librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-13 18:27:38	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-13 18:27:10	card stringlengths 11 1.01M
HusseinHE/icbinh	HusseinHE	2023-09-21T20:12:39Z	16	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-21T20:07:02Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### ICBINH Dreambooth model trained by HusseinHE 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:
fsuarez/autotrain-image-classification-86974143294	fsuarez	2023-09-21T19:47:43Z	194	0	transformers	[ "transformers", "pytorch", "safetensors", "swin", "image-classification", "autotrain", "vision", "dataset:fsuarez/autotrain-data-image-classification", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-04T14:31:09Z	--- tags: - autotrain - vision - image-classification datasets: - fsuarez/autotrain-data-image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 1.6524320573590656 --- ## 📒 image-classification-model This model has undergone training on the "image-classification" dataset, focusing on multi-class classification to categorize specific segments of websites. Each segment corresponds to one of six potential features, encompassing a broad spectrum of web elements, including: - Button: Identifying interactive buttons that users can click or tap on for various website functions. - Textfield: Recognizing text input fields where users can type or enter information. - Checkbox: Detecting checkboxes that users can select or deselect to make choices or indicate preferences. - Radiobutton: Identifying radio buttons that allow users to choose a single option from a list. - Tables: Recognizing tabular data structures that organize information in rows and columns. - AppBar: Detecting app bars or navigation bars typically found at the top of web pages, often containing menus, search bars, or branding elements. This extensive training equips the model with the ability to accurately classify these web elements. # 🧪 Dataset Content The dataset is structured to facilitate the analysis of website components. It includes various types of objects commonly found on websites, such as buttons, text fields, checkboxes, radio buttons, tables, and app bars. Each object type is organized into its respective category within the dataset, allowing for precise classification. \| Web Element Category \| Quantity of images \| \|----------------------\|--------------------\| \| Button \| 2934 \| \| Textfield \| 100 \| \| Checkbox \| 422 \| \| Radiobutton \| 466 \| \| Tables \| 100 \| \| AppBar \| 100 \| # 🤗 Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 86974143294 - CO2 Emissions (in grams): 1.6524 ## 📐 Validation Metrics - Loss: 0.079 - Accuracy: 0.983 - Macro F1: 0.967 - Micro F1: 0.983 - Weighted F1: 0.983 - Macro Precision: 0.971 - Micro Precision: 0.983 - Weighted Precision: 0.983 - Macro Recall: 0.964 - Micro Recall: 0.983 - Weighted Recall: 0.983
Bogdan63/distilbert-imdb	Bogdan63	2023-09-21T19:45:05Z	107	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-10T13:46:45Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 313 \| 0.2279 \| 0.9112 \| 0.9112 \| 0.9112 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Logeswaransr/T5_MineAI_Prototype	Logeswaransr	2023-09-21T19:43:09Z	103	1	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-21T19:37:52Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: results_T5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results_T5 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8755 - Rouge1: 0.2921 - Rouge2: 0.1519 - Rougel: 0.2857 - Rougelsum: 0.2866 ## 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: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| No log \| 1.0 \| 109 \| 0.4682 \| 0.2349 \| 0.0878 \| 0.2327 \| 0.2344 \| \| No log \| 2.0 \| 218 \| 0.4153 \| 0.2519 \| 0.0965 \| 0.2481 \| 0.2503 \| \| No log \| 3.0 \| 327 \| 0.4102 \| 0.3011 \| 0.1465 \| 0.2979 \| 0.2990 \| \| No log \| 4.0 \| 436 \| 0.4386 \| 0.2555 \| 0.1138 \| 0.2496 \| 0.2496 \| \| 0.8199 \| 5.0 \| 545 \| 0.4784 \| 0.2725 \| 0.1188 \| 0.2675 \| 0.2665 \| \| 0.8199 \| 6.0 \| 654 \| 0.5088 \| 0.2524 \| 0.1066 \| 0.2497 \| 0.2501 \| \| 0.8199 \| 7.0 \| 763 \| 0.5680 \| 0.2542 \| 0.1093 \| 0.2497 \| 0.2496 \| \| 0.8199 \| 8.0 \| 872 \| 0.5982 \| 0.2740 \| 0.1375 \| 0.2694 \| 0.2698 \| \| 0.8199 \| 9.0 \| 981 \| 0.6575 \| 0.2730 \| 0.1368 \| 0.2723 \| 0.2714 \| \| 0.0653 \| 10.0 \| 1090 \| 0.6753 \| 0.2822 \| 0.1519 \| 0.2798 \| 0.2781 \| \| 0.0653 \| 11.0 \| 1199 \| 0.6923 \| 0.2795 \| 0.1486 \| 0.2780 \| 0.2774 \| \| 0.0653 \| 12.0 \| 1308 \| 0.7350 \| 0.2471 \| 0.1209 \| 0.2458 \| 0.2457 \| \| 0.0653 \| 13.0 \| 1417 \| 0.7698 \| 0.2762 \| 0.1463 \| 0.2720 \| 0.2733 \| \| 0.0225 \| 14.0 \| 1526 \| 0.7867 \| 0.2771 \| 0.1372 \| 0.2763 \| 0.2755 \| \| 0.0225 \| 15.0 \| 1635 \| 0.8166 \| 0.3166 \| 0.1689 \| 0.3132 \| 0.3133 \| \| 0.0225 \| 16.0 \| 1744 \| 0.8085 \| 0.3027 \| 0.1572 \| 0.2998 \| 0.3009 \| \| 0.0225 \| 17.0 \| 1853 \| 0.8162 \| 0.3090 \| 0.1734 \| 0.3025 \| 0.3038 \| \| 0.0225 \| 18.0 \| 1962 \| 0.8484 \| 0.2965 \| 0.1627 \| 0.2917 \| 0.2909 \| \| 0.0105 \| 19.0 \| 2071 \| 0.8610 \| 0.2881 \| 0.1487 \| 0.2813 \| 0.2819 \| \| 0.0105 \| 20.0 \| 2180 \| 0.8688 \| 0.2811 \| 0.1494 \| 0.2755 \| 0.2770 \| \| 0.0105 \| 21.0 \| 2289 \| 0.8733 \| 0.2777 \| 0.1453 \| 0.2708 \| 0.2724 \| \| 0.0105 \| 22.0 \| 2398 \| 0.8776 \| 0.2771 \| 0.1475 \| 0.2709 \| 0.2711 \| \| 0.0061 \| 23.0 \| 2507 \| 0.8717 \| 0.2829 \| 0.1467 \| 0.2749 \| 0.2749 \| \| 0.0061 \| 24.0 \| 2616 \| 0.8729 \| 0.2878 \| 0.1467 \| 0.2803 \| 0.2806 \| \| 0.0061 \| 25.0 \| 2725 \| 0.8755 \| 0.2921 \| 0.1519 \| 0.2857 \| 0.2866 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
jgeselowitz/poem_labeler	jgeselowitz	2023-09-21T19:43:00Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-21T19:24:46Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: poem_labeler 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. --> # poem_labeler 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: 1.1318 - Accuracy: 0.7315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0602 \| 1.0 \| 2382 \| 0.9264 \| 0.6935 \| \| 0.5889 \| 2.0 \| 4764 \| 0.9186 \| 0.723 \| \| 0.2638 \| 3.0 \| 7146 \| 1.1318 \| 0.7315 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
glaiveai/glaive-coder-7b	glaiveai	2023-09-21T19:35:50Z	1,565	54	transformers	[ "transformers", "pytorch", "llama", "text-generation", "code", "en", "dataset:glaiveai/glaive-code-assistant", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-17T14:49:44Z	--- license: llama2 datasets: - glaiveai/glaive-code-assistant language: - en tags: - code --- # Glaive-coder-7b Glaive-coder-7b is a 7B parameter code model trained on a dataset of ~140k programming related problems and solutions generated from Glaive’s synthetic data generation platform. The model is fine-tuned on the CodeLlama-7b model. ## Usage: The model is trained to act as a code assistant, and can do both single instruction following and multi-turn conversations. It follows the same prompt format as CodeLlama-7b-Instruct- ``` <s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_msg }} [/INST] {{ model_answer }} </s> <s>[INST] {{ user_msg }} [/INST] ``` You can run the model in the following way- ```python from transformers import AutoModelForCausalLM , AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("glaiveai/glaive-coder-7b") model = AutoModelForCausalLM.from_pretrained("glaiveai/glaive-coder-7b").half().cuda() def fmt_prompt(prompt): return f"<s> [INST] {prompt} [/INST]" inputs = tokenizer(fmt_prompt(prompt),return_tensors="pt").to(model.device) outputs = model.generate(**inputs,do_sample=True,temperature=0.1,top_p=0.95,max_new_tokens=100) print(tokenizer.decode(outputs[0],skip_special_tokens=True,clean_up_tokenization_spaces=False)) ``` ## Benchmarks: The model achieves a 63.1% pass@1 on HumanEval and a 45.2% pass@1 on MBPP, however it is evident that these benchmarks are not representative of real-world usage of code models so we are launching the [Code Models Arena](https://arena.glaive.ai/) to let users vote on model outputs so we can have a better understanding of user preference on code models and come up with new and better benchmarks. We plan to release the Arena results as soon as we have a sufficient amount of data. Join the Glaive [discord](https://discord.gg/fjQ4uf3yWD) for improvement suggestions, bug-reports and collaborating on more open-source projects.
mychen76/donut-receipt_v3	mychen76	2023-09-21T19:29:49Z	66	0	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-09-20T17:01:53Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut-receipt_v3 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. --> # donut-receipt_v3 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
Tanor/SRGPTSENTPOS6	Tanor	2023-09-21T19:28:12Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-classification", "generated_from_trainer", "base_model:Tanor/SRGPTSENTPOS6", "base_model:finetune:Tanor/SRGPTSENTPOS6", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-09-07T17:55:14Z	--- base_model: Tanor/SRGPTSENTPOS6 tags: - generated_from_trainer metrics: - f1 model-index: - name: SRGPTSENTPOS6 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. --> # SRGPTSENTPOS6 This model is a fine-tuned version of [Tanor/SRGPTSENTPOS6](https://huggingface.co/Tanor/SRGPTSENTPOS6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3376 - F1: 0.1538 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 32 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.0145 \| 1.0 \| 2706 \| 0.4809 \| 0.2526 \| \| 0.0052 \| 2.0 \| 5412 \| 0.3511 \| 0.1562 \| \| 0.0136 \| 3.0 \| 8118 \| 0.3620 \| 0.2222 \| \| 0.0028 \| 4.0 \| 10824 \| 0.3376 \| 0.1538 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.1.0.dev20230801 - Datasets 2.14.2 - Tokenizers 0.13.3
noelsinghsr/sagemaker-distilbert-emotion	noelsinghsr	2023-09-21T19:26:05Z	103	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	2023-09-21T19:24:14Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: sagemaker-distilbert-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: test args: split metrics: - name: Accuracy type: accuracy value: 0.9125 --- <!-- 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. --> # sagemaker-distilbert-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: 0.2634 - Accuracy: 0.9125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9347 \| 1.0 \| 500 \| 0.2634 \| 0.9125 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.13.3
NousResearch/Llama-2-70b-chat-hf	NousResearch	2023-09-21T19:05:17Z	625	19	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "facebook", "meta", "llama-2", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-07-19T04:36:22Z	--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved." extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
mirfan899/uner-bert-ner	mirfan899	2023-09-21T18:53:09Z	115	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-21T18:52:35Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: uner-bert-ner 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. --> # uner-bert-ner 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: 0.1354 - Precision: 0.8267 - Recall: 0.8707 - F1: 0.8481 - Accuracy: 0.9640 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 144 \| 0.1496 \| 0.7687 \| 0.7971 \| 0.7826 \| 0.9533 \| \| No log \| 2.0 \| 288 \| 0.1429 \| 0.7719 \| 0.8584 \| 0.8129 \| 0.9573 \| \| No log \| 3.0 \| 432 \| 0.1267 \| 0.8014 \| 0.8682 \| 0.8335 \| 0.9629 \| \| 0.1628 \| 4.0 \| 576 \| 0.1316 \| 0.8206 \| 0.8723 \| 0.8457 \| 0.9644 \| \| 0.1628 \| 5.0 \| 720 \| 0.1354 \| 0.8267 \| 0.8707 \| 0.8481 \| 0.9640 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3
mirfan899/uner-roberta-ner	mirfan899	2023-09-21T18:42:07Z	103	1	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-21T18:40:21Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: uner-roberta-ner 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. --> # uner-roberta-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0930 - Precision: 0.8622 - Recall: 0.9010 - F1: 0.8812 - Accuracy: 0.9728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 144 \| 0.1285 \| 0.8005 \| 0.8241 \| 0.8121 \| 0.9589 \| \| No log \| 2.0 \| 288 \| 0.1142 \| 0.8142 \| 0.8748 \| 0.8434 \| 0.9655 \| \| No log \| 3.0 \| 432 \| 0.0962 \| 0.8485 \| 0.8985 \| 0.8728 \| 0.9702 \| \| 0.1923 \| 4.0 \| 576 \| 0.0916 \| 0.8543 \| 0.9018 \| 0.8774 \| 0.9719 \| \| 0.1923 \| 5.0 \| 720 \| 0.0930 \| 0.8622 \| 0.9010 \| 0.8812 \| 0.9728 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3
Tostino/Inkbot-13b-4k	Tostino	2023-09-21T18:36:48Z	9	10	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-21T05:56:48Z	# Model Card for Inkbot ## Model Details Inkbot is a conversational AI model designed to interpret and respond to structured prompts with or without contextual information. Built on the latest advancements in natural language processing (NLP) and understanding (NLU), Inkbot provides users with accurate and meaningful interactions, addressing a wide range of queries and topics. Its unique feature lies in the structured prompt system, allowing users to engage in dynamic dialogues that can evolve based on context, metadata, and user input. ### Performance - The model excels in RAG type queries, answering from context, and overriding memory when available. - It can handle very large contexts, but may sometimes enter a repeating text loop, especially during complex tasks. - The model is intended to be more functional and less chatty, avoiding the waste of tokens on superfluous language. ## How to Use Inkbot uses a structured prompt template system. ### Prompt Template Structure #### With Context: ``` <#meta#> - Date: [DATE] - Task: [TASK TYPE] <#system#> [SYSTEM PROMPT/DESCRIPTION OF THE INTERACTION] <#chat#> <#user#> [USER QUESTION] <#bot#> [BOT RESPONSE] <#user#> [USER FOLLOW-UP QUESTION] <#user_context#> [ADDITIONAL CONTEXT FOR THE BOT] <#bot#> [BOT RESPONSE BASED ON CONTEXT] ``` #### Without Context: ``` <#meta#> - Date: [DATE] - Task: [TASK TYPE] <#system#> [SYSTEM PROMPT/DESCRIPTION OF THE INTERACTION] <#chat#> <#user#> [USER QUESTION] <#bot#> [BOT RESPONSE] <#user#> [USER FOLLOW-UP QUESTION] <#bot#> [BOT RESPONSE] ``` ### Examples #### With Context: ``` <#meta#> - Date: 2023-07-20 - Task: general <#system#> A dialogue focused on determining dates based on context clues. <#chat#> <#user#> What is today's date? <#bot#> The date is: 2023-07-20 <#user#> When did the event in the provided context take place? <#user_context#> It's the day after new years eve, and I'm starting 2018 with a headache. <#bot#> The event in the context took place on: 2018-01-01 ``` #### Without Context: ``` <#meta#> - Date: 2023-07-20 - Task: general <#system#> A simple dialogue about daily queries. <#chat#> <#user#> Will it rain today? <#bot#> I am not sure, without knowing your location I am unable to determine the weather. <#user#> Can you compose a haiku for me? <#bot#> Whispers of the breeze, Nature's melodies unfold, Dawn dispels the night. ``` ## Task Options Inkbot has been trained for a variety of tasks. Below are some of the key task options you can utilize, but feel free to try others to test the effect: 1. general: This is the default task and is designed for a broad range of general questions and interactions. - Usage: Suitable for most day-to-day interactions and queries. 2. knowledge_graph: This task involves extracting, understanding, and representing information in a structured way. - Usage: When you want to extract relationships between entities or desire structured representations of data. 3. question_answer: Explicitly trained for answering questions in a straightforward manner. - Usage: Best used when you have direct questions and expect concise answers. 4. reasoning: Allows Inkbot to showcase its logical and deductive reasoning capabilities. - Usage: Ideal for puzzles, riddles, or scenarios where logical analysis is required. 5. translation: Use this for language translation tasks. - Usage: Provide a sentence or paragraph in one language, and specify the desired target language for translation. 6. summarization: Trained for condensing large texts into shorter, coherent summaries. - Usage: When you have a lengthy text or article that you want to be summarized to its key points. 7. creative_writing: Engage Inkbot in composing stories, poetry, and other creative content. - Usage: For tasks that require imaginative and original content generation. ## Limitations - Adhere to the prompt structure for best results. - When providing contextual details, clarity is essential for Inkbot to derive accurate and meaningful responses. - The overriding memory from user_context property generally only works for the next prompt or two, after which the model reverts to its original behavior. - On complex tasks, like creating a coherent story based on a set of facts from context, there's a potential for a repeating text loop as context fills. - Sometimes the model doesn't know when to end a knowledge graph, which can result in adding nodes and edges until it runs out of context. ## Additional Notes - The 'date', 'task', and 'system' are crucial metadata components that need to be provided outside the core dialogue. - Use the 'user_context' when you want to offer supplementary context that guides Inkbot's response. You can interleave it in the chat log as necessary. - The specific tag format, such as `<#word#>`, is used to because there are filters in a lot of APIs for <\|word\|> and this makes interactions easier. --- license: llama2 ---
dancingninjas/sentiment-model	dancingninjas	2023-09-21T18:24:24Z	68	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "text classification", "transformer", "sentiment analysis", "TensorFlow", "en", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-21T10:54:56Z	--- license: apache-2.0 datasets: - imdb language: - en pipeline_tag: text-classification tags: - text classification - transformer - sentiment analysis - distilbert - TensorFlow ---
sudhanvasp/Sentiment-Analysis	sudhanvasp	2023-09-21T18:19:27Z	164	2	transformers	[ "transformers", "pytorch", "tf", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-20T13:47:15Z	# SentimentAnalysis Sentiment analysis with NLTK (Folder 79) Sentiment analysis with Roberta (Folder 159) Sentiment analysis with Roberta+Awk (Folder 209) Sentiment analysis with Roberta+Gradio (Folder 219) <!-- MARKER: Start of README --> # Stock Sentiment Analysis of Tweets using RoBERTa ![Feature 1 Screenshot](Media/1.jpeg) ![Feature 1 Screenshot](Media/2.jpeg) ![Feature 1 Screenshot](Media/3.png) ![Feature 1 Screenshot](Media/4.png) ![Feature 1 Screenshot](Media/5.png) ## Table of Contents - [Project Description](#project-description) - [Objective](#objective) - [Hypotheses](#hypotheses) - [Data Collection](#data-collection) - [Sentiment Analysis](#sentiment-analysis) - [Machine Learning Model](#machine-learning-model) - [Running the Model](#running-the-model) - [Huggingface](https://huggingface.co/sudhanvasp/Sentiment-Analysis) - [Results and Insights](#results-and-insights) - [License](#license) --- <!-- MARKER: Project Description --> ## Project Description Welcome to the Stock Sentiment Analysis project! This repository houses the code and resources for analyzing Twitter data to predict stock price movements based on sentiment analysis, leveraging the powerful RoBERTa model. Gain valuable insights into market sentiment and enhance your trading strategies. <!-- MARKER: Objective --> ## Objective The primary aim of this project is to explore the intricate relationship between sentiment expressed in tweets and short-term stock price movements. <!-- MARKER: Hypotheses --> ## Hypotheses - Hypothesis 1: Tweets with a positive sentiment will exhibit a positive correlation with stock price increases. - Hypothesis 2: Tweets with a negative sentiment will display a negative correlation with stock price decreases. - Hypothesis 3: Tweets with a neutral sentiment will display a neutral correlation with stock price. <!-- MARKER: Data Collection --> ## Data Collection - We meticulously gathered Twitter data from financial news and analyst accounts. - Data preprocessing was performed, encompassing deduplication, tokenization, and sentiment label encoding (positive, negative, neutral). <!-- MARKER: Sentiment Analysis --> ## Sentiment Analysis - Harnessing RoBERTa, a state-of-the-art transformer-based model, we assigned sentiment scores. - Challenges such as domain-specific sentiment expressions and model fine-tuning were addressed. <!-- MARKER: Machine Learning Model --> ## Machine Learning Model - Our model is a robust ensemble of RoBERTa. - Features encompass RoBERTa-generated F1 scores, tweet volume, and historical stock price data. - This amalgamation empowers us to capture both sequential dependencies and non-linear relationships effectively. <!-- MARKER: Running the Model --> ## Running the Model ## Hosting with Gradio 1. Install Gradio: ```bash pip install gradio import gradio as gr 2. Run the given gradio code in the Folder 219. ## Hosting with FLASK 1. Install FLASK: ```bash pip install flask cd 209 cd twitterka python app.py 2. Open the IP given address. <!-- MARKER: Project Description --> ## Huggingface Page - Execution of the model can be done directly on Huggingface as well - [Huggingface](https://huggingface.co/sudhanvasp/Sentiment-Analysis) <!-- MARKER: Results and Insights--> ## Results and Insights - Our ensemble model boasts an impressive 96% accuracy in sentiment analysis. - Notably, positive sentiment tweets correlate positively with stock price increases, while negative sentiment tweets correlate negatively with decreases. Neutral sentiment, while present, exhibits a weaker influence on stock price movements. <!-- MARKER: License--> ## License - This created by the team "The Lost Pendrive" (Sudhanva SP, Deepa Umesh, Chinmayi Rajaram)
