151

0

transformers

[ "transformers", "pytorch", "tensorboard", "bloom", "text-generation", "generated_from_trainer", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T11:27:42Z

--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer model-index: - name: DS-Chatbox-bigscience-bloom-560m results: [] ---  # DS-Chatbox-bigscience-bloom-560m This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 4.8320 - eval_runtime: 175.7948 - eval_samples_per_second: 37.402 - eval_steps_per_second: 4.676 - epoch: 0.03 - step: 500 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 3.0 ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

thiendio/ppo-from-scratch-lunar

thiendio

2023-06-17T12:26:39Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T12:26:16Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -158.29 +/- 101.80 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters

samata/my_awesome_billsum_model

samata

2023-06-17T12:20:04Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-17T12:09:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1423 ---  # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5563 - Rouge1: 0.1423 - Rouge2: 0.0518 - Rougel: 0.1171 - Rougelsum: 0.1171 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8514 | 0.1229 | 0.0326 | 0.1015 | 0.1016 | 19.0 | | No log | 2.0 | 124 | 2.6361 | 0.1308 | 0.0421 | 0.1066 | 0.1068 | 19.0 | | No log | 3.0 | 186 | 2.5725 | 0.139 | 0.0488 | 0.1138 | 0.114 | 19.0 | | No log | 4.0 | 248 | 2.5563 | 0.1423 | 0.0518 | 0.1171 | 0.1171 | 19.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

alexanderjoossens/w2v2-libri-10min

alexanderjoossens

2023-06-17T12:16:40Z

105

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-05-22T09:09:45Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: w2v2-libri-10min results: [] ---  # w2v2-libri-10min This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 1.18.3 - Tokenizers 0.13.3

SikongSphere/sikong-llama-7b-chinese

SikongSphere

2023-06-17T12:01:59Z

7

2

transformers

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

text-generation

2023-06-17T09:09:19Z

--- tags: - generated_from_trainer datasets: - customized model-index: - name: finetune results: [] ---  # finetune This model is a fine-tuned version of [/root/autodl-tmp/sikong/repo/LMFlow/output_models/Linly-Chinese-LLaMA-7b-hf](https://huggingface.co//root/autodl-tmp/sikong/repo/LMFlow/output_models/Linly-Chinese-LLaMA-7b-hf) on the customized 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: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50.0 ### Training results ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3

jalFaizy/ppo-lunar

jalFaizy

2023-06-17T11:42:59Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T11:42:28Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: trial1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.71 +/- 14.10 name: mean_reward verified: false --- # **trial1** Agent playing **LunarLander-v2** This is a trained model of a **trial1** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

bumstern/segmentation_model_russian_data

bumstern

2023-06-17T11:23:53Z

0

pyannote-audio

[ "pyannote-audio", "code", "ru", "license:mit", "region:us" ]

null

2023-06-17T11:02:51Z

--- license: mit language: - ru library_name: pyannote-audio tags: - code --- # Segmentation model This model was trained on AMI-MixHeadset and my own synthetic dataset of Russian speech. Training time: 5 hours on GTX 3060 This model can be used for diarization model from [pyannote/speaker-diarization](https://huggingface.co/pyannote/speaker-diarization) | Benchmark | DER% | | --------- |------| | [AMI (*headset mix,*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*)](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 38.8 | ## Usage example ```python import yaml from yaml.loader import SafeLoader import torch from pyannote.audio import Model from pyannote.audio.pipelines import SpeakerDiarization segm_model = torch.load('model/segm_model.pth', map_location=torch.device('cpu')) embed_model = Model.from_pretrained("pyannote/embedding", use_auth_token='ACCESS_TOKEN_GOES_HERE') diar_pipeline = SpeakerDiarization( segmentation=segm_model, segmentation_batch_size=16, clustering="AgglomerativeClustering", embedding=embed_model ) with open('model/config.yaml', 'r') as f: diar_config = yaml.load(f, Loader=SafeLoader) diar_pipeline.instantiate(diar_config) annotation = diar_pipeline('audio.wav') ```

Enterprize1/q-taxi-v3

Enterprize1

2023-06-17T11:15:07Z

0

null

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

reinforcement-learning

2023-06-17T11:14:52Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.74 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="Enterprize1/q-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

kejolong/hdxduniform2.0

kejolong

2023-06-17T11:07:19Z

0

null

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

null

2023-06-17T11:05:58Z

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

antphb/DS-Chatbox-mbart-large-50

antphb

2023-06-17T11:03:57Z

117

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T07:09:44Z

--- license: mit tags: - generated_from_trainer model-index: - name: DS-Chatbox-mbart-large-50 results: [] ---  # DS-Chatbox-mbart-large-50 This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0014 ## 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: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.977 | 0.19 | 100 | 0.0001 | | 0.023 | 0.38 | 200 | 0.0002 | | 0.0005 | 0.57 | 300 | 0.0005 | | 0.0007 | 0.76 | 400 | 0.0006 | | 0.0012 | 0.95 | 500 | 0.0014 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

nomad-ai/ppo-LunarLander-v2-1

nomad-ai

2023-06-17T11:01:03Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T11:00:26Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 281.34 +/- 18.86 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

mattladewig/distilbert-base-uncased-finetuned-ner

mattladewig

2023-06-17T10:34:27Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-17T08:37:53Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mattladewig/distilbert-base-uncased-finetuned-ner results: [] ---  # mattladewig/distilbert-base-uncased-finetuned-ner 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: 0.0342 - Validation Loss: 0.0614 - Train Precision: 0.9248 - Train Recall: 0.9365 - Train F1: 0.9306 - Train Accuracy: 0.9833 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.1951 | 0.0694 | 0.9087 | 0.9181 | 0.9134 | 0.9799 | 0 | | 0.0530 | 0.0621 | 0.9246 | 0.9301 | 0.9273 | 0.9823 | 1 | | 0.0342 | 0.0614 | 0.9248 | 0.9365 | 0.9306 | 0.9833 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3

kevinng77/unsup_bert_L3

kevinng77

2023-06-17T10:00:06Z

107

0

transformers

[ "transformers", "pytorch", "onnx", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-17T08:47:52Z