cloudwalkerw/wavlm-base_3	cloudwalkerw	2023-09-21T17:48:37Z	16	0	transformers	[ "transformers", "pytorch", "wavlm", "audio-classification", "generated_from_trainer", "base_model:microsoft/wavlm-base", "base_model:finetune:microsoft/wavlm-base", "endpoints_compatible", "region:us" ]	audio-classification	2023-09-18T14:31:32Z	--- base_model: microsoft/wavlm-base tags: - audio-classification - generated_from_trainer metrics: - accuracy model-index: - name: wavlm-base_3 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. --> # wavlm-base_3 This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6534 - Accuracy: 0.8974 ## 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: 2 - seed: 0 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2236 \| 1.24 \| 100 \| 12.8495 \| 0.4467 \| \| 0.0514 \| 2.48 \| 200 \| 16.3078 \| 0.2677 \| \| 0.0 \| 3.72 \| 300 \| 17.5651 \| 0.2597 \| \| 0.3252 \| 4.95 \| 400 \| 15.0382 \| 0.1912 \| \| 1.0577 \| 6.19 \| 500 \| 0.6534 \| 0.8974 \| \| 0.6973 \| 7.43 \| 600 \| 0.7352 \| 0.1026 \| \| 0.6939 \| 8.67 \| 700 \| 0.6210 \| 0.8974 \| \| 0.6944 \| 9.91 \| 800 \| 0.7129 \| 0.1026 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.0.post302 - Datasets 2.14.5 - Tokenizers 0.13.3
darxkies/bart-large-cnn-samsum-ChatGPT_v3	darxkies	2023-09-21T17:47:31Z	10	1	transformers	[ "transformers", "rust", "bart", "text2text-generation", "summarization", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-09-21T15:44:26Z	--- pipeline_tag: summarization --- Original model: [Qiliang/bart-large-cnn-samsum-ChatGPT_v3](https://https://huggingface.co/Qiliang/bart-large-cnn-samsum-ChatGPT_v3) Added files for [rust-bert](https://github.com/guillaume-be/rust-bert)
TuningAI/DETR-BASE_Marine	TuningAI	2023-09-21T17:47:20Z	167	1	transformers	[ "transformers", "pytorch", "detr", "object-detection", "climate", "ar", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-09-21T08:29:14Z	--- license: apache-2.0 language: - ar - en library_name: transformers pipeline_tag: object-detection tags: - climate --- # DETR-BASE_Marine ## Overview + Model Name: DETR-BASE_Marine + Model Architecture: DETR (End-to-End Object Detection) with ResNet-50 backbone. + Model Type: Object Detection + Framework: PyTorch + Dataset: Aerial Maritime Image Dataset + License: MIT License (for the dataset) ## Model Description The DETR-BASE_Marine Aerial Maritime Detector is a deep learning model based on the DETR architecture with a ResNet-50 backbone. It has been fine-tuned on the "Aerial Maritime Image Dataset," which comprises 74 aerial photographs captured via a Mavic Air 2 drone. The model is designed for object detection tasks in maritime environments and can identify and locate various objects such as docks, boats, lifts, jetskis, and cars in aerial images. ## Key Features: + Multi-class object detection. + Object classes: Docks, Boats, Lifts, Jetskis, Cars. + Robust performance in aerial and maritime scenarios. ## Use Cases + Boat Counting: Count the number of boats on water bodies, such as lakes, using drone imagery. + Boat Lift Detection: Identify the presence of boat lifts on the waterfront via aerial surveillance. + Car Detection: Detect and locate cars within maritime regions using UAV drones. + Habitability Assessment: Determine the level of inhabitation around lakes and water bodies based on detected objects. + Property Monitoring: Identify if visitors or activities are present at lake houses or properties using drone surveillance. + Proof of Concept: Showcase the potential of UAV imagery for maritime projects and object detection tasks. ## Dataset + Dataset Name: Aerial Maritime Image Dataset + Number of Images: 74 + Number of Bounding Boxes: 1,151 + Collection Method: Captured via Mavic Air 2 drone at 400 ft altitude. ## Usage ``` python from transformers import DetrImageProcessor, DetrForObjectDetection import torch from PIL import Image img_path = "" image = Image.open(img_path) extractor = AutoFeatureExtractor.from_pretrained("TuningAI/DETR-BASE_Marine") model = AutoModelForObjectDetection.from_pretrained("TuningAI/DETR-BASE_Marine") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.9 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` ## License This model is provided under the MIT License. The Aerial Maritime Image Dataset used for fine-tuning is also under the MIT License.
darxkies/bge-base-en-v1.5	darxkies	2023-09-21T17:47:07Z	2	0	transformers	[ "transformers", "rust", "bert", "feature-extraction", "sentence-similarity", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-09-20T21:36:50Z	--- pipeline_tag: sentence-similarity --- Original model: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) Added files for [rust-bert](https://github.com/guillaume-be/rust-bert)
megagonlabs/transformers-ud-japanese-electra-base-ginza-520	megagonlabs	2023-09-21T17:45:45Z	125	3	transformers	[ "transformers", "pytorch", "electra", "feature-extraction", "PyTorch", "Transformers", "spaCy", "ELECTRA", "GiNZA", "mC4", "UD_Japanese-BCCWJ", "GSK2014-A", "ja", "MIT", "arxiv:1910.10683", "license:mit", "endpoints_compatible", "region:us" ]	feature-extraction	2023-09-21T14:14:04Z	--- language: - ja thumbnail: "https://raw.githubusercontent.com/megagonlabs/ginza/static/docs/images/GiNZA_logo_4c_s.png" tags: - PyTorch - Transformers - spaCy - ELECTRA - GiNZA - mC4 - UD_Japanese-BCCWJ - GSK2014-A - ja - MIT license: "mit" datasets: - mC4 - UD_Japanese_BCCWJ-r2.8 - GSK2014-A(2019) metrics: - UAS - LAS - UPOS --- # transformers-ud-japanese-electra-ginza-520 (sudachitra-wordpiece, mC4 Japanese) This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences extracted from the [mC4](https://huggingface.co/datasets/mc4) and finetuned by [spaCy v3](https://spacy.io/usage/v3) on [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html). The base pretrain model is [megagonlabs/transformers-ud-japanese-electra-base-discrimininator](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator). The entire spaCy v3 model is distributed as a python package named [`ja_ginza_electra`](https://pypi.org/project/ja-ginza-electra/) from PyPI along with [`GiNZA v5`](https://github.com/megagonlabs/ginza) which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures. Try running it as below: ```console $ pip install ginza ja_ginza_electra $ ginza ``` ## Licenses The models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php). ## Acknowledgments This model is permitted to be published under the `MIT License` under a joint research agreement between NINJAL (National Institute for Japanese Language and Linguistics) and Megagon Labs Tokyo. ## Citations - [mC4](https://huggingface.co/datasets/mc4) Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` - [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html) ``` Asahara, M., Kanayama, H., Tanaka, T., Miyao, Y., Uematsu, S., Mori, S., Matsumoto, Y., Omura, M., & Murawaki, Y. (2018). Universal Dependencies Version 2 for Japanese. In LREC-2018. ``` - [GSK2014-A(2019)](https://www.gsk.or.jp/catalog/gsk2014-a/)
kmposkid1/Horse-Health-Outcome-6d56348a-8c02-416a-8d5d-fdf98b0d4f59	kmposkid1	2023-09-21T17:38:09Z	0	0	sklearn	[ "sklearn", "skops", "tabular-classification", "license:mit", "region:us" ]	tabular-classification	2023-09-20T13:45:41Z	--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: LightGBM_without_hospital_number_01.pkl widget: structuredData: abdomen: - distend_small - distend_small - distend_large abdominal_distention: - none - none - moderate abdomo_appearance: - serosanguious - cloudy - serosanguious abdomo_protein: - 4.1 - 4.3 - 2.0 age: - adult - adult - adult capillary_refill_time: - less_3_sec - less_3_sec - more_3_sec cp_data: - 'yes' - 'yes' - 'no' lesion_1: - 7209 - 2112 - 5400 lesion_2: - 0 - 0 - 0 lesion_3: - 0 - 0 - 0 mucous_membrane: - bright_pink - bright_pink - dark_cyanotic nasogastric_reflux: - none - none - more_1_liter nasogastric_reflux_ph: - 7.0 - 3.5 - 2.0 nasogastric_tube: - slight - none - significant packed_cell_volume: - 37.0 - 44.0 - 65.0 pain: - depressed - mild_pain - extreme_pain peripheral_pulse: - normal - normal - reduced peristalsis: - hypermotile - hypomotile - absent pulse: - 84.0 - 66.0 - 72.0 rectal_exam_feces: - absent - decreased - absent rectal_temp: - 39.0 - 38.5 - 37.3 respiratory_rate: - 24.0 - 21.0 - 30.0 surgery: - 'yes' - 'yes' - 'yes' surgical_lesion: - 'yes' - 'yes' - 'yes' temp_of_extremities: - cool - normal - cool total_protein: - 6.5 - 7.6 - 13.0 --- # Model description This is a `LightGBM` model trained on horse health outcome data from Kaggle. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure [More Information Needed] ### Hyperparameters <details> <summary> Click to expand </summary> \| Hyperparameter \| Value \| \|--------------------------------------------------\|-----------------------------------------------------------------------------------------------------------------------------\| \| memory \| \| \| steps \| [('preprocessor', ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])])), ('classifier', LGBMClassifier(max_depth=3))] \| \| verbose \| False \| \| preprocessor \| ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])]) \| \| classifier \| LGBMClassifier(max_depth=3) \| \| preprocessor__n_jobs \| \| \| preprocessor__remainder \| passthrough \| \| preprocessor__sparse_threshold \| 0.3 \| \| preprocessor__transformer_weights \| \| \| preprocessor__transformers \| [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]), ['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']), ('cat', Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data'])] \| \| preprocessor__verbose \| False \| \| preprocessor__verbose_feature_names_out \| True \| \| preprocessor__num \| Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]) \| \| preprocessor__cat \| Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]) \| \| preprocessor__num__memory \| \| \| preprocessor__num__steps \| [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())] \| \| preprocessor__num__verbose \| False \| \| preprocessor__num__imputer \| SimpleImputer(strategy='median') \| \| preprocessor__num__scaler \| StandardScaler() \| \| preprocessor__num__imputer__add_indicator \| False \| \| preprocessor__num__imputer__copy \| True \| \| preprocessor__num__imputer__fill_value \| \| \| preprocessor__num__imputer__keep_empty_features \| False \| \| preprocessor__num__imputer__missing_values \| nan \| \| preprocessor__num__imputer__strategy \| median \| \| preprocessor__num__scaler__copy \| True \| \| preprocessor__num__scaler__with_mean \| True \| \| preprocessor__num__scaler__with_std \| True \| \| preprocessor__cat__memory \| \| \| preprocessor__cat__steps \| [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))] \| \| preprocessor__cat__verbose \| False \| \| preprocessor__cat__imputer \| SimpleImputer(fill_value='missing', strategy='constant') \| \| preprocessor__cat__onehot \| OneHotEncoder(handle_unknown='ignore') \| \| preprocessor__cat__imputer__add_indicator \| False \| \| preprocessor__cat__imputer__copy \| True \| \| preprocessor__cat__imputer__fill_value \| missing \| \| preprocessor__cat__imputer__keep_empty_features \| False \| \| preprocessor__cat__imputer__missing_values \| nan \| \| preprocessor__cat__imputer__strategy \| constant \| \| preprocessor__cat__onehot__categories \| auto \| \| preprocessor__cat__onehot__drop \| \| \| preprocessor__cat__onehot__dtype \| <class 'numpy.float64'> \| \| preprocessor__cat__onehot__feature_name_combiner \| concat \| \| preprocessor__cat__onehot__handle_unknown \| ignore \| \| preprocessor__cat__onehot__max_categories \| \| \| preprocessor__cat__onehot__min_frequency \| \| \| preprocessor__cat__onehot__sparse \| deprecated \| \| preprocessor__cat__onehot__sparse_output \| True \| \| classifier__boosting_type \| gbdt \| \| classifier__class_weight \| \| \| classifier__colsample_bytree \| 1.0 \| \| classifier__importance_type \| split \| \| classifier__learning_rate \| 0.1 \| \| classifier__max_depth \| 3 \| \| classifier__min_child_samples \| 20 \| \| classifier__min_child_weight \| 0.001 \| \| classifier__min_split_gain \| 0.0 \| \| classifier__n_estimators \| 100 \| \| classifier__n_jobs \| \| \| classifier__num_leaves \| 31 \| \| classifier__objective \| \| \| classifier__random_state \| \| \| classifier__reg_alpha \| 0.0 \| \| classifier__reg_lambda \| 0.0 \| \| classifier__subsample \| 1.0 \| \| classifier__subsample_for_bin \| 200000 \| \| classifier__subsample_freq \| 0 \| </details> ### Model Plot <style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: 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border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 /display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier', LGBMClassifier(max_depth=3))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier', LGBMClassifier(max_depth=3))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler())]),['rectal_temp', 'pulse', 'respiratory_rate','nasogastric_reflux_ph', 'packed_cell_volume','total_protein', 'abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pipeline(steps=[('imputer',SimpleI...='missing',strategy='constant')),('onehot',OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age', 'temp_of_extremities','peripheral_pulse', 'mucous_membrane','capillary_refill_time', 'pain','peristalsis', 'abdominal_distention','nasogastric_tube', 'nasogastric_reflux','rectal_exam_feces', 'abdomen','abdomo_appearance', 'surgical_lesion','cp_data'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-30" type="checkbox" ><label for="sk-estimator-id-30" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-31" type="checkbox" ><label for="sk-estimator-id-31" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-32" type="checkbox" ><label for="sk-estimator-id-32" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-33" type="checkbox" ><label for="sk-estimator-id-33" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMClassifier</label><div class="sk-toggleable__content"><pre>LGBMClassifier(max_depth=3)</pre></div></div></div></div></div></div></div> ## Evaluation Results \| Metric \| Value \| \|----------\|----------\| \| accuracy \| 0.740891 \| \| f1 score \| 0.740891 \| ### Confusion Matrix ![Confusion Matrix](confusion_matrix.png) ## Permutation Importance ![Permutation Importance](feature_importance.png) # How to Get Started with the Model [More Information Needed] # Model Card Authors kmposkid # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. BibTeX:* ``` [More Information Needed] ```
benedikt-schaber/q-Taxi-v3	benedikt-schaber	2023-09-21T17:35:46Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T17:35:45Z	--- 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: 7.56 +/- 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="benedikt-schaber/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"]) ```
AmirH98/Q-Taxi-V3	AmirH98	2023-09-21T17:35:01Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T17:35:00Z	--- 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: 7.56 +/- 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="AmirH98/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"]) ```
steveice/videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen	steveice	2023-09-21T17:13:55Z	8	0	transformers	[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-large-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-large-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-09-20T21:16:12Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-large-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen 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. --> # videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6309 - Accuracy: 0.8900 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 11100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 3.5158 \| 0.02 \| 222 \| 3.6067 \| 0.0588 \| \| 2.8571 \| 1.02 \| 444 \| 3.1445 \| 0.3014 \| \| 1.8854 \| 2.02 \| 666 \| 2.3644 \| 0.4607 \| \| 1.5533 \| 3.02 \| 888 \| 1.7967 \| 0.5621 \| \| 1.3935 \| 4.02 \| 1110 \| 1.3755 \| 0.6502 \| \| 1.1722 \| 5.02 \| 1332 \| 1.2232 \| 0.7109 \| \| 0.2896 \| 6.02 \| 1554 \| 1.2859 \| 0.6256 \| \| 0.3166 \| 7.02 \| 1776 \| 1.2910 \| 0.6720 \| \| 0.6902 \| 8.02 \| 1998 \| 1.2702 \| 0.6995 \| \| 0.4193 \| 9.02 \| 2220 \| 1.2087 \| 0.7137 \| \| 0.1889 \| 10.02 \| 2442 \| 1.0500 \| 0.7611 \| \| 0.4502 \| 11.02 \| 2664 \| 1.1647 \| 0.7118 \| \| 0.7703 \| 12.02 \| 2886 \| 1.1037 \| 0.7242 \| \| 0.0957 \| 13.02 \| 3108 \| 1.0967 \| 0.7706 \| \| 0.3202 \| 14.02 \| 3330 \| 1.0479 \| 0.7545 \| \| 0.3634 \| 15.02 \| 3552 \| 1.0714 \| 0.8057 \| \| 0.3883 \| 16.02 \| 3774 \| 1.2323 \| 0.7498 \| \| 0.0322 \| 17.02 \| 3996 \| 1.0504 \| 0.7848 \| \| 0.5108 \| 18.02 \| 4218 \| 1.1356 \| 0.7915 \| \| 0.309 \| 19.02 \| 4440 \| 1.1409 \| 0.7592 \| \| 0.56 \| 20.02 \| 4662 \| 1.0828 \| 0.7915 \| \| 0.3675 \| 21.02 \| 4884 \| 0.9154 \| 0.8123 \| \| 0.0076 \| 22.02 \| 5106 \| 1.0974 \| 0.8133 \| \| 0.0451 \| 23.02 \| 5328 \| 1.0361 \| 0.8152 \| \| 0.2558 \| 24.02 \| 5550 \| 0.7830 \| 0.8237 \| \| 0.0125 \| 25.02 \| 5772 \| 0.8728 \| 0.8171 \| \| 0.4184 \| 26.02 \| 5994 \| 0.8413 \| 0.8265 \| \| 0.2566 \| 27.02 \| 6216 \| 1.0644 \| 0.8009 \| \| 0.1257 \| 28.02 \| 6438 \| 0.8641 \| 0.8265 \| \| 0.1326 \| 29.02 \| 6660 \| 0.8444 \| 0.8417 \| \| 0.0436 \| 30.02 \| 6882 \| 0.8615 \| 0.8322 \| \| 0.0408 \| 31.02 \| 7104 \| 0.8075 \| 0.8332 \| \| 0.0316 \| 32.02 \| 7326 \| 0.8699 \| 0.8341 \| \| 0.2235 \| 33.02 \| 7548 \| 0.8151 \| 0.8455 \| \| 0.0079 \| 34.02 \| 7770 \| 0.8099 \| 0.8550 \| \| 0.001 \| 35.02 \| 7992 \| 0.8640 \| 0.8370 \| \| 0.0007 \| 36.02 \| 8214 \| 0.7146 \| 0.8483 \| \| 0.464 \| 37.02 \| 8436 \| 0.7917 \| 0.8464 \| \| 0.0005 \| 38.02 \| 8658 \| 0.7239 \| 0.8531 \| \| 0.0004 \| 39.02 \| 8880 \| 0.7702 \| 0.8701 \| \| 0.1705 \| 40.02 \| 9102 \| 0.7543 \| 0.8521 \| \| 0.0039 \| 41.02 \| 9324 \| 0.7456 \| 0.8673 \| \| 0.0168 \| 42.02 \| 9546 \| 0.7255 \| 0.8730 \| \| 0.2615 \| 43.02 \| 9768 \| 0.7453 \| 0.8758 \| \| 0.0004 \| 44.02 \| 9990 \| 0.6824 \| 0.8806 \| \| 0.236 \| 45.02 \| 10212 \| 0.6624 \| 0.8825 \| \| 0.0007 \| 46.02 \| 10434 \| 0.6727 \| 0.8815 \| \| 0.0004 \| 47.02 \| 10656 \| 0.6478 \| 0.8863 \| \| 0.268 \| 48.02 \| 10878 \| 0.6309 \| 0.8900 \| \| 0.0025 \| 49.02 \| 11100 \| 0.6284 \| 0.8900 \| ### Framework versions - Transformers 4.33.2 - Pytorch 1.12.1+cu113 - Datasets 2.14.5 - Tokenizers 0.13.3
annahaz/xlm-roberta-base-misogyny-sexism-tweets	annahaz	2023-09-21T17:11:00Z	104	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-19T17:14:54Z	This model was an experiment BUT NOT THE FINAL MODEL. The final model was *annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal* (https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal) Please consider using/trying that model instead. This model was an experiment for the following paper BUT THIS MODEL IS NOT THE FINAL MODEL: ``` @InProceedings{10.1007/978-3-031-43129-6_9, author="Chang, Rong-Ching and May, Jonathan and Lerman, Kristina", editor="Thomson, Robert and Al-khateeb, Samer and Burger, Annetta and Park, Patrick and A. Pyke, Aryn", title="Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions", booktitle="Social, Cultural, and Behavioral Modeling", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="85--94", abstract="Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model (https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.", isbn="978-3-031-43129-6" } ``` --- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: xlm-roberta-base-misogyny-sexism-tweets 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-misogyny-sexism-tweets This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5009 - Accuracy: 0.796 - F1: 0.8132 - Precision: 0.75 - Recall: 0.888 - Mae: 0.204 - Tn: 352 - Fp: 148 - Fn: 56 - Tp: 444 ## 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 \| Accuracy \| F1 \| Precision \| Recall \| Mae \| Tn \| Fp \| Fn \| Tp \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-----:\|:---:\|:---:\|:--:\|:---:\| \| 0.4947 \| 1.0 \| 1646 \| 0.4683 \| 0.765 \| 0.7866 \| 0.7205 \| 0.866 \| 0.235 \| 332 \| 168 \| 67 \| 433 \| \| 0.4285 \| 2.0 \| 3292 \| 0.4514 \| 0.779 \| 0.8004 \| 0.7298 \| 0.886 \| 0.221 \| 336 \| 164 \| 57 \| 443 \| \| 0.3721 \| 3.0 \| 4938 \| 0.4430 \| 0.781 \| 0.8060 \| 0.7234 \| 0.91 \| 0.219 \| 326 \| 174 \| 45 \| 455 \| \| 0.3127 \| 4.0 \| 6584 \| 0.5009 \| 0.796 \| 0.8132 \| 0.75 \| 0.888 \| 0.204 \| 352 \| 148 \| 56 \| 444 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
newronai/clma2-13b-Chat-Adapter-NasdaqBalanced-1epoch	newronai	2023-09-21T17:05:34Z	2	0	peft	[ "peft", "region:us" ]	null	2023-09-21T17:05:26Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
calculater/looking-up	calculater	2023-09-21T17:03:38Z	0	4	null	[ "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-21T15:55:59Z	--- pipeline_tag: text-to-image license: creativeml-openrail-m --- [looking-up](https://huggingface.co/hhpoo/looking-up/blob/main/looking-up.safetensors) 視線を上げるloraです。ファイルをwebuiのlora指定フォルダ内に入れてご使用ください。トリガータグ等は指定しておらず、適用するだけで画面上方を見上げるはずです。 weightをマイナスにすることで下を見るようにもできるかもしれません。 ![sampleimage](tmpcplve8dm-2.png)
amirabdullah19852020/pythia-410m_utility_reward	amirabdullah19852020	2023-09-21T17:03:36Z	59	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-09-21T15:26:30Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="amirabdullah19852020//tmp/tmplbhw73mv/amirabdullah19852020/pythia-410m_utility_reward") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("amirabdullah19852020//tmp/tmplbhw73mv/amirabdullah19852020/pythia-410m_utility_reward") model = AutoModelForCausalLMWithValueHead.from_pretrained("amirabdullah19852020//tmp/tmplbhw73mv/amirabdullah19852020/pythia-410m_utility_reward") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
TheBlokeAI/jackfram_llama-68m-GPTQ	TheBlokeAI	2023-09-21T16:33:08Z	91	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2023-09-21T16:30:27Z	A 4-bit, 128g, act_order=True GPTQ quantisation of JackFram/llama-68m, a 68 million parameter Llama1 model; created on request for software testing. Not for normal usage!
gpadam/autotrain-prospero-query-training-87679143506	gpadam	2023-09-21T16:23:36Z	114	0	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autotrain", "summarization", "unk", "dataset:gpadam/autotrain-data-prospero-query-training", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-09-07T11:44:46Z	--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain" datasets: - gpadam/autotrain-data-prospero-query-training co2_eq_emissions: emissions: 16.811591021038232 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 87679143506 - CO2 Emissions (in grams): 16.8116 ## Validation Metrics - Loss: 1.544 - Rouge1: 26.107 - Rouge2: 12.267 - RougeL: 22.582 - RougeLsum: 22.590 - Gen Len: 19.956 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/gpadam/autotrain-prospero-query-training-87679143506 ```
Panchovix/airoboros-l2-70b-gpt4-1.4.1_4bit-bpw_variants_h6-exl2	Panchovix	2023-09-21T16:21:13Z	5	0	transformers	[ "transformers", "llama", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-09-13T05:59:53Z	--- license: other --- 4bit variants quantizations of airoboros 70b 1.4.1 (https://huggingface.co/jondurbin/airoboros-l2-70b-gpt4-1.4.1), using exllama2. You can find 4.25bpw (main branch), 4.5bpw and 4.75bpw in each branch. Update 21/09/2023 Re-quanted all variants with latest exllamav2 version, which fixed some measurement issues.
Parkhat/llama2-qlora-finetunined-french	Parkhat	2023-09-21T16:20:44Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-21T16:20:38Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
salesforce/blipdiffusion-controlnet	salesforce	2023-09-21T15:55:24Z	85	2	diffusers	[ "diffusers", "en", "arxiv:2305.14720", "license:apache-2.0", "diffusers:BlipDiffusionControlNetPipeline", "region:us" ]	null	2023-09-21T15:55:24Z	--- license: apache-2.0 language: - en library_name: diffusers --- # BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing <!-- Provide a quick summary of what the model is/does. --> Model card for BLIP-Diffusion, a text to image Diffusion model which enables zero-shot subject-driven generation and control-guided zero-shot generation. The abstract from the paper is: Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. The model is created by Dongxu Li, Junnan Li, Steven C.H. Hoi. ### Model Sources <!-- Provide the basic links for the model. --> - Original Repository: https://github.com/salesforce/LAVIS/tree/main - Project Page: https://dxli94.github.io/BLIP-Diffusion-website/ ## Uses ### Zero-Shot Subject Driven Generation ```python from diffusers.pipelines import BlipDiffusionPipeline from diffusers.utils import load_image import torch blip_diffusion_pipe = BlipDiffusionPipeline.from_pretrained( "Salesforce/blipdiffusion", torch_dtype=torch.float16 ).to("cuda") cond_subject = "dog" tgt_subject = "dog" text_prompt_input = "swimming underwater" cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" ) iter_seed = 88888 guidance_scale = 7.5 num_inference_steps = 25 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt_input, cond_image, cond_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" style="width:500px;"/> Generatred Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog_underwater.png" style="width:500px;"/> ### Controlled subject-driven generation ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import CannyDetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet", torch_dtype=torch.float16 ).to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "teapot" # subject to generate. text_prompt = "on a marble table" cldm_cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" ).resize((512, 512)) canny = CannyDetector() cldm_cond_image = canny(cldm_cond_image, 30, 70, output_type="pil") style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Canny Edge Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/canny_generated.png" style="width:500px;"/> ### Controlled subject-driven generation Scribble ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import HEDdetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet" ) controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble") blip_diffusion_pipe.controlnet = controlnet blip_diffusion_pipe.to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "bag" # subject to generate. text_prompt = "on a table" cldm_cond_image = load_image( "https://huggingface.co/lllyasviel/sd-controlnet-scribble/resolve/main/images/bag.png" ).resize((512, 512)) hed = HEDdetector.from_pretrained("lllyasviel/Annotators") cldm_cond_image = hed(cldm_cond_image) style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Scribble Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble.png" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble_output.png" style="width:500px;"/> ## Model Architecture Blip-Diffusion learns a pre-trained subject representation. uch representation aligns with text embeddings and in the meantime also encodes the subject appearance. This allows efficient fine-tuning of the model for high-fidelity subject-driven applications, such as text-to-image generation, editing and style transfer. To this end, they design a two-stage pre-training strategy to learn generic subject representation. In the first pre-training stage, they perform multimodal representation learning, which enforces BLIP-2 to produce text-aligned visual features based on the input image. In the second pre-training stage, they design a subject representation learning task, called prompted context generation, where the diffusion model learns to generate novel subject renditions based on the input visual features. To achieve this, they curate pairs of input-target images with the same subject appearing in different contexts. Specifically, they synthesize input images by composing the subject with a random background. During pre-training, they feed the synthetic input image and the subject class label through BLIP-2 to obtain the multimodal embeddings as subject representation. The subject representation is then combined with a text prompt to guide the generation of the target image. ![img](https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch.jpg) The architecture is also compatible to integrate with established techniques built on top of the diffusion model, such as ControlNet. They attach the U-Net of the pre-trained ControlNet to that of BLIP-Diffusion via residuals. In this way, the model takes into account the input structure condition, such as edge maps and depth maps, in addition to the subject cues. Since the model inherits the architecture of the original latent diffusion model, they observe satisfying generations using off-the-shelf integration with pre-trained ControlNet without further training. <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch_controlnet.png" style="width:50%;"/> ## Citation BibTeX: If you find this repository useful in your research, please cite: ``` @misc{li2023blipdiffusion, title={BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing}, author={Dongxu Li and Junnan Li and Steven C. H. Hoi}, year={2023}, eprint={2305.14720}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
am-infoweb/QA_SYNTH_19_SEPT_FINETUNE_1.0	am-infoweb	2023-09-21T15:51:29Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T15:00:50Z	--- tags: - generated_from_trainer model-index: - name: QA_SYNTH_19_SEPT_FINETUNE_1.0 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. --> # QA_SYNTH_19_SEPT_FINETUNE_1.0 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1182 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.1211 \| 1.0 \| 1350 \| 0.1318 \| \| 0.0599 \| 2.0 \| 2700 \| 0.1617 \| \| 0.0571 \| 3.0 \| 4050 \| 0.0833 \| \| 0.0248 \| 4.0 \| 5400 \| 0.0396 \| \| 0.0154 \| 5.0 \| 6750 \| 0.0911 \| \| 0.0 \| 6.0 \| 8100 \| 0.1054 \| \| 0.0 \| 7.0 \| 9450 \| 0.1086 \| \| 0.0 \| 8.0 \| 10800 \| 0.1224 \| \| 0.0002 \| 9.0 \| 12150 \| 0.1155 \| \| 0.0025 \| 10.0 \| 13500 \| 0.1182 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
ShivamMangale/XLM-Roberta-base-allhiweakdap_5th_iteration_d5_d4	ShivamMangale	2023-09-21T15:44:38Z	122	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T14:53:27Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-allhiweakdap_5th_iteration_d5_d4 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-allhiweakdap_5th_iteration_d5_d4 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.4580000000000001e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
tim-d/CurtGPT	tim-d	2023-09-21T15:39:01Z	17	1	peft	[ "peft", "text-generation", "en", "dataset:LDJnr/Puffin", "dataset:pvduy/rm_hh_helpful_only", "arxiv:2305.14314", "arxiv:2305.18290", "license:other", "region:us" ]	text-generation	2023-09-21T15:24:37Z	--- license: other language: - en pipeline_tag: text-generation datasets: - LDJnr/Puffin - pvduy/rm_hh_helpful_only library_name: peft widget: - text: "USER: What's better, farming, or using computers (which suck)\nASSISTANT:" --- <table> <tr> <td style="width: 30%; text-align: left; vertical-align: middle"> # CurtGPT Using Microsoft's Phi 1.5 model like it was never intended. </td> <td style="text-align: center;"> <img src="https://github.com/tim-a-davis/silly_little_language_modeling_thing_at_utd/blob/main/curtgpt%20logo.png?raw=true" width="300" height="auto"> </td> </tr> </table> # Main Procedure This model is an adapter on [puffin phi v2](https://huggingface.co/teknium/Puffin-Phi-v2) trained using [QLoRA](https://arxiv.org/pdf/2305.14314.pdf) and [DPO](https://arxiv.org/pdf/2305.18290.pdf) on 60,000 samples from the [anthropic helpful only](https://huggingface.co/datasets/pvduy/rm_hh_helpful_only) dataset. --- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0
ryatora/distilbert-base-uncased-finetuned-emotion	ryatora	2023-09-21T15:36:40Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-19T12:44:18Z	--- 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 args: split metrics: - name: Accuracy type: accuracy value: 0.9225 - name: F1 type: f1 value: 0.9224787080842691 --- <!-- 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: 0.2185 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8423 \| 1.0 \| 250 \| 0.3084 \| 0.9065 \| 0.9049 \| \| 0.2493 \| 2.0 \| 500 \| 0.2185 \| 0.9225 \| 0.9225 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0
ShivamMangale/XLM-Roberta-base-allhiweakdap_5th_iteration_d5	ShivamMangale	2023-09-21T15:35:52Z	122	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T14:45:26Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-allhiweakdap_5th_iteration_d5 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-allhiweakdap_5th_iteration_d5 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.3122e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
neksjgg/rav3nus	neksjgg	2023-09-21T15:22:16Z	0	0	null	[ "streamer", "twitch", "ru", "region:us" ]	null	2023-09-21T15:15:09Z	--- language: - ru tags: - streamer - twitch ---
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1	ShivamMangale	2023-09-21T14:52:39Z	122	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T14:34:47Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1 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-all_hi_weakdap_4th_iteration_d4_d3_d2_d1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
mann-e/mann-e_5.4	mann-e	2023-09-21T14:52:30Z	3	0	diffusers	[ "diffusers", "text-to-image", "region:us" ]	text-to-image	2023-09-21T12:47:14Z	--- library_name: diffusers pipeline_tag: text-to-image --- # Mann-E 5.4 This repository represents what is the main brain of [Mann-E](https://manne.ir) artificial intelligence platform. ## Features 1. _LoRa support_. In previous versions, most of LoRa models weren't working perfectly with the model. 2. _More coherent results_. Compared to the old versions, this version has more "midjourney" feel to its outputs. 3. _New License_. Unlike old versions this one isn't licensed undet MIT, we decided to go with our own license. ## Samples <span align="center"> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-1.png" width=512px /> <br/> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-2.png" width=512px /> <br/> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-3.png" width=512px /> <br/> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-4.png" width=512px /> <br/> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-5.png" width=512px /> </span> ## License This software and associated checkpoints are provided by Mann-E for educational and non-commercial use only. By accessing or using this software and checkpoints, you agree to the following terms and conditions: 1. Access and Use: - You are granted the right to access and use the source code and checkpoints for educational and non-commercial purposes. 2. Modification and Distribution: - You may modify and distribute the source code and checkpoints solely for educational and non-commercial purposes, provided that you retain this license notice. 3. Commercial Use: - Commercial use of this software and checkpoints is strictly prohibited without the explicit written consent of the Copyright Holder. 4. Fine-tuning of Checkpoints: - You may not fine-tune or modify the provided checkpoints without obtaining the express written consent of the Copyright Holder. 5. No Warranty: - This software and checkpoints are provided "as is" without any warranty. The Copyright Holder shall not be liable for any damages or liabilities arising out of the use or inability to use the software and checkpoints. 6. Termination: - This license is effective until terminated by the Copyright Holder. Your rights under this license will terminate automatically without notice from the Copyright Holder if you fail to comply with any term or condition of this license. If you do not agree to these terms and conditions or do not have the legal authority to bind yourself, you may not use, modify, or distribute this software and checkpoints. For inquiries regarding commercial use or fine-tuning of checkpoints, please contact Mann-E.
nickypro/tinyllama-15M-fp32	nickypro	2023-09-21T14:50:50Z	152	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-16T17:23:46Z	--- license: mit --- This is the Float32 15M parameter Llama 2 architecture model trained on the TinyStories dataset. These are converted from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas). See the [llama2.c](https://github.com/karpathy/llama2.c) project for more details.
anjakuzev/13b_200	anjakuzev	2023-09-21T14:50:40Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-21T14:50:37Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0
yunosuken/results	yunosuken	2023-09-21T14:50:34Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:tohoku-nlp/bert-large-japanese-v2", "base_model:finetune:tohoku-nlp/bert-large-japanese-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-13T14:15:12Z	--- license: apache-2.0 base_model: cl-tohoku/bert-large-japanese-v2 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bert-large-japanease-v2-gpt4-relevance-learned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-japanease-v2-gpt4-relevance-learned This model is a fine-tuned version of [cl-tohoku/bert-large-japanese-v2](https://huggingface.co/cl-tohoku/bert-large-japanese-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2693 - Accuracy: 0.885 - F1: 0.8788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 3.3692 \| 1.0 \| 563 \| 3.2122 \| 0.872 \| 0.8560 \| \| 3.0963 \| 2.0 \| 1126 \| 3.1045 \| 0.866 \| 0.8625 \| \| 2.8698 \| 3.0 \| 1689 \| 3.1410 \| 0.882 \| 0.8755 \| \| 2.6212 \| 4.0 \| 2252 \| 3.2119 \| 0.876 \| 0.8702 \| \| 2.407 \| 5.0 \| 2815 \| 3.2693 \| 0.885 \| 0.8788 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
nickypro/tinyllama-110M-fp32	nickypro	2023-09-21T14:50:10Z	166	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-16T17:26:31Z	--- license: mit --- This is the float32 110M parameter Llama 2 architecture model trained on the TinyStories dataset. These are converted from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas). See the [llama2.c](https://github.com/karpathy/llama2.c) project for more details.