--- license: apache-2.0 language: - en metrics: - accuracy - f1 pipeline_tag: text-classification --- ```python # transformers==4.29.1 from transformers import AutoTokenizer, pipeline from optimum.onnxruntime import ORTModelForSequenceClassification onnx_model_path = "kevinng77/unsup_bert_L3" tokenizer = AutoTokenizer.from_pretrained(onnx_model_path) onnx_model = ORTModelForSequenceClassification.from_pretrained(onnx_model_path) onnx_pipe = pipeline(task="text-classification", model=onnx_model, tokenizer=tokenizer) onnx_pipe("How many rows are there in the table?") ```

parkyunmin/beatles_lyrics

parkyunmin

2023-06-17T09:38:03Z

198

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T09:11:41Z

--- tags: - generated_from_trainer model-index: - name: beatles_lyrics results: [] ---  # beatles_lyrics This model is a fine-tuned version of [wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics](https://huggingface.co/wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0584 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 50 | 1.1221 | | No log | 2.0 | 100 | 1.0710 | | No log | 3.0 | 150 | 1.0584 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

ganghe74/distilbert-base-uncased-finetuned-emotion

ganghe74

2023-06-17T09:34:40Z

105

0

transformers

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

text-classification

2023-06-17T09:13:51Z

--- license: apache-2.0 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: 0.9225 - name: F1 type: f1 value: 0.922469380812715 ---  # 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: 0.2170 - Accuracy: 0.9225 - F1: 0.9225 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8057 | 1.0 | 250 | 0.3170 | 0.905 | 0.9023 | | 0.242 | 2.0 | 500 | 0.2170 | 0.9225 | 0.9225 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.0 - Tokenizers 0.13.3

benol/Roma_Pyatifan

benol

2023-06-17T09:10:31Z

0

null

[ "ru", "en", "arxiv:1910.09700", "license:unknown", "region:us" ]

null

2023-06-17T08:58:04Z

--- license: unknown language: - ru - en --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### 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. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [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 [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

parkyunmin/my_awesome_eli5_clm-model

parkyunmin

2023-06-17T09:09:15Z

211

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T05:54:26Z

--- license: mit tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] ---  # my_awesome_eli5_clm-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5380 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 49 | 1.6679 | | No log | 2.0 | 98 | 1.5629 | | No log | 3.0 | 147 | 1.5380 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

PabloQuant29/ppo-LunarLander-v2

PabloQuant29

2023-06-17T08:36:13Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T08:35:40Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 244.46 +/- 18.98 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

mustika/alan2

mustika

2023-06-17T08:36:10Z

0

null

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

null

2023-06-17T08:34:12Z

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

AriesChen/GeoLLM

AriesChen

2023-06-17T08:32:06Z

195

3

transformers

[ "transformers", "pytorch", "chatglm", "feature-extraction", "custom_code", "region:us" ]

feature-extraction

2023-06-17T08:30:04Z

# GeoLLM **Large Language Model for Geology** Large language models are used to organize geology-related knowledge (geology, geophysics, geophysical logging, etc.). This version uses the [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) base model and fine-tunes it using P-tuning. --- ### Sedimentology Sedimentology, the study of sedimentary rocks and the processes by which they are formed, includes and is related to a large number of phenomena. Sedimentology includes the five fundamental processes defined by the term sediaentation --weathering, erosion, transportation, deposition and diagenesis. **Datasets：**《沉积岩石学（第四版）》朱筱敏 **Model：** ChatGLM-6B **Fine-tuning：** P-Tuning v2 **Before fine-tuning** ``` response, history = model.chat(tokenizer, "什么是沉积岩石学？", history=[]) response 沉积岩石学是一门研究沉积岩的形成、结构和成分的学科，主要关注地球表面上不同条件下的沉积过程和岩石形成机制，包括岩浆沉积、冰川沉积、洪水沉积、海洋沉积等。沉积岩石学在地质学、地球物理学、地球化学、材料科学等领域都有广泛应用，因为沉积岩是许多自然和人工地质工程的基础，如地质勘探、矿产资源开发、土木工程、环境科学等。沉积岩石学的研究对象包括沉积岩的地质特征、成分和构造特征，以及沉积岩与地壳、岩浆和变质岩的关系。研究方法包括沉积岩分析、岩相学分析、岩浆动力学分析等。 ``` **After fine-tuning** ``` response, history = model.chat(tokenizer, "什么是沉积岩石学？", history=[]) response 沉积岩石学是研究沉积岩的物质成分、结构构造、岩石类型、沉积物沉积作用和沉积物质形成环境以及沉积岩分布规律的一门科学。 ``` **Error Analysis:** We meticulously refined the model by approximately 500 entries from academic textbooks, subsequently applying P-Tuning v2 for optimization. Detailed control of parameters was not conducted for the time being. Given the scarcity of the training data and the fine-tuning parameters, the outcomes might exhibit some irregularities. **Results Analysis:** It is evident that the fine-tuned model shows enhanced reliability(more precise and concise) when providing answers within specialized knowledge domains. Moving forward, we will persist in enriching our training data and optimizing our fine-tuning methodologies in order to yield superior results. --- ### TODO 1. Geophysical Exploration 2. Geophysical logging 3. Petroleum Geology etc... --- ### Related Resources 1. [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B): ChatGLM-6B is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters.

SM16/TreeClassifier

SM16

2023-06-17T08:15:11Z

218

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-06-17T07:27:25Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: TreeClassifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # TreeClassifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Pepper Tree ![Pepper Tree](images/Pepper_Tree.jpg) #### Weeping Willow ![Weeping Willow](images/Weeping_Willow.jpg)

musabg/mt5-xl-tr-summarization

musabg

2023-06-17T07:25:20Z

5

1

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "tr", "dataset:musabg/wikipedia-tr-summarization", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-08T16:24:59Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - musabg/wikipedia-tr-summarization metrics: - rouge model-index: - name: mt5-xl-tr-summarization results: - task: name: Summarization type: summarization dataset: name: musabg/wikipedia-tr-summarization type: musabg/wikipedia-tr-summarization split: validation metrics: - name: Rouge1 type: rouge value: 56.4468 language: - tr --- # mT5-Xl Turkish Summarization This model is a fine-tuned version of [google/mt5-xl](https://huggingface.co/google/mt5-xl) on the musabg/wikipedia-tr-summarization dataset. This can be used with HF summarization pipeline. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Eval results It achieves the following results on the evaluation set: - Loss: 0.5676 - Rouge1: 56.4468 - Rouge2: 41.3258 - Rougel: 48.1909 - Rougelsum: 48.4284 - Gen Len: 75.9265 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 1.13.1 - Datasets 2.12.0 - Tokenizers 0.13.3

alsonlai/test

alsonlai

2023-06-17T07:23:18Z

0

null

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

reinforcement-learning

2023-06-17T07:22:43Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: test results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="alsonlai/test", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Irgendsoeine/FaceTheVote3

Irgendsoeine

2023-06-17T07:10:58Z

4

0

tf-keras

[ "tf-keras", "mobilenet", "image-classification", "region:us" ]

image-classification

2023-06-17T06:56:45Z

--- pipeline_tag: image-classification ---

kjiwon1222/my_awesome_eli5_clm-model

kjiwon1222

2023-06-17T06:54:34Z

217

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T06:32:49Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] ---  # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7506 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8621 | 1.0 | 1137 | 3.7690 | | 3.7782 | 2.0 | 2274 | 3.7533 | | 3.7245 | 3.0 | 3411 | 3.7506 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

aga3134/poca-SoccerTwos

aga3134

2023-06-17T06:48:55Z

0

ml-agents

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

reinforcement-learning

2023-06-17T06:48:14Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: aga3134/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

octipuw/policy_gradient-cartpole-v1

octipuw

2023-06-17T06:40:28Z

0

null

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

reinforcement-learning

2023-06-17T05:35:35Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: policy_gradient-cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 166.70 +/- 18.03 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

vedu/bart-large-perturbed

vedu

2023-06-17T06:21:37Z

103

0

transformers

[ "transformers", "pytorch", "jax", "rust", "bart", "feature-extraction", "en", "arxiv:1910.13461", "license:apache-2.0", "endpoints_compatible", "region:us" ]

feature-extraction

2023-06-16T20:22:31Z

--- license: apache-2.0 language: en --- # BART (large-sized model) ## Model description BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). Weights shared here are effectively from facebook/bart-large but with added noise for BOS embedding to assist the finetuning. ## Intended uses & limitations There have been quite a few issues related to finetuning BART for text generation, and this repo implements solution discussed in [#15559](https://github.com/huggingface/transformers/issues/15559). Particularly adding some noise to pre-trained model's BOS embedding. This seems to solve the problem of endless BOS generation for a finetuned BART model. You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import BartTokenizer, BartModel tokenizer = BartTokenizer.from_pretrained('vedu/bart-large-perturbed') model = BartModel.from_pretrained('vedu/bart-large-perturbed') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

pigeon01/sungju-finetuned-zh-to-ko1

pigeon01

2023-06-17T05:47:05Z

228

0

transformers

[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "translation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-06-17T05:12:16Z

--- license: mit tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: sungju-finetuned-zh-to-ko1 results: [] ---  # sungju-finetuned-zh-to-ko1 This model is a fine-tuned version of [alirezamsh/small100](https://huggingface.co/alirezamsh/small100) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0467 - Bleu: 10.2096 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

sunflowermarshmallows/dqn-SpaceInvadersNoFrameskip-v4

sunflowermarshmallows

2023-06-17T05:25:16Z

1

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T05:24:36Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 629.00 +/- 184.89 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo 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 dqn --env SpaceInvadersNoFrameskip-v4 -orga sunflowermarshmallows -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sunflowermarshmallows -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sunflowermarshmallows ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

nolanaatama/rngrndrvcv800pchsrthysttylrsvrsn

nolanaatama

2023-06-17T04:33:50Z

0

null

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

null

2023-05-31T04:13:59Z

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

ALPHONSE28/EQUIPO06SEMANA09

ALPHONSE28

2023-06-17T04:33:00Z

105

0

transformers

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

text-classification

2023-06-16T06:38:04Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: EQUIPO06SEMANA09 results: [] ---  # EQUIPO06SEMANA09 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2161 - Accuracy: 0.9233 - F1: 0.9514 ## 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 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

fgeyer/reinforce-CartPole-v1

fgeyer

2023-06-17T04:04:51Z

0

null

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

reinforcement-learning

2023-06-17T03:59:14Z

--- 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: 1000.00 +/- 0.00 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

aozora-esther/aozora-esther

aozora-esther

2023-06-17T04:00:52Z

0

null

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

null

2023-06-17T04:00:52Z

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

2022happy/swin-tiny-patch4-window7-224-finetuned-eurosat

2022happy

2023-06-17T03:51:48Z

245

0

transformers

[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:cifar10", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-06-15T13:46:26Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cifar10 metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: cifar10 type: cifar10 config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.97 ---  # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0893 - Accuracy: 0.97 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5255 | 1.0 | 351 | 0.1262 | 0.9596 | | 0.3808 | 2.0 | 703 | 0.1031 | 0.9652 | | 0.3268 | 2.99 | 1053 | 0.0893 | 0.97 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

nolanaatama/dmnslyrkmtsnybnmstyllr

nolanaatama

2023-06-17T02:31:22Z

0

null

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

null

2023-06-17T02:27:58Z

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

Atnafu/amhric_xlmr-small

Atnafu

2023-06-17T02:23:50Z

105

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-17T02:17:40Z

--- license: afl-3.0 tags: - generated_from_trainer model-index: - name: amh_small results: [] ---  # amh_small This model is a fine-tuned version of [Davlan/afro-xlmr-small](https://huggingface.co/Davlan/afro-xlmr-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2386 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

breadlicker45/MuseRift

breadlicker45

2023-06-17T02:11:29Z

170

0

transformers

[ "transformers", "pytorch", "rwkv", "text-generation", "dataset:breadlicker45/musenet-encoders-40k", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-06-15T01:22:00Z