ayushtues/blipdiffusion	ayushtues	2023-09-21T14:44:10Z	7	0	diffusers	[ "diffusers", "safetensors", "en", "arxiv:2305.14720", "license:apache-2.0", "diffusers:BlipDiffusionPipeline", "region:us" ]	null	2023-08-07T05:45:01Z	--- license: apache-2.0 language: - en library_name: diffusers --- # BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing <!-- Provide a quick summary of what the model is/does. --> Model card for BLIP-Diffusion, a text to image Diffusion model which enables zero-shot subject-driven generation and control-guided zero-shot generation. The abstract from the paper is: Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. The model is created by Dongxu Li, Junnan Li, Steven C.H. Hoi. ### Model Sources <!-- Provide the basic links for the model. --> - Original Repository: https://github.com/salesforce/LAVIS/tree/main - Project Page: https://dxli94.github.io/BLIP-Diffusion-website/ ## Uses ### Zero-Shot Subject Driven Generation ```python from diffusers.pipelines import BlipDiffusionPipeline from diffusers.utils import load_image import torch blip_diffusion_pipe = BlipDiffusionPipeline.from_pretrained( "Salesforce/blipdiffusion", torch_dtype=torch.float16 ).to("cuda") cond_subject = "dog" tgt_subject = "dog" text_prompt_input = "swimming underwater" cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" ) iter_seed = 88888 guidance_scale = 7.5 num_inference_steps = 25 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt_input, cond_image, cond_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" style="width:500px;"/> Generatred Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog_underwater.png" style="width:500px;"/> ### Controlled subject-driven generation ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import CannyDetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet", torch_dtype=torch.float16 ).to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "teapot" # subject to generate. text_prompt = "on a marble table" cldm_cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" ).resize((512, 512)) canny = CannyDetector() cldm_cond_image = canny(cldm_cond_image, 30, 70, output_type="pil") style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Canny Edge Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/canny_generated.png" style="width:500px;"/> ### Controlled subject-driven generation Scribble ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import HEDdetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet" ) controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble") blip_diffusion_pipe.controlnet = controlnet blip_diffusion_pipe.to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "bag" # subject to generate. text_prompt = "on a table" cldm_cond_image = load_image( "https://huggingface.co/lllyasviel/sd-controlnet-scribble/resolve/main/images/bag.png" ).resize((512, 512)) hed = HEDdetector.from_pretrained("lllyasviel/Annotators") cldm_cond_image = hed(cldm_cond_image) style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Scribble Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble.png" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble_output.png" style="width:500px;"/> ## Model Architecture Blip-Diffusion learns a pre-trained subject representation. uch representation aligns with text embeddings and in the meantime also encodes the subject appearance. This allows efficient fine-tuning of the model for high-fidelity subject-driven applications, such as text-to-image generation, editing and style transfer. To this end, they design a two-stage pre-training strategy to learn generic subject representation. In the first pre-training stage, they perform multimodal representation learning, which enforces BLIP-2 to produce text-aligned visual features based on the input image. In the second pre-training stage, they design a subject representation learning task, called prompted context generation, where the diffusion model learns to generate novel subject renditions based on the input visual features. To achieve this, they curate pairs of input-target images with the same subject appearing in different contexts. Specifically, they synthesize input images by composing the subject with a random background. During pre-training, they feed the synthetic input image and the subject class label through BLIP-2 to obtain the multimodal embeddings as subject representation. The subject representation is then combined with a text prompt to guide the generation of the target image. ![img](https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch.jpg) The architecture is also compatible to integrate with established techniques built on top of the diffusion model, such as ControlNet. They attach the U-Net of the pre-trained ControlNet to that of BLIP-Diffusion via residuals. In this way, the model takes into account the input structure condition, such as edge maps and depth maps, in addition to the subject cues. Since the model inherits the architecture of the original latent diffusion model, they observe satisfying generations using off-the-shelf integration with pre-trained ControlNet without further training. <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch_controlnet.png" style="width:50%;"/> ## Citation BibTeX: If you find this repository useful in your research, please cite: ``` @misc{li2023blipdiffusion, title={BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing}, author={Dongxu Li and Junnan Li and Steven C. H. Hoi}, year={2023}, eprint={2305.14720}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1_d0-hq	ShivamMangale	2023-09-21T14:42:50Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T14:20:18Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1_d0-hq 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-all_hi_weakdap_4th_iteration_d4_d3_d2_d1_d0-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
newronai/clma2-13b-Chat-Adapter-NasdaqBalanced-3epoch	newronai	2023-09-21T14:41:07Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-21T14:41:00Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
jonas-luehrs/chembert_cased-textCLS-RHEOLOGY	jonas-luehrs	2023-09-21T14:40:21Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:jiangg/chembert_cased", "base_model:finetune:jiangg/chembert_cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-21T14:34:07Z	--- base_model: jiangg/chembert_cased tags: - generated_from_trainer metrics: - f1 - precision - recall - accuracy model-index: - name: chembert_cased-textCLS-RHEOLOGY 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. --> # chembert_cased-textCLS-RHEOLOGY This model is a fine-tuned version of [jiangg/chembert_cased](https://huggingface.co/jiangg/chembert_cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6766 - F1: 0.7253 - Precision: 0.7446 - Recall: 0.7407 - Accuracy: 0.7407 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Precision \| Recall \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:---------:\|:------:\|:--------:\| \| 1.2479 \| 1.0 \| 46 \| 0.9758 \| 0.6185 \| 0.5919 \| 0.6605 \| 0.6605 \| \| 0.8039 \| 2.0 \| 92 \| 0.7210 \| 0.7277 \| 0.7472 \| 0.7407 \| 0.7407 \| \| 0.5982 \| 3.0 \| 138 \| 0.6766 \| 0.7253 \| 0.7446 \| 0.7407 \| 0.7407 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
LarryAIDraw/takina_inoue_v1	LarryAIDraw	2023-09-21T14:35:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-21T13:33:54Z	--- license: creativeml-openrail-m --- https://civitai.com/models/148903/takina-inoue-or-lycoris-recoil-5-outfits
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2	ShivamMangale	2023-09-21T14:34:44Z	133	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T14:23:28Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2 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-all_hi_weakdap_4th_iteration_d4_d3_d2 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.8e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
FredericProtat/output	FredericProtat	2023-09-21T14:33:01Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "dataset:decision_transformer_gym_replay", "endpoints_compatible", "region:us" ]	null	2023-09-21T14:32:59Z	--- tags: - generated_from_trainer datasets: - decision_transformer_gym_replay model-index: - name: output 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. --> # output This model is a fine-tuned version of [](https://huggingface.co/) on the decision_transformer_gym_replay 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: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 120 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
ramboind/infra	ramboind	2023-09-21T14:26:54Z	0	0	null	[ "license:cc-by-nc-nd-3.0", "region:us" ]	null	2023-09-21T14:26:54Z	--- license: cc-by-nc-nd-3.0 ---
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1-hq	ShivamMangale	2023-09-21T14:20:17Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T14:06:26Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1-hq 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-all_hi_weakdap_4th_iteration_d4_d3_d2_d1-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
yagizergil/llama2-yagiz	yagizergil	2023-09-21T14:12:17Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-21T12:49:31Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0	ShivamMangale	2023-09-21T14:02:28Z	122	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T09:57:35Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0 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-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3-hq	ShivamMangale	2023-09-21T13:56:46Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T13:48:23Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3-hq 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-all_hi_weakdap_4th_iteration_d4_d3-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.62e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
jmgb0127/bloom-lotr	jmgb0127	2023-09-21T13:56:28Z	1	0	peft	[ "peft", "base_model:bigscience/bloom-3b", "base_model:adapter:bigscience/bloom-3b", "region:us" ]	null	2023-08-28T00:11:48Z	--- library_name: peft base_model: bigscience/bloom-3b --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
cedric7ginobili/margaux	cedric7ginobili	2023-09-21T13:56:23Z	2	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-20T12:04:11Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a margauxmafille person tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
swastikhurana/q-Taxi-v1	swastikhurana	2023-09-21T13:54:37Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T13:54:35Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 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="swastikhurana/q-Taxi-v1", 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"]) ```
Coroseven/TEST	Coroseven	2023-09-21T13:49:30Z	0	0	null	[ "region:us" ]	null	2023-09-07T14:18:20Z	TEST 2 este un model combinat intre Primary model (A) - V3.0 Nordrin_little（诺德琳little）; Secondary model (B) - aamAnyloraAnimeMixAnime_v1 ; Tertiary model (C) - aingdiffusion_v92 la Multiplier (M) - 0.5 Weighted sum TEST 3 este un model combinat intre Primary model (A) - aamAnyloraAnimeMixAnime_v1 ; Secondary model (B) - V3.0 Nordrin_little（诺德琳little）; la Multiplier (M) - 0.5 Weighted sum TEST 5 este un model combinat intre Primary model (A) - aamAnyloraAnimeMixAnime_v1 ; Secondary model (B) - V3.0 Nordrin_little（诺德琳little）; Tertiary model (C) - aingdiffusion_v92 la Multiplier (M) - 0.3 Weighted sum TEST 6 este un model combinat intre Primary model (A) - aamAnyloraAnimeMixAnime_v1 ; Secondary model (B) - BlueAilandMix (blueailandmix_v11) ; la Multiplier (M) - 0.4 Weighted sum TEST 12 este un model combinat intre Primary model (A) - aamAnyloraAnimeMixAnime_v1 ; Secondary model (B) - Sudachi (sudachi_v1.0) ; la Multiplier (M) - 0.5 Weighted sum TEST 13 este un model combinat intre Primary model (A) - TEST 12 ; Secondary model (B) - AingDiffusion (AingDiffusion_v9.2) ; la Multiplier (M) - 0.4 Weighted sum
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4-hq	ShivamMangale	2023-09-21T13:48:23Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T13:40:12Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4-hq 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-all_hi_weakdap_4th_iteration_d4-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.4580000000000001e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
nichonifroa/bert-finetuned-squad	nichonifroa	2023-09-21T13:47:34Z	122	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:nichonifroa/bert-finetuned-squad", "base_model:finetune:nichonifroa/bert-finetuned-squad", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T10:04:51Z	--- base_model: nichonifroa/bert-finetuned-squad tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [nichonifroa/bert-finetuned-squad](https://huggingface.co/nichonifroa/bert-finetuned-squad) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Ibrahim-Alam/finetuning-bert-base-uncased-on-Cornell_sentiment	Ibrahim-Alam	2023-09-21T13:44:05Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-21T13:41:53Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-bert-base-uncased-on-Cornell_sentiment 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. --> # finetuning-bert-base-uncased-on-Cornell_sentiment This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3582 - Accuracy: 0.8626 - F1: 0.8542 ## 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: 1 ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
BigR/Lunar_lander	BigR	2023-09-21T13:41:04Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T13:40:44Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.02 +/- 15.24 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 ... ```
fsuarez/autotrain-logo-identifier-90194144191	fsuarez	2023-09-21T13:38:51Z	184	0	transformers	[ "transformers", "pytorch", "safetensors", "swin", "image-classification", "autotrain", "vision", "dataset:fsuarez/autotrain-data-logo-identifier", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-19T14:59:47Z	--- tags: - autotrain - vision - image-classification datasets: - fsuarez/autotrain-data-logo-identifier widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.060824697813101125 --- # 📒 logo-identifier-model This model has been trained on a dataset called "LogoIdentifier" for multi-class classification of logos from 57 renowned brands and companies. These brands encompass a wide spectrum of industries and recognition, ranging from global giants like Coca-Cola, Coleman, Google, IBM, Nike, Pepsi, and many others. Each brand is thoughtfully organized into its designated subfolder, housing a comprehensive set of logo images for precise and accurate classification. Whether you're identifying iconic logos or exploring the branding diversity of these 57 famous names, this model is your go-to solution for logo recognition and classification. # 🧪 Dataset Content - The dataset includes logos from various brands and companies. - The dataset is organized into subfolders, each corresponding to a specific brand or company. - It contains a wide range of brand logos, including Acer, Acura, Adidas, Samsung, Lenovo, McDonald's, Java, and many more. - Each brand or company in the dataset is associated with a numerical value, likely representing the number of images available for that brand. The model has been trained to recognize and classify logos into their respective brand categories based on the images provided in the dataset. \| Company \| Quantity of images \| \| ----------------- \| ------------------ \| \| Acer \| 67 \| \| Acura \| 74 \| \| Addidas \| 90 \| \| Ades \| 36 \| \| Adio \| 63 \| \| Cadillac \| 69 \| \| CalvinKlein \| 65 \| \| Canon \| 59 \| \| Cocacola \| 40 \| \| CocaColaZero \| 91 \| \| Coleman \| 57 \| \| Converse \| 60 \| \| CornFlakes \| 62 \| \| DominossPizza \| 99 \| \| Excel \| 88 \| \| Gillette \| 86 \| \| GMC \| 75 \| \| Google \| 93 \| \| HardRockCafe \| 93 \| \| HBO \| 103 \| \| Heineken \| 84 \| \| HewlettPackard \| 81 \| \| Hp \| 87 \| \| Huawei \| 84 \| \| Hyundai \| 84 \| \| IBM \| 84 \| \| Java \| 62 \| \| KFC \| 84 \| \| Kia \| 76 \| \| Kingston \| 79 \| \| Lenovo \| 82 \| \| LG \| 95 \| \| Lipton \| 94 \| \| Mattel \| 77 \| \| McDonalds \| 98 \| \| MercedesBenz \| 94 \| \| Motorola \| 86 \| \| Nestle \| 94 \| \| Nickelodeon \| 74 \| \| Nike \| 50 \| \| Pennzoil \| 82 \| \| Pepsi \| 93 \| \| Peugeot \| 60 \| \| Porsche \| 71 \| \| Samsung \| 96 \| \| SchneiderElectric \| 42 \| \| Shell \| 58 \| To use this model for brand logo identification, you can make use of the Hugging Face Transformers library and load the model using its model ID (90194144191). You can then input an image of a brand logo, and the model should be able to predict the brand it belongs to based on its training. # 🤗 Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 90194144191 - CO2 Emissions (in grams): 0.0608 ## 📐 Validation Metrics - Loss: 0.300 - Accuracy: 0.924 - Macro F1: 0.924 - Micro F1: 0.924 - Weighted F1: 0.922 - Macro Precision: 0.930 - Micro Precision: 0.924 - Weighted Precision: 0.928 - Macro Recall: 0.924 - Micro Recall: 0.924 - Weighted Recall: 0.924
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0-hq	ShivamMangale	2023-09-21T13:37:37Z	106	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T13:15:08Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0-hq 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-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
LarryAIDraw/Char_Honkai_Raiden_Mei_adult	LarryAIDraw	2023-09-21T13:36:19Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-21T13:34:19Z	--- license: creativeml-openrail-m ---
sanctia/lora-sd-finesse	sanctia	2023-09-21T13:33:39Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-20T02:37:21Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - sanctia/lora-sd-finesse These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the sanctia/finesse-image-generation dataset. You can find some example images in the following. - Model and architecture details: https://www.notion.so/Design-document-Finesse-Generative-Challenge-4ed87ea624f84ff5a9ac09dc21885366 - Wandb report: https://wandb.ai/hpml3/text2image-fine-tune/runs/cdyy9un3?workspace=user-sanctia ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
srushtibhavsar/squad_bloom_3b	srushtibhavsar	2023-09-21T13:29:25Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-21T13:29:23Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
chanifrusydi/t5-dialogue-summarization	chanifrusydi	2023-09-21T13:27:14Z	134	2	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "summarization", "dataset:samsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-06-08T05:08:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - accuracy pipeline_tag: summarization base_model: t5-small model-index: - name: t5-dialogue-summarization results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-dialogue-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. dataset: type: {summarization} name: {samsum} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
CyberHarem/nishikawa_honami_idolmastercinderellagirls	CyberHarem	2023-09-21T13:13:40Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/nishikawa_honami_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-21T13:02:46Z	--- license: mit datasets: - CyberHarem/nishikawa_honami_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of nishikawa_honami_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4080, you need to download `4080/nishikawa_honami_idolmastercinderellagirls.pt` as the embedding and `4080/nishikawa_honami_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4080, with the score of 0.919. The trigger words are: 1. `nishikawa_honami_idolmastercinderellagirls` 2. `long_hair, brown_hair, green_eyes, earrings, jewelry, smile, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:--------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.903 \| [Download](5100/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.895 \| [Download](4760/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.912 \| [Download](4420/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.919 \| [Download](4080/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.831 \| [Download](3740/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.872 \| [Download](3400/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.882 \| [Download](3060/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.909 \| [Download](2720/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.857 \| [Download](2380/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.893 \| [Download](2040/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.874 \| [Download](1700/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.860 \| [Download](1360/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.877 \| [Download](1020/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.753 \| [Download](680/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.579 \| [Download](340/nishikawa_honami_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2	ShivamMangale	2023-09-21T13:12:54Z	123	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T09:29:28Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2 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-all_hi_weakdap_3rd_iteration_d3_d2 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.8e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3	ShivamMangale	2023-09-21T13:01:35Z	122	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T09:20:45Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3 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-all_hi_weakdap_3rd_iteration_d3 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.62e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
aminh/squad-falcon-7b	aminh	2023-09-21T12:57:14Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-21T12:57:06Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
MattStammers/appo-mujoco-doublependulum	MattStammers	2023-09-21T12:56:15Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T12:56:12Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: mujoco_doublependulum type: mujoco_doublependulum metrics: - type: mean_reward value: 6568.13 +/- 4264.02 name: mean_reward verified: false --- A(n) APPO model trained on the mujoco_doublependulum environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-mujoco-doublependulum ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_doublependulum --train_dir=./train_dir --experiment=appo-mujoco-doublependulum ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_doublependulum --train_dir=./train_dir --experiment=appo-mujoco-doublependulum --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