--- datasets: - breadlicker45/musenet-encoders-40k ---

paulahugging/MABEPA_2

paulahugging

2023-06-17T01:48:24Z

103

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2023-06-16T15:43:20Z

El objetivo Modelo parte de un modelo Bert Base con el objetivo de identificar la similitud semántica entre dos oraciones (o Semantic Textual Similarity: “STS”), es decir, medir qué tan parecidos son dos documentos. Dicho modelo será a través de una red neuronal siamesa, que implica usar la misma red, con idénticos parámetros, para procesar la premisa y la hipótesis. “La tarea STS está motivada por la observación de que modelar con precisión la similitud de significado de las oraciones es un problema fundamental de comprensión del lenguaje relevante para numerosas aplicaciones, incluyendo: traducción automática (MT), resumen, generación, pregunta respuesta (QA), calificación de respuestas cortas, semántica, sistemas de búsqueda, diálogo y conversación.” (Cera Et al, 2017, p. 1). Datos El dataset elegido fue XNLI en español. El mismo contiene los campos de 'premise', 'hypothesis' y 'label’, donde los dos primeros campos son oraciones o cadenas de texto mientras que el tercero es la similitud semántica entre ambas con la siguiente codificación: 'entailment': 0, 'neutral': 1, 'contradiction': 2 El mismo está compuesto por tres dataset: TRAINING, con 392.702 datos; TEST, con 5.010 datos; VALIDATION, con 2.490 datos. Además se utiliza un vocabulario en español que contiene alrededor de 31.000 palabras, incluyendo los siguientes caracteres especiales: "[MASK]", "[PAD]", "[EOS]","[UNK]","[CLS]","[SEP]" que se encuentran en las primeras posiciones del vocabulario. Dicho vocabulario surge del modelo de Huggigface cuyo model_name es "dccuchile/bert-base-spanish-wwm-uncased". Método Tokenización En primer lugar importamos AutoTokenizer y obtenemos el tokenizador del modelo definido anteriormente. El mismo, al tokenizar adicionalmente de convertir los tokens o palabras en su ID del vocabulario, le incorpora al inicio el id del carácter especial “CLS” y al final el “SEP”. Además fijamos como parámetro la longitud máxima del modelo (tokenizer.model_max_length) esto genera que corte las premisas y las hipotesis si son mas largas y que las complete con padding si son mas cortas hasta completar la longitud deseada (con “PAD”).. Notamos que este tokenizador contiene funciones como las del itos y el stoi ya generadas. Procedemos a tokenizar el dataset a utilizando la función map, tanto para la premisa como la hipótesis. Armado de Batches Con la tokenizacion realizada procedemos a separar los Batches, para lo cual usamos el dataloader de torch. El resultado serán batches de tamaño 32 para el dataset de train, y 16 tanto para el de validación como para el de test. Sus dimensiones son el tamaño de cada batch x la cantidad de elementos. En el caso de la premisa y la hipótesis la cantidad de elementos será el largo utilizado para la tokenización mientras que en el caso del label, al ser único, la dimension será del tamaño del batch x 1. Asimismo incorporamos a los batches el attention_mask de la premisa y de la hipótesis. Modelo base BERT es una red pre-entrenada de transformadores (...). El input de BERT consiste en las dos oraciones separadas por un token especial [SEP]. (...) yla salida se pasa a una función de regresión simple para derivar la etiqueta final. (Reimers and Gurevich, 2019, p. 2). Sobre este modelo base, se realizó el finetuning de nuestra red, basándonos en el siguiente diagrama (Reimers and Gurevich, 2019, p. 3).: Es decir que lo que haremos será pasar la premisa y la hipótesis por una BERT, obteniendo luego un pooler output para cada una de ellas (“u” y “v”). Luego se concatenan, junto con el módulo de la diferencia, y ese resultado es pasado por la capa lineal obteniendo 3 resultados, que serán las probabilidades asociadas a cada label. Se procedió a entrenar la red con el dataset de train, utilizando como función de pérdida la entropía cruzada, y luego se procedió a validar el modelo. Los resultados se exponen en la próxima sección. En este caso el modelo tiene dos epocas completas de entrenamiento

zhangjian94cn/Taxi-v3

zhangjian94cn

2023-06-17T01:33:35Z

0

null

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

reinforcement-learning

2023-06-17T01:33:26Z

--- 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="zhangjian94cn/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"]) ```

UofA-LINGO/text-to-triplets-explanation-v2

UofA-LINGO

2023-06-17T00:41:02Z

0

null

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

null

2023-06-17T00:38:48Z

--- license: mit --- LoRA weights for `LLaMA-7B` Trained on 'taesiri/webnlg-triplets-explanation-v1' for 4 epochs. Command: ``` WORLD_SIZE=2 CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=1234 finetune.py --base_model='decapoda-research/llama-7b-hf' --data_path 'taesiri/webnlg-triplets-explanation-v1' --num_epochs=4 --cutoff_len=512 --group_by_length --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' --lora_r=8 --micro_batch_size=8 --batch_size=32 ```

UofA-LINGO/text-to-triplets-explanation-v1

UofA-LINGO

2023-06-17T00:39:59Z

0

null

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

null

2023-06-16T22:26:38Z

--- license: mit --- LoRA weights for `LLaMA-7B` Trained on 'taesiri/webnlg-triplets-explanation-v1' for 2 epochs.

arsalsyed/distilgpt2-finetuned-wikitext2

arsalsyed

2023-06-17T00:14:20Z

135

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-16T23:41:59Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] ---  # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6420 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7596 | 1.0 | 2334 | 3.6651 | | 3.6543 | 2.0 | 4668 | 3.6468 | | 3.6024 | 3.0 | 7002 | 3.6420 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

H4nan/dqn-SpaceInvadersNoFrameskip-v4

H4nan

2023-06-16T23:54:53Z

1

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-05-23T18:30:15Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 537.00 +/- 181.32 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo 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 dqn --env SpaceInvadersNoFrameskip-v4 -orga H4nan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga H4nan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga H4nan ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