antphb/pretrain-gpt2-large	antphb	2023-09-21T12:53:55Z	132	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:NlpHUST/gpt2-vietnamese", "base_model:finetune:NlpHUST/gpt2-vietnamese", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-21T08:31:12Z	--- base_model: NlpHUST/gpt2-vietnamese tags: - generated_from_trainer model-index: - name: pretrain-gpt2-large 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. --> # pretrain-gpt2-large This model is a fine-tuned version of [NlpHUST/gpt2-vietnamese](https://huggingface.co/NlpHUST/gpt2-vietnamese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4155 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 70 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.0875 \| 13.05 \| 500 \| 2.6828 \| \| 2.5739 \| 26.1 \| 1000 \| 2.5363 \| \| 2.4573 \| 39.15 \| 1500 \| 2.4643 \| \| 2.3962 \| 52.2 \| 2000 \| 2.4294 \| \| 2.3662 \| 65.25 \| 2500 \| 2.4155 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
duwuonline/my-ielts	duwuonline	2023-09-21T12:49:48Z	105	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-21T12:20:17Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: my-ielts 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-ielts This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-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: 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: 100 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
BlahBlah1/LLama2	BlahBlah1	2023-09-21T12:45:20Z	2	0	null	[ "gguf", "license:apache-2.0", "region:us" ]	null	2023-09-21T12:10:06Z	--- license: apache-2.0 --- Openbuddy's conversion of GGML model to GGUF as per new AUG 21 update of llama.cpp supporting GGML files A quantised version of the model.
MThonar/Linkk	MThonar	2023-09-21T12:33:31Z	30	0	diffusers	[ "diffusers", "safetensors", "pytorch", "stable-diffusion", "text-to-image", "diffusion-models-class", "dreambooth", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-21T12:27:35Z	--- license: creativeml-openrail-m tags: - pytorch - diffusers - stable-diffusion - text-to-image - diffusion-models-class - dreambooth --- # DreamBooth model of Link trained by MThonar on the MThonar/link dataset. This is a Stable Diffusion model fine-tuned with Dreambooth on images of Linkk. It can be used by modifying the `instance_prompt`: a photo of Linkk ## Description This is a Stable Diffusion model fine-tuned on images of Linkk. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('MThonar/Linkk') image = pipeline().images[0] image ```
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1_d0	ShivamMangale	2023-09-21T12:32:06Z	122	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T08:46:47Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1_d0 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-all_hi_weakdap_2nd_iteration_d2_d1_d0 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
jphme/phi-1_5_Wizard_Vicuna_uncensored	jphme	2023-09-21T12:23:23Z	69	27	transformers	[ "transformers", "pytorch", "mixformer-sequential", "text-generation", "phi", "phi-1_5", "english", "custom_code", "en", "dataset:ehartford/wizard_vicuna_70k_unfiltered", "autotrain_compatible", "region:us" ]	text-generation	2023-09-12T17:30:57Z	--- language: - en library_name: transformers pipeline_tag: text-generation inference: true tags: - pytorch - phi - phi-1_5 - english datasets: - ehartford/wizard_vicuna_70k_unfiltered --- # Phi 1.5 Wizard Vicuna Experimental Experimental Finetune on Microsoft's [Phi 1.5](https://huggingface.co/microsoft/phi-1_5). This is highly experimental, only trained on a subset of the 70k Wizard Vicuna dataset and not meant for production use. This model also runs reasonably fast on CPU! Will update with later checkpoints later. # Prompt Format ShareGPT / Vicuna (without newlines): ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: This is a question? ASSISTANT: Here is my answer" ``` # Code Example ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("jphme/phi-1_5_wizzard_vicuna_uncensored", trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda") tokenizer = AutoTokenizer.from_pretrained("jphme/phi-1_5_wizzard_vicuna_uncensored", trust_remote_code=True, torch_dtype=torch.bfloat16) prompt_template=("A chat between a curious user and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the user's questions. " "USER: {prompt} ASSISTANT:") inputs = tokenizer( prompt_template.format(prompt="What is 1+1?"), return_tensors="pt", return_attention_mask=False).to('cuda') outputs = model.generate( **inputs, max_length=200, do_sample=True, temperature=0.5, top_p=0.95, use_cache=True, repetition_penalty=1.1, #can try to uncomment this if model doesn't end #eos_token_id=tokenizer.eos_token_id ) text = tokenizer.batch_decode(outputs,skip_special_tokens=True)[0] print(text) ``` The model doesn't stop by itself (this could be an issue due to eos/bos tokens being identical), so you can try to explicitly set the `eos_token_id` (see above) or use a custom stopping criteria. You can remove `.to("cuda")` to let the model run without a GPU! # Examples > A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Who was president of the USA in 1995? ASSISTANT: The president of the United States at that time was Bill Clinton (...) > A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Write a python function that returns the fibonacci sequence ASSISTANT:The Fibonacci sequence is a series of numbers where each number after the first two are obtained by adding the two preceding ones together (starting from 0). So the sequence goes 0, 1, 1, 2, 3, 5, 8, 13, 21... Here's a Python function that generates the nth term in the Fibonacci sequence: ```python def fibonacci(n): if n <= 1: return n else: f = [0,1] for i in range(2, n+1): f.append(f[-1]+f[-2]) return f[-1] ``` > This function takes as input `n`, which represents the (...) (this function works with corrected intendation) > A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: What is (500+12)/4? ASSISTANT:The answer to this expression is 7.5. (...) Some potential here ;-)... # Thanks Thanks to Microsoft for releaseing Phi 1.5, [Eric Hartford](https://huggingface.co/ehartford) for releasing the Wizard Vicuna dataset, [Winglian](https://huggingface.co/winglian) for his great work on Axolotl that I use for finetuning and [Teknium](https://huggingface.co/teknium) for some Phi finetuning discussion. # License The original licenses of the dataset and model applies. No warranty whatsoever, this model is only intended for research purposes.
bavolesy/Reinforce-Cartpole-v1	bavolesy	2023-09-21T12:19:12Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T12:18:58Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 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
Volkan/photography-upscalers	Volkan	2023-09-21T12:15:58Z	0	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2023-09-05T19:05:51Z	--- license: cc-by-nc-nd-4.0 ---
MattStammers/appo-mujoco-pendulum	MattStammers	2023-09-21T12:11:43Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T12:11:40Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: mujoco_pendulum type: mujoco_pendulum metrics: - type: mean_reward value: 1000.00 +/- 0.00 name: mean_reward verified: false --- A(n) APPO model trained on the mujoco_pendulum environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-mujoco-pendulum ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_pendulum --train_dir=./train_dir --experiment=appo-mujoco-pendulum ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_pendulum --train_dir=./train_dir --experiment=appo-mujoco-pendulum --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Rexe/Deci-Decicoder-1b-qlora-coder	Rexe	2023-09-21T12:07:27Z	3	0	peft	[ "peft", "base_model:Deci/DeciCoder-1b", "base_model:adapter:Deci/DeciCoder-1b", "region:us" ]	null	2023-09-19T01:30:55Z	--- library_name: peft base_model: Deci/DeciCoder-1b --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1	ShivamMangale	2023-09-21T12:00:23Z	133	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T08:29:28Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1 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-all_hi_weakdap_2nd_iteration_d2_d1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
MattStammers/appo-mujoco-walker	MattStammers	2023-09-21T11:43:35Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T17:00:51Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: ATQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: mujoco_walker type: mujoco_walker metrics: - type: mean_reward value: 3553.55 +/- 944.12 name: mean_reward verified: false --- A(n) ATQC model trained on the mujoco_walker environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-mujoco-swimmer ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=ATQC --env=mujoco_walker --train_dir=./train_dir --experiment=appo-mujoco-swimmer ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.mujoco.train_mujoco --algo=ATQC --env=mujoco_walker --train_dir=./train_dir --experiment=appo-mujoco-swimmer --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0-hq	ShivamMangale	2023-09-21T11:36:49Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T11:20:16Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0-hq 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-all_hi_weakdap_1st_iteration_d1_d0-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
Ori/lama-2-13b-peft-strategyqa-no-retrieval-1-v2-seed-3	Ori	2023-09-21T11:36:27Z	3	0	peft	[ "peft", "safetensors", "region:us" ]	null	2023-09-21T11:34:07Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
dss107/mp_base	dss107	2023-09-21T11:33:48Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-09-21T11:32:21Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # dss107/mp_base This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("dss107/mp_base") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
haseong8012/wav2vec2-large-xlsr-53_ko2	haseong8012	2023-09-21T11:30:32Z	105	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:zeroth_korean", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-20T11:27:30Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - zeroth_korean metrics: - wer model-index: - name: wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: zeroth_korean type: zeroth_korean config: clean split: test args: clean metrics: - name: Wer type: wer value: 0.9067911459117602 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the zeroth_korean dataset. It achieves the following results on the evaluation set: - Loss: 1.4929 - Wer: 0.9068 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.99 \| 38 \| 54.4059 \| 1.0 \| \| No log \| 2.0 \| 77 \| 38.8388 \| 1.0 \| \| No log \| 2.99 \| 115 \| 24.1740 \| 1.0 \| \| No log \| 4.0 \| 154 \| 16.4733 \| 1.0 \| \| No log \| 4.99 \| 192 \| 10.1900 \| 1.0 \| \| No log \| 6.0 \| 231 \| 6.0076 \| 1.0 \| \| No log \| 6.99 \| 269 \| 4.8990 \| 1.0 \| \| No log \| 8.0 \| 308 \| 4.8442 \| 1.0 \| \| No log \| 8.99 \| 346 \| 4.8284 \| 1.0 \| \| No log \| 10.0 \| 385 \| 4.8316 \| 1.0 \| \| 16.886 \| 10.99 \| 423 \| 4.8164 \| 1.0 \| \| 16.886 \| 12.0 \| 462 \| 4.7815 \| 1.0 \| \| 16.886 \| 12.99 \| 500 \| 4.7204 \| 0.9989 \| \| 16.886 \| 14.0 \| 539 \| 4.6842 \| 0.9989 \| \| 16.886 \| 14.99 \| 577 \| 4.6641 \| 0.9994 \| \| 16.886 \| 16.0 \| 616 \| 4.6527 \| 1.0 \| \| 16.886 \| 16.99 \| 654 \| 4.6745 \| 0.9992 \| \| 16.886 \| 18.0 \| 693 \| 4.6591 \| 1.0 \| \| 16.886 \| 18.99 \| 731 \| 4.6506 \| 0.9997 \| \| 16.886 \| 20.0 \| 770 \| 4.6719 \| 0.9967 \| \| 4.4391 \| 20.99 \| 808 \| 4.6067 \| 0.9968 \| \| 4.4391 \| 22.0 \| 847 \| 4.5748 \| 0.9968 \| \| 4.4391 \| 22.99 \| 885 \| 4.5166 \| 0.9962 \| \| 4.4391 \| 24.0 \| 924 \| 4.3783 \| 0.9926 \| \| 4.4391 \| 24.99 \| 962 \| 4.2711 \| 0.9913 \| \| 4.4391 \| 26.0 \| 1001 \| 3.6515 \| 1.0030 \| \| 4.4391 \| 26.99 \| 1039 \| 3.1057 \| 1.0640 \| \| 4.4391 \| 28.0 \| 1078 \| 2.6593 \| 1.0742 \| \| 4.4391 \| 28.99 \| 1116 \| 2.4071 \| 1.0587 \| \| 4.4391 \| 30.0 \| 1155 \| 2.2041 \| 1.0379 \| \| 4.4391 \| 30.99 \| 1193 \| 2.0495 \| 1.0319 \| \| 3.1722 \| 32.0 \| 1232 \| 1.9754 \| 1.0459 \| \| 3.1722 \| 32.99 \| 1270 \| 1.8658 \| 0.9968 \| \| 3.1722 \| 34.0 \| 1309 \| 1.7887 \| 0.9883 \| \| 3.1722 \| 34.99 \| 1347 \| 1.7560 \| 0.9776 \| \| 3.1722 \| 36.0 \| 1386 \| 1.6987 \| 0.9675 \| \| 3.1722 \| 36.99 \| 1424 \| 1.6513 \| 0.9443 \| \| 3.1722 \| 38.0 \| 1463 \| 1.6187 \| 0.9473 \| \| 3.1722 \| 38.99 \| 1501 \| 1.6210 \| 0.9408 \| \| 3.1722 \| 40.0 \| 1540 \| 1.5957 \| 0.9458 \| \| 3.1722 \| 40.99 \| 1578 \| 1.5673 \| 0.9246 \| \| 1.2364 \| 42.0 \| 1617 \| 1.5748 \| 0.9286 \| \| 1.2364 \| 42.99 \| 1655 \| 1.5333 \| 0.9217 \| \| 1.2364 \| 44.0 \| 1694 \| 1.5138 \| 0.9100 \| \| 1.2364 \| 44.99 \| 1732 \| 1.5244 \| 0.9223 \| \| 1.2364 \| 46.0 \| 1771 \| 1.5041 \| 0.9080 \| \| 1.2364 \| 46.99 \| 1809 \| 1.5151 \| 0.9155 \| \| 1.2364 \| 48.0 \| 1848 \| 1.4955 \| 0.9077 \| \| 1.2364 \| 48.99 \| 1886 \| 1.4924 \| 0.9065 \| \| 1.2364 \| 49.35 \| 1900 \| 1.4929 \| 0.9068 \| ### Framework versions - Transformers 4.33.2 - Pytorch 1.12.1 - Datasets 2.14.5 - Tokenizers 0.13.3
ashishpatel26/phi-1_5-finetuned-dialogstudio	ashishpatel26	2023-09-21T11:27:33Z	0	0	null	[ "generated_from_trainer", "dataset:dialogstudio", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "region:us" ]	null	2023-09-21T10:43:09Z	--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer datasets: - dialogstudio model-index: - name: phi-1_5-finetuned-dialogstudio 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. --> # phi-1_5-finetuned-dialogstudio This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the dialogstudio dataset. It achieves the following results on the evaluation set: - Loss: 3.2430 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 3 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
lora-library/zac	lora-library	2023-09-21T11:27:25Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stablediffusionapi/majicmixrealistic", "base_model:adapter:stablediffusionapi/majicmixrealistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-21T11:27:25Z	--- license: creativeml-openrail-m base_model: stablediffusionapi/majicmixrealistic instance_prompt: z4c tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - zac These are LoRA adaption weights for [stablediffusionapi/majicmixrealistic](https://huggingface.co/stablediffusionapi/majicmixrealistic). The weights were trained on the instance prompt "z4c" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
ldos/text_shortening_model_v47	ldos	2023-09-21T11:25:38Z	4	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-xsum", "base_model:finetune:facebook/bart-large-xsum", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-21T10:04:22Z	--- license: mit base_model: facebook/bart-large-xsum tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v47 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. --> # text_shortening_model_v47 This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.3912 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Bert precision: 0.6047 - Bert recall: 0.5681 - Average word count: 1.0 - Max word count: 1 - Min word count: 1 - Average token count: 12.0 - % shortened texts with length > 12: 0.0 ## 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: 0.005 - 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: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bert precision \| Bert recall \| Average word count \| Max word count \| Min word count \| Average token count \| % shortened texts with length > 12 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------------:\|:-----------:\|:------------------:\|:--------------:\|:--------------:\|:-------------------:\|:----------------------------------:\| \| 7.822 \| 1.0 \| 83 \| 7.4737 \| 0.0776 \| 0.0 \| 0.0775 \| 0.0776 \| 0.6348 \| 0.6223 \| 2.0 \| 2 \| 2 \| 13.0 \| 0.0 \| \| 3.2859 \| 2.0 \| 166 \| 6.6585 \| 0.1063 \| 0.0 \| 0.1063 \| 0.1063 \| 0.6469 \| 0.608 \| 5.0026 \| 6 \| 5 \| 12.0 \| 0.0 \| \| 3.0284 \| 3.0 \| 249 \| 6.4761 \| 0.116 \| 0.0 \| 0.116 \| 0.1161 \| 0.6479 \| 0.6388 \| 3.9974 \| 4 \| 3 \| 14.0 \| 0.0 \| \| 2.9681 \| 4.0 \| 332 \| 6.4592 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6071 \| 0.5723 \| 1.0 \| 1 \| 1 \| 12.0 \| 0.0 \| \| 2.9377 \| 5.0 \| 415 \| 6.4142 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6047 \| 0.5681 \| 1.0 \| 1 \| 1 \| 12.0 \| 0.0 \| \| 2.9168 \| 6.0 \| 498 \| 6.4049 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6049 \| 0.5685 \| 1.0 \| 1 \| 1 \| 12.0 \| 0.0 \| \| 2.8964 \| 7.0 \| 581 \| 6.3912 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.6047 \| 0.5681 \| 1.0 \| 1 \| 1 \| 12.0 \| 0.0 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
monsterapi/opt1.3B_codeinstruct	monsterapi	2023-09-21T11:23:26Z	0	0	peft	[ "peft", "facebook-opt-1.3b", "code", "instruct", "instruct-code", "code-alpaca", "alpaca-instruct", "alpaca", "opt-1.3b", "dataset:sahil2801/CodeAlpaca-20k", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]	null	2023-05-06T03:17:16Z	--- library_name: peft tags: - facebook-opt-1.3b - code - instruct - instruct-code - code-alpaca - alpaca-instruct - alpaca - opt-1.3b datasets: - sahil2801/CodeAlpaca-20k base_model: codellama/CodeLlama-7b-hf --- We finetuned Facebook/OPT-1.3B on Code-Alpaca-Instruct Dataset (sahil2801/CodeAlpaca-20k) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 1 hour and 30 minutes and costed us only `$6` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: facebook/opt-1.3b - Dataset: sahil2801/CodeAlpaca-20k - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---
monsterapi/opt125M_alpaca	monsterapi	2023-09-21T11:23:21Z	146	0	peft	[ "peft", "facebook/opt-125m", "code", "instruct", "alpaca-instruct", "alpaca", "dataset:tatsu-lab/alpaca", "base_model:facebook/opt-125m", "base_model:adapter:facebook/opt-125m", "region:us" ]	null	2023-05-13T05:38:51Z	--- library_name: peft tags: - facebook/opt-125m - code - instruct - alpaca-instruct - alpaca datasets: - tatsu-lab/alpaca base_model: facebook/opt-125m --- We finetuned facebook/opt-125m on tatsu-lab/alpaca Dataset for 10 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/tatsu-lab/alpaca unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 40 minutes and costed us only `$4` for the entire finetuning run! #### Hyperparameters & Run details: - Model: facebook/opt-125m - Dataset: tatsu-lab/alpaca - Learning rate: 0.0003 - Number of epochs: 10 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 - --- license: apache-2.0 ---
monsterapi/OpenPlatypus_LLAMA2_7b	monsterapi	2023-09-21T11:23:18Z	6	1	peft	[ "peft", "meta-llama/Llama-2-7b-hf", "code", "instruct", "instruct-code", "logical-reasoning", "Platypus2", "dataset:garage-bAInd/Open-Platypus", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2023-09-05T10:13:05Z	--- library_name: peft tags: - meta-llama/Llama-2-7b-hf - code - instruct - instruct-code - logical-reasoning - Platypus2 datasets: - garage-bAInd/Open-Platypus base_model: meta-llama/Llama-2-7b-hf --- We finetuned Meta-Llama/Llama-2-7b-hf on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). #### About OpenPlatypus Dataset OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0. The finetuning session got completed in 1 hour and 30 minutes and costed us only `$15` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: meta-llama/Llama-2-7b-hf - Dataset: garage-bAInd/Open-Platypus - Learning rate: 0.0002 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---
alexalbala/llam2test	alexalbala	2023-09-21T11:23:16Z	0	0	peft	[ "peft", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2023-09-21T08:49:01Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- ## Training procedure ### Framework versions - PEFT 0.5.0
monsterapi/OpenPlatypus_Falcon_7b	monsterapi	2023-09-21T11:23:15Z	2	0	peft	[ "peft", "tiiuae/falcon-7b", "code", "instruct", "instruct-code", "logical-reasoning", "Platypus2", "dataset:garage-bAInd/Open-Platypus", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]	null	2023-09-05T11:28:00Z	--- library_name: peft tags: - tiiuae/falcon-7b - code - instruct - instruct-code - logical-reasoning - Platypus2 datasets: - garage-bAInd/Open-Platypus base_model: codellama/CodeLlama-7b-hf --- We finetuned TIIUAE/Falcon-7B on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 3 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). #### About OpenPlatypus Dataset OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0. The finetuning session got completed in ~ 3 hrs and costed us only `$14` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: tiiuae/falcon-7b - Dataset: garage-bAInd/Open-Platypus - Learning rate: 0.0003 - Number of epochs: 3 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---