Ioanaaaaaaa/bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay

Ioanaaaaaaa

2023-06-16T23:47:54Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-16T23:30:14Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay 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: 0.941 - name: F1 type: f1 value: 0.9411169346964399 ---  # bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2591 - Accuracy: 0.941 - F1: 0.9411 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0799 | 1.0 | 250 | 0.1898 | 0.9375 | 0.9377 | | 0.0516 | 2.0 | 500 | 0.2290 | 0.938 | 0.9383 | | 0.0386 | 3.0 | 750 | 0.2107 | 0.9415 | 0.9419 | | 0.0195 | 4.0 | 1000 | 0.2607 | 0.9435 | 0.9433 | | 0.0149 | 5.0 | 1250 | 0.2591 | 0.941 | 0.9411 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

sam34738/mBERT

sam34738

2023-06-16T23:44:39Z

186

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-16T20:24:12Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: mbert results: [] ---  # mbert This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9812 - Accuracy: 0.6583 - F1: 0.6948 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-05 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.749 | 1.0 | 2100 | 0.7068 | 0.4994 | 0.0131 | | 0.7707 | 2.0 | 4200 | 0.9812 | 0.6583 | 0.6948 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

TacLucas/Test

TacLucas

2023-06-16T23:07:19Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2023-06-16T23:06:14Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### 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. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [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 [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ghze/Taxi_v3

ghze

2023-06-16T23:00:53Z

0

null

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

reinforcement-learning

2023-06-16T23:00:48Z

--- 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 +/- 2.71 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="ghze/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"]) ```

ghze/Taxi

ghze

2023-06-16T22:59:16Z

0

null

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

reinforcement-learning

2023-06-16T22:59:09Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="ghze/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

amjadfqs/finalProject

amjadfqs

2023-06-16T22:28:48Z

15

0

transformers

[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-06-15T17:30:57Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision model-index: - name: finalProject 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: 0.9890023566378633 - name: Precision type: precision value: 0.9894345375382527 ---  # finalProject This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0411 - Accuracy: 0.9890 - F1 Score: 0.9892 - Precision: 0.9894 - Sensitivity: 0.9891 - Specificity: 0.9972 ## 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: 0.0001 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:| | 0.3384 | 1.0 | 30 | 0.2387 | 0.9144 | 0.9163 | 0.9197 | 0.9146 | 0.9781 | | 0.1608 | 2.0 | 60 | 0.1635 | 0.9466 | 0.9476 | 0.9485 | 0.9474 | 0.9865 | | 0.0953 | 3.0 | 90 | 0.0915 | 0.9698 | 0.9703 | 0.9706 | 0.9706 | 0.9924 | | 0.0573 | 4.0 | 120 | 0.1125 | 0.9607 | 0.9617 | 0.9634 | 0.9621 | 0.9901 | | 0.0335 | 5.0 | 150 | 0.0536 | 0.9827 | 0.9831 | 0.9837 | 0.9826 | 0.9957 | | 0.0185 | 6.0 | 180 | 0.0543 | 0.9827 | 0.9830 | 0.9837 | 0.9825 | 0.9957 | | 0.0226 | 7.0 | 210 | 0.0478 | 0.9859 | 0.9861 | 0.9866 | 0.9856 | 0.9965 | | 0.0131 | 8.0 | 240 | 0.0468 | 0.9843 | 0.9846 | 0.9847 | 0.9846 | 0.9961 | | 0.0087 | 9.0 | 270 | 0.0411 | 0.9890 | 0.9892 | 0.9894 | 0.9891 | 0.9972 | | 0.0043 | 10.0 | 300 | 0.0376 | 0.9886 | 0.9888 | 0.9890 | 0.9887 | 0.9971 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3

devonho/my_awesome_opus_books_model

devonho

2023-06-16T22:28:30Z

8

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus100", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-06T07:28:09Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: my_awesome_opus_books_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 config: en-ja split: test args: en-ja metrics: - name: Bleu type: bleu value: 23.8215 ---  # my_awesome_opus_books_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.4506 - Bleu: 23.8215 - Gen Len: 4.6055 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-------:|:---------------:|:-------:|:-------:| | 0.4468 | 1.0 | 500000 | 0.4585 | 23.9023 | 4.705 | | 0.4397 | 2.0 | 1000000 | 0.4506 | 23.8215 | 4.6055 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

maren-hugg/xlm-roberta-base-finetuned-panx-en-custom

maren-hugg

2023-06-16T21:56:26Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-12T06:49:39Z

--- license: mit tags: - generated_from_trainer metrics: - f1 - precision - recall - accuracy model-index: - name: xlm-roberta-base-finetuned-panx-en-custom results: [] ---  # xlm-roberta-base-finetuned-panx-en-custom This model is a fine-tuned version of [maren-hugg/xlm-roberta-base-finetuned-panx-en](https://huggingface.co/maren-hugg/xlm-roberta-base-finetuned-panx-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1045 - F1: 0.8782 - Precision: 0.8496 - Recall: 0.9088 - Accuracy: 0.9754 ## 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: 4.886597454037411e-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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| | 0.128 | 0.75 | 24 | 0.1087 | 0.8514 | 0.8299 | 0.8740 | 0.9713 | | 0.074 | 1.5 | 48 | 0.1006 | 0.8637 | 0.8505 | 0.8773 | 0.9750 | | 0.0506 | 2.25 | 72 | 0.0987 | 0.8728 | 0.8587 | 0.8872 | 0.9749 | | 0.0393 | 3.0 | 96 | 0.1045 | 0.8782 | 0.8496 | 0.9088 | 0.9754 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