HusseinHE/icbinh

HusseinHE

2023-09-21T20:12:39Z

16

0

diffusers

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

text-to-image

2023-09-21T20:07:02Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### ICBINH Dreambooth model trained by HusseinHE 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:

fsuarez/autotrain-image-classification-86974143294

fsuarez

2023-09-21T19:47:43Z

194

0

transformers

[ "transformers", "pytorch", "safetensors", "swin", "image-classification", "autotrain", "vision", "dataset:fsuarez/autotrain-data-image-classification", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-04T14:31:09Z

--- tags: - autotrain - vision - image-classification datasets: - fsuarez/autotrain-data-image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 1.6524320573590656 --- ## 📒 image-classification-model This model has undergone training on the "image-classification" dataset, focusing on multi-class classification to categorize specific segments of websites. Each segment corresponds to one of six potential features, encompassing a broad spectrum of web elements, including: - **Button**: Identifying interactive buttons that users can click or tap on for various website functions. - **Textfield**: Recognizing text input fields where users can type or enter information. - **Checkbox**: Detecting checkboxes that users can select or deselect to make choices or indicate preferences. - **Radiobutton**: Identifying radio buttons that allow users to choose a single option from a list. - **Tables**: Recognizing tabular data structures that organize information in rows and columns. - **AppBar**: Detecting app bars or navigation bars typically found at the top of web pages, often containing menus, search bars, or branding elements. This extensive training equips the model with the ability to accurately classify these web elements. # 🧪 Dataset Content The dataset is structured to facilitate the analysis of website components. It includes various types of objects commonly found on websites, such as buttons, text fields, checkboxes, radio buttons, tables, and app bars. Each object type is organized into its respective category within the dataset, allowing for precise classification. | Web Element Category | Quantity of images | |----------------------|--------------------| | Button | 2934 | | Textfield | 100 | | Checkbox | 422 | | Radiobutton | 466 | | Tables | 100 | | AppBar | 100 | # 🤗 Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 86974143294 - CO2 Emissions (in grams): 1.6524 ## 📐 Validation Metrics - Loss: 0.079 - Accuracy: 0.983 - Macro F1: 0.967 - Micro F1: 0.983 - Weighted F1: 0.983 - Macro Precision: 0.971 - Micro Precision: 0.983 - Weighted Precision: 0.983 - Macro Recall: 0.964 - Micro Recall: 0.983 - Weighted Recall: 0.983

Bogdan63/distilbert-imdb

Bogdan63

2023-09-21T19:45:05Z

107

0

transformers

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

text-classification

2023-09-10T13:46:45Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-imdb results: [] ---  # distilbert-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:| | No log | 1.0 | 313 | 0.2279 | 0.9112 | 0.9112 | 0.9112 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Logeswaransr/T5_MineAI_Prototype