Enterprize1/ppo-LunarLander-v2

Enterprize1

2023-06-16T21:45:24Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-16T21:45:00Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 242.78 +/- 66.66 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

stanford-crfm/music-small-ar-inter-100k

stanford-crfm

2023-06-16T21:28:37Z

182

1

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-05T00:04:27Z

--- license: apache-2.0 --- This is a Small (112M parameter) Transformer trained for 100k steps on interarrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

stanford-crfm/music-small-ar-800k

stanford-crfm

2023-06-16T21:28:12Z

183

1

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-05T00:01:12Z

--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

FALLENSTAR/Volvo850LoRa

FALLENSTAR

2023-06-16T21:28:07Z

0

null

[ "region:us" ]

null

2023-06-11T17:03:34Z

![00008-2488555434.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/ttKEY5ht3tpprSQP1bh6x.png) ![00010-1641710529.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/9DeVYLdy4z_5-GheRX7mp.png) ![00012-3333952880.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/_CXajXOTGrrAWV-hwQSvX.png) ![00014-2737276302.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/WdKhjgwrb7IKVIZH70geW.png) ![00018-4039153412.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/fgMyYsKjaRvQzN30z7LwU.png) ![00024-759743265.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/iaJIqLM6wfV_7tERVKtnf.png) ![00029-2851129221.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/cnHpl9xmcr7tChZIEMxpq.png) ![00030-857339483.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/eaJLLbx10bqGTvVM5n8im.png) ![00038-414082621.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/qWRmZchGH9LpFpcuK0-7E.png) ![00040-3062714132.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/aMnkr_RvEYs-_obSmQlF5.png)

stanford-crfm/music-small-ar-100k

stanford-crfm

2023-06-16T21:27:39Z

184

0

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-04T23:58:03Z

--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 100k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

stanford-crfm/music-small-100k

stanford-crfm

2023-06-16T21:26:29Z

181

0

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-04T23:26:52Z

--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 100k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

stanford-crfm/music-medium-800k

stanford-crfm

2023-06-16T21:25:52Z

572

4

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-05T00:17:20Z

--- license: apache-2.0 --- This is a Medium (360M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

jondurbin/airoboros-65b-gpt4-1.2-peft

jondurbin

2023-06-16T21:01:26Z

0

null

[ "dataset:jondurbin/airoboros-gpt4-1.2", "license:other", "region:us" ]

null

2023-06-14T09:11:36Z

--- license: other datasets: - jondurbin/airoboros-gpt4-1.2 --- peft weights of https://hugginface.co/jondurbin/airoboros-65b-gpt4-1.2, see that card for details

Schnitzl/detr-resnet-50_finetuned_cppe5

Schnitzl

2023-06-16T20:54:42Z

191

0

transformers

[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-06-16T17:17:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: detr-resnet-50_finetuned_cppe5 results: [] ---  # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.13.0 - Tokenizers 0.13.3

crlandsc/bsrnn-bass

crlandsc

2023-06-16T20:24:33Z

0

1

null

[ "audio source separation", "music demixing", "band-split recurrent neural network", "bsrnn", "spectrogram", "bass", "region:us" ]

null

2023-06-16T20:16:53Z

--- tags: - audio source separation - music demixing - band-split recurrent neural network - bsrnn - spectrogram - bass --- # Model Card for bsrnn-bass Bass model for [Music-Demixing-with-Band-Split-RNN](https://github.com/crlandsc/Music-Demixing-with-Band-Split-RNN).

sngsfydy/resnet-50-finetuned-eurosat

sngsfydy

2023-06-16T20:17:05Z

209

0

transformers

[ "transformers", "pytorch", "tensorboard", "resnet", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-06-16T19:14:44Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: resnet-50-finetuned-eurosat results: [] ---  # resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0706 - Accuracy: 0.5152 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6069 | 0.99 | 20 | 1.5839 | 0.3879 | | 1.5395 | 1.98 | 40 | 1.4860 | 0.5485 | | 1.4321 | 2.96 | 60 | 1.3500 | 0.5364 | | 1.3292 | 4.0 | 81 | 1.1826 | 0.5212 | | 1.233 | 4.99 | 101 | 1.0706 | 0.5152 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

blevlabs/alpaca-7b

blevlabs

2023-06-16T20:16:19Z

6

0

transformers

[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]

null

2023-06-15T16:01:29Z

Found. Redirecting to https://cdn-lfs.hf.co/repos/d1/e7/d1e74b32fb5b1cbe2ba63f424b0731e786a5ae300beb41ac103060925f9becff/d17913b0f97b6c92903cbf2c6d8ff9e412644769ba8ea848fd4dc4246a38c8d0?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1739044824&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczOTA0NDgyNH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy9kMS9lNy9kMWU3NGIzMmZiNWIxY2JlMmJhNjNmNDI0YjA3MzFlNzg2YTVhZTMwMGJlYjQxYWMxMDMwNjA5MjVmOWJlY2ZmL2QxNzkxM2IwZjk3YjZjOTI5MDNjYmYyYzZkOGZmOWU0MTI2NDQ3NjliYThlYTg0OGZkNGRjNDI0NmEzOGM4ZDA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=sUzC3YAwA35JO1%7EsqsOl3QIpzizB%7EEv%7EXYdYit3C5o8V8Suda2NcVf2R2yyjugIu%7EIl4lNae7CS-CFdgqk7b%7Eo86c3g3cLEPoPSpj-7li1vD14C0df9YB6OzS26xfSPL1mxTVlK3NdDAeGXwlIcfEJHK2xJeMYL9Yw5J0IZgdyffOdyAw2GMaQdoQ2puzRSjGJyykxQ7ESbDV135z-qhCWsn1XiKFlXKM2xrx7K1nZBcEmNKd9nwVWSWoh5XB69c8dOvT91vTel6l15B1aAJzTRYyQfJEv0vI0m7Vuf8kXSHNP1Unl8bNFulxPAIaOmw5QjTnT4yUbnuwJlCImU74A__&Key-Pair-Id=K3RPWS32NSSJCE