Logeswaransr

2023-09-21T19:43:09Z

103

1

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-21T19:37:52Z

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: results_T5 results: [] ---  # results_T5 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8755 - Rouge1: 0.2921 - Rouge2: 0.1519 - Rougel: 0.2857 - Rougelsum: 0.2866 ## 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: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 1.0 | 109 | 0.4682 | 0.2349 | 0.0878 | 0.2327 | 0.2344 | | No log | 2.0 | 218 | 0.4153 | 0.2519 | 0.0965 | 0.2481 | 0.2503 | | No log | 3.0 | 327 | 0.4102 | 0.3011 | 0.1465 | 0.2979 | 0.2990 | | No log | 4.0 | 436 | 0.4386 | 0.2555 | 0.1138 | 0.2496 | 0.2496 | | 0.8199 | 5.0 | 545 | 0.4784 | 0.2725 | 0.1188 | 0.2675 | 0.2665 | | 0.8199 | 6.0 | 654 | 0.5088 | 0.2524 | 0.1066 | 0.2497 | 0.2501 | | 0.8199 | 7.0 | 763 | 0.5680 | 0.2542 | 0.1093 | 0.2497 | 0.2496 | | 0.8199 | 8.0 | 872 | 0.5982 | 0.2740 | 0.1375 | 0.2694 | 0.2698 | | 0.8199 | 9.0 | 981 | 0.6575 | 0.2730 | 0.1368 | 0.2723 | 0.2714 | | 0.0653 | 10.0 | 1090 | 0.6753 | 0.2822 | 0.1519 | 0.2798 | 0.2781 | | 0.0653 | 11.0 | 1199 | 0.6923 | 0.2795 | 0.1486 | 0.2780 | 0.2774 | | 0.0653 | 12.0 | 1308 | 0.7350 | 0.2471 | 0.1209 | 0.2458 | 0.2457 | | 0.0653 | 13.0 | 1417 | 0.7698 | 0.2762 | 0.1463 | 0.2720 | 0.2733 | | 0.0225 | 14.0 | 1526 | 0.7867 | 0.2771 | 0.1372 | 0.2763 | 0.2755 | | 0.0225 | 15.0 | 1635 | 0.8166 | 0.3166 | 0.1689 | 0.3132 | 0.3133 | | 0.0225 | 16.0 | 1744 | 0.8085 | 0.3027 | 0.1572 | 0.2998 | 0.3009 | | 0.0225 | 17.0 | 1853 | 0.8162 | 0.3090 | 0.1734 | 0.3025 | 0.3038 | | 0.0225 | 18.0 | 1962 | 0.8484 | 0.2965 | 0.1627 | 0.2917 | 0.2909 | | 0.0105 | 19.0 | 2071 | 0.8610 | 0.2881 | 0.1487 | 0.2813 | 0.2819 | | 0.0105 | 20.0 | 2180 | 0.8688 | 0.2811 | 0.1494 | 0.2755 | 0.2770 | | 0.0105 | 21.0 | 2289 | 0.8733 | 0.2777 | 0.1453 | 0.2708 | 0.2724 | | 0.0105 | 22.0 | 2398 | 0.8776 | 0.2771 | 0.1475 | 0.2709 | 0.2711 | | 0.0061 | 23.0 | 2507 | 0.8717 | 0.2829 | 0.1467 | 0.2749 | 0.2749 | | 0.0061 | 24.0 | 2616 | 0.8729 | 0.2878 | 0.1467 | 0.2803 | 0.2806 | | 0.0061 | 25.0 | 2725 | 0.8755 | 0.2921 | 0.1519 | 0.2857 | 0.2866 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

jgeselowitz/poem_labeler

jgeselowitz

2023-09-21T19:43:00Z

105

0

transformers

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

text-classification

2023-09-21T19:24:46Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: poem_labeler results: [] ---  # poem_labeler 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: 1.1318 - Accuracy: 0.7315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0602 | 1.0 | 2382 | 0.9264 | 0.6935 | | 0.5889 | 2.0 | 4764 | 0.9186 | 0.723 | | 0.2638 | 3.0 | 7146 | 1.1318 | 0.7315 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

glaiveai/glaive-coder-7b

glaiveai

2023-09-21T19:35:50Z

1,565

54

transformers

[ "transformers", "pytorch", "llama", "text-generation", "code", "en", "dataset:glaiveai/glaive-code-assistant", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-17T14:49:44Z

--- license: llama2 datasets: - glaiveai/glaive-code-assistant language: - en tags: - code --- # Glaive-coder-7b Glaive-coder-7b is a 7B parameter code model trained on a dataset of ~140k programming related problems and solutions generated from Glaive’s synthetic data generation platform. The model is fine-tuned on the CodeLlama-7b model. ## Usage: The model is trained to act as a code assistant, and can do both single instruction following and multi-turn conversations. It follows the same prompt format as CodeLlama-7b-Instruct- ``` <s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_msg }} [/INST] {{ model_answer }} </s> <s>[INST] {{ user_msg }} [/INST] ``` You can run the model in the following way- ```python from transformers import AutoModelForCausalLM , AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("glaiveai/glaive-coder-7b") model = AutoModelForCausalLM.from_pretrained("glaiveai/glaive-coder-7b").half().cuda() def fmt_prompt(prompt): return f"<s> [INST] {prompt} [/INST]" inputs = tokenizer(fmt_prompt(prompt),return_tensors="pt").to(model.device) outputs = model.generate(**inputs,do_sample=True,temperature=0.1,top_p=0.95,max_new_tokens=100) print(tokenizer.decode(outputs[0],skip_special_tokens=True,clean_up_tokenization_spaces=False)) ``` ## Benchmarks: The model achieves a 63.1% pass@1 on HumanEval and a 45.2% pass@1 on MBPP, however it is evident that these benchmarks are not representative of real-world usage of code models so we are launching the [Code Models Arena](https://arena.glaive.ai/) to let users vote on model outputs so we can have a better understanding of user preference on code models and come up with new and better benchmarks. We plan to release the Arena results as soon as we have a sufficient amount of data. Join the Glaive [discord](https://discord.gg/fjQ4uf3yWD) for improvement suggestions, bug-reports and collaborating on more open-source projects.

mychen76/donut-receipt_v3

mychen76

2023-09-21T19:29:49Z

66

0

transformers

[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-09-20T17:01:53Z

--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut-receipt_v3 results: [] ---  # donut-receipt_v3 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3

Tanor/SRGPTSENTPOS6

Tanor

2023-09-21T19:28:12Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-classification", "generated_from_trainer", "base_model:Tanor/SRGPTSENTPOS6", "base_model:finetune:Tanor/SRGPTSENTPOS6", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2023-09-07T17:55:14Z

--- base_model: Tanor/SRGPTSENTPOS6 tags: - generated_from_trainer metrics: - f1 model-index: - name: SRGPTSENTPOS6 results: [] ---  # SRGPTSENTPOS6 This model is a fine-tuned version of [Tanor/SRGPTSENTPOS6](https://huggingface.co/Tanor/SRGPTSENTPOS6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3376 - F1: 0.1538 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 32 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0145 | 1.0 | 2706 | 0.4809 | 0.2526 | | 0.0052 | 2.0 | 5412 | 0.3511 | 0.1562 | | 0.0136 | 3.0 | 8118 | 0.3620 | 0.2222 | | 0.0028 | 4.0 | 10824 | 0.3376 | 0.1538 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.1.0.dev20230801 - Datasets 2.14.2 - Tokenizers 0.13.3

noelsinghsr/sagemaker-distilbert-emotion

noelsinghsr

2023-09-21T19:26:05Z

103

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

2023-09-21T19:24:14Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: sagemaker-distilbert-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: test args: split metrics: - name: Accuracy type: accuracy value: 0.9125 ---  # sagemaker-distilbert-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: 0.2634 - Accuracy: 0.9125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9347 | 1.0 | 500 | 0.2634 | 0.9125 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.13.3

NousResearch/Llama-2-70b-chat-hf

NousResearch

2023-09-21T19:05:17Z

625

19

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "facebook", "meta", "llama-2", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-07-19T04:36:22Z

--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "**Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved.**" extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

mirfan899/uner-bert-ner

mirfan899

2023-09-21T18:53:09Z

115

0

transformers

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

token-classification

2023-09-21T18:52:35Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: uner-bert-ner results: [] ---  # uner-bert-ner 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: 0.1354 - Precision: 0.8267 - Recall: 0.8707 - F1: 0.8481 - Accuracy: 0.9640 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 144 | 0.1496 | 0.7687 | 0.7971 | 0.7826 | 0.9533 | | No log | 2.0 | 288 | 0.1429 | 0.7719 | 0.8584 | 0.8129 | 0.9573 | | No log | 3.0 | 432 | 0.1267 | 0.8014 | 0.8682 | 0.8335 | 0.9629 | | 0.1628 | 4.0 | 576 | 0.1316 | 0.8206 | 0.8723 | 0.8457 | 0.9644 | | 0.1628 | 5.0 | 720 | 0.1354 | 0.8267 | 0.8707 | 0.8481 | 0.9640 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3

mirfan899/uner-roberta-ner

mirfan899

2023-09-21T18:42:07Z

103

1

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-09-21T18:40:21Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: uner-roberta-ner results: [] ---  # uner-roberta-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0930 - Precision: 0.8622 - Recall: 0.9010 - F1: 0.8812 - Accuracy: 0.9728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 144 | 0.1285 | 0.8005 | 0.8241 | 0.8121 | 0.9589 | | No log | 2.0 | 288 | 0.1142 | 0.8142 | 0.8748 | 0.8434 | 0.9655 | | No log | 3.0 | 432 | 0.0962 | 0.8485 | 0.8985 | 0.8728 | 0.9702 | | 0.1923 | 4.0 | 576 | 0.0916 | 0.8543 | 0.9018 | 0.8774 | 0.9719 | | 0.1923 | 5.0 | 720 | 0.0930 | 0.8622 | 0.9010 | 0.8812 | 0.9728 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3

Tostino/Inkbot-13b-4k

Tostino

2023-09-21T18:36:48Z

9

10

transformers

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

text-generation

2023-09-21T05:56:48Z

# Model Card for Inkbot ## Model Details Inkbot is a conversational AI model designed to interpret and respond to structured prompts with or without contextual information. Built on the latest advancements in natural language processing (NLP) and understanding (NLU), Inkbot provides users with accurate and meaningful interactions, addressing a wide range of queries and topics. Its unique feature lies in the structured prompt system, allowing users to engage in dynamic dialogues that can evolve based on context, metadata, and user input. ### Performance - The model excels in RAG type queries, answering from context, and overriding memory when available. - It can handle very large contexts, but may sometimes enter a repeating text loop, especially during complex tasks. - The model is intended to be more functional and less chatty, avoiding the waste of tokens on superfluous language. ## How to Use Inkbot uses a structured prompt template system. ### Prompt Template Structure #### With Context: ``` <#meta#> - Date: [DATE] - Task: [TASK TYPE] <#system#> [SYSTEM PROMPT/DESCRIPTION OF THE INTERACTION] <#chat#> <#user#> [USER QUESTION] <#bot#> [BOT RESPONSE] <#user#> [USER FOLLOW-UP QUESTION] <#user_context#> [ADDITIONAL CONTEXT FOR THE BOT] <#bot#> [BOT RESPONSE BASED ON CONTEXT] ``` #### Without Context: ``` <#meta#> - Date: [DATE] - Task: [TASK TYPE] <#system#> [SYSTEM PROMPT/DESCRIPTION OF THE INTERACTION] <#chat#> <#user#> [USER QUESTION] <#bot#> [BOT RESPONSE] <#user#> [USER FOLLOW-UP QUESTION] <#bot#> [BOT RESPONSE] ``` ### Examples #### With Context: ``` <#meta#> - Date: 2023-07-20 - Task: general <#system#> A dialogue focused on determining dates based on context clues. <#chat#> <#user#> What is today's date? <#bot#> The date is: 2023-07-20 <#user#> When did the event in the provided context take place? <#user_context#> It's the day after new years eve, and I'm starting 2018 with a headache. <#bot#> The event in the context took place on: 2018-01-01 ``` #### Without Context: ``` <#meta#> - Date: 2023-07-20 - Task: general <#system#> A simple dialogue about daily queries. <#chat#> <#user#> Will it rain today? <#bot#> I am not sure, without knowing your location I am unable to determine the weather. <#user#> Can you compose a haiku for me? <#bot#> Whispers of the breeze, Nature's melodies unfold, Dawn dispels the night. ``` ## Task Options Inkbot has been trained for a variety of tasks. Below are some of the key task options you can utilize, but feel free to try others to test the effect: 1. **general**: This is the default task and is designed for a broad range of general questions and interactions. - Usage: Suitable for most day-to-day interactions and queries. 2. **knowledge_graph**: This task involves extracting, understanding, and representing information in a structured way. - Usage: When you want to extract relationships between entities or desire structured representations of data. 3. **question_answer**: Explicitly trained for answering questions in a straightforward manner. - Usage: Best used when you have direct questions and expect concise answers. 4. **reasoning**: Allows Inkbot to showcase its logical and deductive reasoning capabilities. - Usage: Ideal for puzzles, riddles, or scenarios where logical analysis is required. 5. **translation**: Use this for language translation tasks. - Usage: Provide a sentence or paragraph in one language, and specify the desired target language for translation. 6. **summarization**: Trained for condensing large texts into shorter, coherent summaries. - Usage: When you have a lengthy text or article that you want to be summarized to its key points. 7. **creative_writing**: Engage Inkbot in composing stories, poetry, and other creative content. - Usage: For tasks that require imaginative and original content generation. ## Limitations - Adhere to the prompt structure for best results. - When providing contextual details, clarity is essential for Inkbot to derive accurate and meaningful responses. - The overriding memory from user_context property generally only works for the next prompt or two, after which the model reverts to its original behavior. - On complex tasks, like creating a coherent story based on a set of facts from context, there's a potential for a repeating text loop as context fills. - Sometimes the model doesn't know when to end a knowledge graph, which can result in adding nodes and edges until it runs out of context. ## Additional Notes - The 'date', 'task', and 'system' are crucial metadata components that need to be provided outside the core dialogue. - Use the 'user_context' when you want to offer supplementary context that guides Inkbot's response. You can interleave it in the chat log as necessary. - The specific tag format, such as `<#word#>`, is used to because there are filters in a lot of APIs for <|word|> and this makes interactions easier. --- license: llama2 ---

dancingninjas/sentiment-model

dancingninjas

2023-09-21T18:24:24Z

68

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "text classification", "transformer", "sentiment analysis", "TensorFlow", "en", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-21T10:54:56Z

--- license: apache-2.0 datasets: - imdb language: - en pipeline_tag: text-classification tags: - text classification - transformer - sentiment analysis - distilbert - TensorFlow ---

sudhanvasp/Sentiment-Analysis

sudhanvasp

2023-09-21T18:19:27Z

164

2

transformers

[ "transformers", "pytorch", "tf", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-20T13:47:15Z