FALLENSTAR/CedricGloriaLoRa

FALLENSTAR

2023-06-16T20:10:23Z

0

null

[ "region:us" ]

null

2023-06-09T20:58:56Z

### Model Description First of all, it's LoRa. It is based on my favorite Nissan Cedric/Gloria Y31 Hardtop from the years '87-91. It is a test model, so it has defects. I don't remember how many samples and epochs were used in it... But, with some of the checkpoints it turns out very similar and funny. The best images I was able to get with this LoRa were at these settings: Steps: 25 Sampler: DPM++ SDE Karras, CFG scale: 6.5 and with LoRa strength 0.8-1 ### Results ![00056-2998859893.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/Hqc-19MXbw-tOHcytmCgO.png) ![00037-1095574331.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/6d8uCXt5U2KvnqtZdl1d2.png) ![00039-89618184.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/tmzZoZQT6fK4_UPCCA5sk.png) ![00041-562450270.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/tp0QNuodfbsEkgLh8Yhht.png) ![00066-2959432492.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/FO5BR5uRtvAwm7jMCzUTa.png) ![00058-1357569836.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/yC3gSTgtQnnBTg3ibDtXw.png) ![00065-3713693640.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/xRNkGnsBb8kiW15_v7nws.png) ![00013-3313807285.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/1r1CFhjES8GncHPFudrGZ.png) ![00067-1452084223.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/8a86GW52YDweESQKp5fRR.png) ![00004-873995140.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/ZCu2ffZkEGW_DlLWYu6QC.png) ![00084-419571825.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/4PXZdvNBX-kWwMyyZ1bRm.png) ![00021-3472542211.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/_2fwDF2KUlXq7bS-P65Hi.png) ![00048-2623366528.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/m-XuUdhEXhT7-Vaa_D0pa.png)

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

GEMCorp

2023-06-16T19:51:18Z

0

null

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

reinforcement-learning

2023-06-16T19:51:12Z

--- 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="GEMCorp/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"]) ```

ChristineCheng/my_awesome_eli5_clm-model

ChristineCheng

2023-06-16T19:49:19Z

61

0

transformers

[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-06-16T19:33:04Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ChristineCheng/my_awesome_eli5_clm-model results: [] ---  # ChristineCheng/my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7347 - Validation Loss: 3.7399 - 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.9119 | 3.7667 | 0 | | 3.7942 | 3.7493 | 1 | | 3.7347 | 3.7399 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3

SSSSSSSSSSSJJJJJJJJJJJJJ/my_awesome_eli5_clm-model

SSSSSSSSSSSJJJJJJJJJJJJJ

2023-06-16T19:44:19Z

179

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-16T19:13:40Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] ---  # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7341 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8765 | 1.0 | 1120 | 3.7555 | | 3.7769 | 2.0 | 2240 | 3.7368 | | 3.7331 | 3.0 | 3360 | 3.7341 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

crlandsc/tiny-audio-diffusion-snares

crlandsc

2023-06-16T19:25:10Z

3

1

null

[ "audio", "diffusion", "waveform diffusion", "audio diffusion", "unet", "region:us" ]

null

2023-06-10T15:20:00Z

--- tags: - audio - diffusion - waveform diffusion - audio diffusion - unet --- # Model Card for tiny-audio-diffusion-snares Snare drum model for tiny-audio-diffusion. Use with [tiny-audio-diffusion](https://github.com/crlandsc/tiny-audio-diffusion) repo to generate snare drum samples.

ananay/kneearch

ananay

2023-06-16T19:17:59Z

22

0

diffusers

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

text-to-image

2023-06-16T19:05:11Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### kneearch Dreambooth model trained by ananay with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

AustinCarthy/OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5

AustinCarthy

2023-06-16T19:17:42Z

0

null

[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]

null

2023-06-16T15:49:03Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5 results: [] ---  # OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_OnlyPhishGPT2_using_benigh_200K_top_p_0.75 dataset. It achieves the following results on the evaluation set: - Loss: 0.0192 - Accuracy: 0.9978 - F1: 0.9767 - Precision: 0.9994 - Recall: 0.955 - Roc Auc Score: 0.9775 - Tpr At Fpr 0.01: 0.9632 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0057 | 1.0 | 35625 | 0.0113 | 0.9979 | 0.9779 | 0.9954 | 0.961 | 0.9804 | 0.9518 | | 0.0035 | 2.0 | 71250 | 0.0150 | 0.9975 | 0.9726 | 0.9983 | 0.9482 | 0.9741 | 0.95 | | 0.0011 | 3.0 | 106875 | 0.0175 | 0.9975 | 0.9727 | 0.9994 | 0.9474 | 0.9737 | 0.9554 | | 0.0009 | 4.0 | 142500 | 0.0160 | 0.9979 | 0.9778 | 0.9990 | 0.9576 | 0.9788 | 0.9618 | | 0.0 | 5.0 | 178125 | 0.0192 | 0.9978 | 0.9767 | 0.9994 | 0.955 | 0.9775 | 0.9632 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

YoneShiro/SpaceInvadersNoFrameskip-v4

YoneShiro

2023-06-16T19:14:05Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-16T19:13:20Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 708.00 +/- 250.51 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo 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 dqn --env SpaceInvadersNoFrameskip-v4 -orga YoneShiro -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga YoneShiro -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga YoneShiro ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

Boristss/modellunarlander

Boristss

2023-06-16T19:13:23Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-16T19:12:58Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.44 +/- 21.50 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

JvThunder/ppo-Pyramids

JvThunder

2023-06-16T18:37:51Z

1

0

ml-agents

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

reinforcement-learning

2023-06-16T18:37:41Z

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

sambanovasystems/starcoder-toolbench

sambanovasystems

2023-06-16T18:23:22Z

23

4

transformers

[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "arxiv:2305.16504", "arxiv:2305.06161", "license:bigcode-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-24T04:41:14Z

--- license: bigcode-openrail-m --- # starcoder-toolbench  starcoder-toolbench is a 15 billion parameter model used for api based action generation. It is instruction tuned from [starcoder](https://huggingface.co/bigcode/starcoder) on api based action generation datasets. ## Model Details ### Model Description  - **Developed by:** [SambaNova Systems](https://sambanova.ai/) - **Model type:** Language Model - **Language(s):** English - **License:** bigcode-openrail-m - **Finetuned from model:** [starcoder](https://huggingface.co/bigcode/starcoder) ### Basic Information  - **Paper**: [link](https://arxiv.org/abs/2305.16504) - **Github**: [link](https://github.com/sambanova/toolbench) ## Uses <details> <summary>Click to expand</summary>  ### Direct Use  This model is intended for commercial and research use. ### Out-of-Scope Use  starcoder-toolbench should NOT be used for purpose other than API based action generation. </details> --- ## How to Get Started with the Model <details> <summary>Click to expand</summary> ### Loading in model with Huggingface ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/starcoder-toolbench") model = AutoModelForCausalLM.from_pretrained("sambanovasystems/starcoder-toolbench", device_map="auto", torch_dtype="auto") ``` ### Example Prompts To Try in GPU Tutorial Prompt 1: ``` I have the following set of API:\n\n# To set the maximum commute time in minute to your office location, assuming the office location is already defined\nAPI.set_max_commute_time(value: int)\n\n# To set the maximum home size in square feet\nAPI.set_max_square_feet(value: int)\n\n# To set the minimum home price in dollars\nAPI.set_min_price(value: int)\n\n# To set the number of garage(s)\nAPI.set_num_garages(value: int)\n\n# To set home types for search. For home buying, home_types choices are: \"House\", \"Townhouse\", \"Condo\", \"Land\", \"Multi-family\", \"Mobile\", \"Co-op\"; for home renting, home_types choices are: \"House\", \"Townhouse\", \"Condo\", \"Apartment\".\nAPI.select_home_type(home_types: List[str])\n\n# To set the number of balconies\nAPI.set_num_balconies(value: int)\n\n# Submit criterion to get search results. This function should be called after setting all the criterion.\nAPI.search()\n\n# To set the floor number\nAPI.set_floor_number(value: int)\n\n# To set the number of bedroom(s)\nAPI.set_num_beds(value: int)\n\n# To set the number of swimming pool(s)\nAPI.set_num_swimming_pools(value: int)\n\n# To set the maximum home price in dollars\nAPI.set_max_price(value: int)\n\n# To specify whether to search homes for buying or renting. 'value' can be chosen from ['buy', 'rent']. This function must be called after setting the location and before setting any other criteria.\nAPI.set_buy_or_rent(value: str)\n\n# To set the number of bathroom(s)\nAPI.set_num_baths(value: float)\n\n# To set the location for the search area. This function must be called before setting any criteria.\nAPI.set_location(value: string)\n\n# To set the minimum home size in square feet\nAPI.set_min_square_feet(value: int)\n\n-------------\n\nTask: Looking for homes to rent in Santa Clarita with a price range between $110000 and $1753000, a minimum of 1700 square feet, at least 2 balconies, and 3.5 bathrooms.\nAction:\n ``` Prompt 2: ``` I have the following set of API:\n\n# To set the location for hotel search, given a Loc object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_hotel_location(Loc)\n\n# To set the number of hotel rooms to book.\nAPI.set_num_rooms(value)\n\n# To set the location for departure, given a Loc object. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.set_origin(Loc)\n\n# To select the transportation type from ['flight', 'train', 'bus', 'cruise']. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.select_transportation(transportation_type)\n\n# To set the return date of the trip, given a Date object. If booking type is 'both' and this function is not called explicitly, 'return_date' will be set to 'hotel_checkout_date' implicitly.\nAPI.set_return_date(Date)\n\n# To set the hotel check-in date, given a Date object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_checkin_date(Date)\n\n# To define a date.\ndate = Date(month, day, year)\n\n# To set the departure date of the trip, given a Date object. This function must be called if booking type is 'trip tickets'. If booking type is 'both' and this function is not called explicitly, 'departure_date' will be set to 'hotel_checkin_date' implicitly.\nAPI.set_departure_date(Date)\n\n# To set the location for arrival, given a Loc object. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.set_destination(Loc)\n\n# To define a location of a given city 'City'.\nlocation = Loc('City')\n\n# To set maximum hotel room price.\nAPI.set_max_room_price(value)\n\n# To set minimum ticket price.\nAPI.set_min_ticket_price(value)\n\n# To select the booking type from ['hotels', 'trip tickets', 'both']. This function must be called before setting any criteria.\nAPI.select_booking_type(booking_type)\n\n# To set minimum hotel room price.\nAPI.set_min_room_price(value)\n\n# To set the number of child tickets to purchase.\nAPI.set_num_children(value)\n\n# To set the number of adult tickets to purchase.\nAPI.set_num_adults(value)\n\n# To select the hotel room type from ['King Bed', 'Queen Bed', 'Double', 'Luxury'].\nAPI.select_room_type(room_type)\n\n# To set maximum ticket price.\nAPI.set_max_ticket_price(value)\n\n# Submit criterion to get search results. This function should be called after setting all the criterion.\nAPI.search()\n\n# To set the hotel check-out date, given a Date object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_checkout_date(Date)\n\n-------------\n\nTask: Looking to book 2 adult and 4 child tickets from Stockton to Baltimore by cruise, on 2023-07-29.\nAction:\n ``` </details> --- ## Training Details <details> <summary>Click to expand</summary> ### Training Data  The training data is curated for the 8 tasks in ToolBench. See Appendix A of the [paper](https://arxiv.org/abs/2305.16504) for task details and Appendix C.1 for the training data curation details. In total, there are 9704 training samples, organized in all-shot format as described in Appendix C.2. Here is the [download link](https://drive.google.com/file/d/1lUatLGnSVhfy1uVIPEQ7qCoLtnCIXi2O/view?usp=sharing) to the training data. ### Training Procedure  We trained starcoder-toolbench on 4 80GB A100 gpu's. We started from [starcoder](https://huggingface.co/bigcode/starcoder) and finetuned it on the dataset mentioned above. ### Hyperparameters - Hardware: A100 GPU - Optimizer: AdamW - Grad accumulation: 1 - Epochs: 8 - Global Batch size: 16 - Batch tokens: 16 * 2048 = 32,768 tokens - Learning Rate: 1e-5 - Learning Rate Scheduler: Fixed LR - Weight decay: 0.1 </details> ## Acknowledgment We would like to express our gratitude to the great work done in [StarCoder: may the source be with you!](https://arxiv.org/abs/2305.06161) ## Cite starcoder-toolbench ``` @misc{xu2023tool, title={On the Tool Manipulation Capability of Open-source Large Language Models}, author={Qiantong Xu and Fenglu Hong and Bo Li and Changran Hu and Zhengyu Chen and Jian Zhang}, year={2023}, eprint={2305.16504}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```