# SentimentAnalysis Sentiment analysis with NLTK (Folder 79) Sentiment analysis with Roberta (Folder 159) Sentiment analysis with Roberta+Awk (Folder 209) Sentiment analysis with Roberta+Gradio (Folder 219)  # Stock Sentiment Analysis of Tweets using RoBERTa ![Feature 1 Screenshot](Media/1.jpeg) ![Feature 1 Screenshot](Media/2.jpeg) ![Feature 1 Screenshot](Media/3.png) ![Feature 1 Screenshot](Media/4.png) ![Feature 1 Screenshot](Media/5.png) ## Table of Contents - [Project Description](#project-description) - [Objective](#objective) - [Hypotheses](#hypotheses) - [Data Collection](#data-collection) - [Sentiment Analysis](#sentiment-analysis) - [Machine Learning Model](#machine-learning-model) - [Running the Model](#running-the-model) - [Huggingface](https://huggingface.co/sudhanvasp/Sentiment-Analysis) - [Results and Insights](#results-and-insights) - [License](#license) ---  ## Project Description Welcome to the Stock Sentiment Analysis project! This repository houses the code and resources for analyzing Twitter data to predict stock price movements based on sentiment analysis, leveraging the powerful RoBERTa model. Gain valuable insights into market sentiment and enhance your trading strategies.  ## Objective The primary aim of this project is to explore the intricate relationship between sentiment expressed in tweets and short-term stock price movements.  ## Hypotheses - *Hypothesis 1:* Tweets with a positive sentiment will exhibit a positive correlation with stock price increases. - *Hypothesis 2:* Tweets with a negative sentiment will display a negative correlation with stock price decreases. - *Hypothesis 3:* Tweets with a neutral sentiment will display a neutral correlation with stock price.  ## Data Collection - We meticulously gathered Twitter data from financial news and analyst accounts. - Data preprocessing was performed, encompassing deduplication, tokenization, and sentiment label encoding (positive, negative, neutral).  ## Sentiment Analysis - Harnessing RoBERTa, a state-of-the-art transformer-based model, we assigned sentiment scores. - Challenges such as domain-specific sentiment expressions and model fine-tuning were addressed.  ## Machine Learning Model - Our model is a robust ensemble of RoBERTa. - Features encompass RoBERTa-generated F1 scores, tweet volume, and historical stock price data. - This amalgamation empowers us to capture both sequential dependencies and non-linear relationships effectively.  ## Running the Model ## Hosting with Gradio 1. *Install Gradio:* ```bash pip install gradio import gradio as gr 2. Run the given gradio code in the Folder 219. ## Hosting with FLASK 1. *Install FLASK:* ```bash pip install flask cd 209 cd twitterka python app.py 2. Open the IP given address.  ## Huggingface Page - Execution of the model can be done directly on Huggingface as well - [Huggingface](https://huggingface.co/sudhanvasp/Sentiment-Analysis)  ## Results and Insights - Our ensemble model boasts an impressive 96% accuracy in sentiment analysis. - Notably, positive sentiment tweets correlate positively with stock price increases, while negative sentiment tweets correlate negatively with decreases. Neutral sentiment, while present, exhibits a weaker influence on stock price movements.  ## License - This created by the team "The Lost Pendrive" (Sudhanva SP, Deepa Umesh, Chinmayi Rajaram)

cloudwalkerw/wavlm-base_3

cloudwalkerw

2023-09-21T17:48:37Z

16

0

transformers

[ "transformers", "pytorch", "wavlm", "audio-classification", "generated_from_trainer", "base_model:microsoft/wavlm-base", "base_model:finetune:microsoft/wavlm-base", "endpoints_compatible", "region:us" ]

audio-classification

2023-09-18T14:31:32Z

--- base_model: microsoft/wavlm-base tags: - audio-classification - generated_from_trainer metrics: - accuracy model-index: - name: wavlm-base_3 results: [] ---  # wavlm-base_3 This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6534 - Accuracy: 0.8974 ## 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: 2 - seed: 0 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2236 | 1.24 | 100 | 12.8495 | 0.4467 | | 0.0514 | 2.48 | 200 | 16.3078 | 0.2677 | | 0.0 | 3.72 | 300 | 17.5651 | 0.2597 | | 0.3252 | 4.95 | 400 | 15.0382 | 0.1912 | | 1.0577 | 6.19 | 500 | 0.6534 | 0.8974 | | 0.6973 | 7.43 | 600 | 0.7352 | 0.1026 | | 0.6939 | 8.67 | 700 | 0.6210 | 0.8974 | | 0.6944 | 9.91 | 800 | 0.7129 | 0.1026 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.0.post302 - Datasets 2.14.5 - Tokenizers 0.13.3

darxkies/bart-large-cnn-samsum-ChatGPT_v3

darxkies

2023-09-21T17:47:31Z

10

1

transformers

[ "transformers", "rust", "bart", "text2text-generation", "summarization", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-09-21T15:44:26Z

--- pipeline_tag: summarization --- Original model: [Qiliang/bart-large-cnn-samsum-ChatGPT_v3](https://https://huggingface.co/Qiliang/bart-large-cnn-samsum-ChatGPT_v3) Added files for [rust-bert](https://github.com/guillaume-be/rust-bert)

TuningAI/DETR-BASE_Marine

TuningAI

2023-09-21T17:47:20Z

167

1

transformers

[ "transformers", "pytorch", "detr", "object-detection", "climate", "ar", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-09-21T08:29:14Z

--- license: apache-2.0 language: - ar - en library_name: transformers pipeline_tag: object-detection tags: - climate --- # DETR-BASE_Marine ## Overview + Model Name: DETR-BASE_Marine + Model Architecture: DETR (End-to-End Object Detection) with ResNet-50 backbone. + Model Type: Object Detection + Framework: PyTorch + Dataset: Aerial Maritime Image Dataset + License: MIT License (for the dataset) ## Model Description The DETR-BASE_Marine Aerial Maritime Detector is a deep learning model based on the DETR architecture with a ResNet-50 backbone. It has been fine-tuned on the "Aerial Maritime Image Dataset," which comprises 74 aerial photographs captured via a Mavic Air 2 drone. The model is designed for object detection tasks in maritime environments and can identify and locate various objects such as docks, boats, lifts, jetskis, and cars in aerial images. ## Key Features: + Multi-class object detection. + Object classes: Docks, Boats, Lifts, Jetskis, Cars. + Robust performance in aerial and maritime scenarios. ## Use Cases + **Boat Counting**: Count the number of boats on water bodies, such as lakes, using drone imagery. + **Boat Lift Detection**: Identify the presence of boat lifts on the waterfront via aerial surveillance. + **Car Detection**: Detect and locate cars within maritime regions using UAV drones. + **Habitability Assessment**: Determine the level of inhabitation around lakes and water bodies based on detected objects. + **Property Monitoring**: Identify if visitors or activities are present at lake houses or properties using drone surveillance. + **Proof of Concept**: Showcase the potential of UAV imagery for maritime projects and object detection tasks. ## Dataset + **Dataset Name**: Aerial Maritime Image Dataset + **Number of Images**: 74 + **Number of Bounding Boxes**: 1,151 + **Collection Method**: Captured via Mavic Air 2 drone at 400 ft altitude. ## Usage ``` python from transformers import DetrImageProcessor, DetrForObjectDetection import torch from PIL import Image img_path = "" image = Image.open(img_path) extractor = AutoFeatureExtractor.from_pretrained("TuningAI/DETR-BASE_Marine") model = AutoModelForObjectDetection.from_pretrained("TuningAI/DETR-BASE_Marine") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.9 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` ## License This model is provided under the MIT License. The Aerial Maritime Image Dataset used for fine-tuning is also under the MIT License.

darxkies/bge-base-en-v1.5

darxkies

2023-09-21T17:47:07Z

2

0

transformers

[ "transformers", "rust", "bert", "feature-extraction", "sentence-similarity", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-09-20T21:36:50Z

--- pipeline_tag: sentence-similarity --- Original model: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) Added files for [rust-bert](https://github.com/guillaume-be/rust-bert)

megagonlabs/transformers-ud-japanese-electra-base-ginza-520

megagonlabs

2023-09-21T17:45:45Z

125

3

transformers

[ "transformers", "pytorch", "electra", "feature-extraction", "PyTorch", "Transformers", "spaCy", "ELECTRA", "GiNZA", "mC4", "UD_Japanese-BCCWJ", "GSK2014-A", "ja", "MIT", "arxiv:1910.10683", "license:mit", "endpoints_compatible", "region:us" ]

feature-extraction

2023-09-21T14:14:04Z

--- language: - ja thumbnail: "https://raw.githubusercontent.com/megagonlabs/ginza/static/docs/images/GiNZA_logo_4c_s.png" tags: - PyTorch - Transformers - spaCy - ELECTRA - GiNZA - mC4 - UD_Japanese-BCCWJ - GSK2014-A - ja - MIT license: "mit" datasets: - mC4 - UD_Japanese_BCCWJ-r2.8 - GSK2014-A(2019) metrics: - UAS - LAS - UPOS --- # transformers-ud-japanese-electra-ginza-520 (sudachitra-wordpiece, mC4 Japanese) This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences extracted from the [mC4](https://huggingface.co/datasets/mc4) and finetuned by [spaCy v3](https://spacy.io/usage/v3) on [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html). The base pretrain model is [megagonlabs/transformers-ud-japanese-electra-base-discrimininator](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator). The entire spaCy v3 model is distributed as a python package named [`ja_ginza_electra`](https://pypi.org/project/ja-ginza-electra/) from PyPI along with [`GiNZA v5`](https://github.com/megagonlabs/ginza) which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures. Try running it as below: ```console $ pip install ginza ja_ginza_electra $ ginza ``` ## Licenses The models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php). ## Acknowledgments This model is permitted to be published under the `MIT License` under a joint research agreement between NINJAL (National Institute for Japanese Language and Linguistics) and Megagon Labs Tokyo. ## Citations - [mC4](https://huggingface.co/datasets/mc4) Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` - [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html) ``` Asahara, M., Kanayama, H., Tanaka, T., Miyao, Y., Uematsu, S., Mori, S., Matsumoto, Y., Omura, M., & Murawaki, Y. (2018). Universal Dependencies Version 2 for Japanese. In LREC-2018. ``` - [GSK2014-A(2019)](https://www.gsk.or.jp/catalog/gsk2014-a/)

kmposkid1/Horse-Health-Outcome-6d56348a-8c02-416a-8d5d-fdf98b0d4f59

kmposkid1

2023-09-21T17:38:09Z

0

sklearn

[ "sklearn", "skops", "tabular-classification", "license:mit", "region:us" ]

tabular-classification

2023-09-20T13:45:41Z

--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: LightGBM_without_hospital_number_01.pkl widget: structuredData: abdomen: - distend_small - distend_small - distend_large abdominal_distention: - none - none - moderate abdomo_appearance: - serosanguious - cloudy - serosanguious abdomo_protein: - 4.1 - 4.3 - 2.0 age: - adult - adult - adult capillary_refill_time: - less_3_sec - less_3_sec - more_3_sec cp_data: - 'yes' - 'yes' - 'no' lesion_1: - 7209 - 2112 - 5400 lesion_2: - 0 - 0 - 0 lesion_3: - 0 - 0 - 0 mucous_membrane: - bright_pink - bright_pink - dark_cyanotic nasogastric_reflux: - none - none - more_1_liter nasogastric_reflux_ph: - 7.0 - 3.5 - 2.0 nasogastric_tube: - slight - none - significant packed_cell_volume: - 37.0 - 44.0 - 65.0 pain: - depressed - mild_pain - extreme_pain peripheral_pulse: - normal - normal - reduced peristalsis: - hypermotile - hypomotile - absent pulse: - 84.0 - 66.0 - 72.0 rectal_exam_feces: - absent - decreased - absent rectal_temp: - 39.0 - 38.5 - 37.3 respiratory_rate: - 24.0 - 21.0 - 30.0 surgery: - 'yes' - 'yes' - 'yes' surgical_lesion: - 'yes' - 'yes' - 'yes' temp_of_extremities: - cool - normal - cool total_protein: - 6.5 - 7.6 - 13.0 --- # Model description This is a `LightGBM` model trained on horse health outcome data from Kaggle. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure [More Information Needed] ### Hyperparameters <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('preprocessor', ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])])), ('classifier', LGBMClassifier(max_depth=3))] | | verbose | False | | preprocessor | ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])]) | | classifier | LGBMClassifier(max_depth=3) | | preprocessor__n_jobs | | | preprocessor__remainder | passthrough | | preprocessor__sparse_threshold | 0.3 | | preprocessor__transformer_weights | | | preprocessor__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]), ['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']), ('cat', Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data'])] | | preprocessor__verbose | False | | preprocessor__verbose_feature_names_out | True | | preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]) | | preprocessor__cat | Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]) | | preprocessor__num__memory | | | preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())] | | preprocessor__num__verbose | False | | preprocessor__num__imputer | SimpleImputer(strategy='median') | | preprocessor__num__scaler | StandardScaler() | | preprocessor__num__imputer__add_indicator | False | | preprocessor__num__imputer__copy | True | | preprocessor__num__imputer__fill_value | | | preprocessor__num__imputer__keep_empty_features | False | | preprocessor__num__imputer__missing_values | nan | | preprocessor__num__imputer__strategy | median | | preprocessor__num__scaler__copy | True | | preprocessor__num__scaler__with_mean | True | | preprocessor__num__scaler__with_std | True | | preprocessor__cat__memory | | | preprocessor__cat__steps | [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))] | | preprocessor__cat__verbose | False | | preprocessor__cat__imputer | SimpleImputer(fill_value='missing', strategy='constant') | | preprocessor__cat__onehot | OneHotEncoder(handle_unknown='ignore') | | preprocessor__cat__imputer__add_indicator | False | | preprocessor__cat__imputer__copy | True | | preprocessor__cat__imputer__fill_value | missing | | preprocessor__cat__imputer__keep_empty_features | False | | preprocessor__cat__imputer__missing_values | nan | | preprocessor__cat__imputer__strategy | constant | | preprocessor__cat__onehot__categories | auto | | preprocessor__cat__onehot__drop | | | preprocessor__cat__onehot__dtype | <class 'numpy.float64'> | | preprocessor__cat__onehot__feature_name_combiner | concat | | preprocessor__cat__onehot__handle_unknown | ignore | | preprocessor__cat__onehot__max_categories | | | preprocessor__cat__onehot__min_frequency | | | preprocessor__cat__onehot__sparse | deprecated | | preprocessor__cat__onehot__sparse_output | True | | classifier__boosting_type | gbdt | | classifier__class_weight | | | classifier__colsample_bytree | 1.0 | | classifier__importance_type | split | | classifier__learning_rate | 0.1 | | classifier__max_depth | 3 | | classifier__min_child_samples | 20 | | classifier__min_child_weight | 0.001 | | classifier__min_split_gain | 0.0 | | classifier__n_estimators | 100 | | classifier__n_jobs | | | classifier__num_leaves | 31 | | classifier__objective | | | classifier__random_state | | | classifier__reg_alpha | 0.0 | | classifier__reg_lambda | 0.0 | | classifier__subsample | 1.0 | | classifier__subsample_for_bin | 200000 | | classifier__subsample_freq | 0 | </details> ### Model Plot <style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 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input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier', LGBMClassifier(max_depth=3))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier', LGBMClassifier(max_depth=3))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler())]),['rectal_temp', 'pulse', 'respiratory_rate','nasogastric_reflux_ph', 'packed_cell_volume','total_protein', 'abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pipeline(steps=[('imputer',SimpleI...='missing',strategy='constant')),('onehot',OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age', 'temp_of_extremities','peripheral_pulse', 'mucous_membrane','capillary_refill_time', 'pain','peristalsis', 'abdominal_distention','nasogastric_tube', 'nasogastric_reflux','rectal_exam_feces', 'abdomen','abdomo_appearance', 'surgical_lesion','cp_data'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-30" type="checkbox" ><label for="sk-estimator-id-30" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-31" type="checkbox" ><label for="sk-estimator-id-31" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-32" type="checkbox" ><label for="sk-estimator-id-32" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-33" type="checkbox" ><label for="sk-estimator-id-33" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMClassifier</label><div class="sk-toggleable__content"><pre>LGBMClassifier(max_depth=3)</pre></div></div></div></div></div></div></div> ## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.740891 | | f1 score | 0.740891 | ### Confusion Matrix ![Confusion Matrix](confusion_matrix.png) ## Permutation Importance ![Permutation Importance](feature_importance.png) # How to Get Started with the Model [More Information Needed] # Model Card Authors kmposkid # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ```

benedikt-schaber/q-Taxi-v3

benedikt-schaber

2023-09-21T17:35:46Z

0

null

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

reinforcement-learning

2023-09-21T17:35:45Z

--- 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: 7.56 +/- 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="benedikt-schaber/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"]) ```

AmirH98/Q-Taxi-V3

AmirH98

2023-09-21T17:35:01Z

0

null

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

reinforcement-learning

2023-09-21T17:35:00Z

--- 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: 7.56 +/- 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="AmirH98/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"]) ```

steveice/videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen

steveice

2023-09-21T17:13:55Z

8

0

transformers

[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-large-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-large-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-09-20T21:16:12Z

--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-large-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen results: [] ---  # videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6309 - Accuracy: 0.8900 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 11100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.5158 | 0.02 | 222 | 3.6067 | 0.0588 | | 2.8571 | 1.02 | 444 | 3.1445 | 0.3014 | | 1.8854 | 2.02 | 666 | 2.3644 | 0.4607 | | 1.5533 | 3.02 | 888 | 1.7967 | 0.5621 | | 1.3935 | 4.02 | 1110 | 1.3755 | 0.6502 | | 1.1722 | 5.02 | 1332 | 1.2232 | 0.7109 | | 0.2896 | 6.02 | 1554 | 1.2859 | 0.6256 | | 0.3166 | 7.02 | 1776 | 1.2910 | 0.6720 | | 0.6902 | 8.02 | 1998 | 1.2702 | 0.6995 | | 0.4193 | 9.02 | 2220 | 1.2087 | 0.7137 | | 0.1889 | 10.02 | 2442 | 1.0500 | 0.7611 | | 0.4502 | 11.02 | 2664 | 1.1647 | 0.7118 | | 0.7703 | 12.02 | 2886 | 1.1037 | 0.7242 | | 0.0957 | 13.02 | 3108 | 1.0967 | 0.7706 | | 0.3202 | 14.02 | 3330 | 1.0479 | 0.7545 | | 0.3634 | 15.02 | 3552 | 1.0714 | 0.8057 | | 0.3883 | 16.02 | 3774 | 1.2323 | 0.7498 | | 0.0322 | 17.02 | 3996 | 1.0504 | 0.7848 | | 0.5108 | 18.02 | 4218 | 1.1356 | 0.7915 | | 0.309 | 19.02 | 4440 | 1.1409 | 0.7592 | | 0.56 | 20.02 | 4662 | 1.0828 | 0.7915 | | 0.3675 | 21.02 | 4884 | 0.9154 | 0.8123 | | 0.0076 | 22.02 | 5106 | 1.0974 | 0.8133 | | 0.0451 | 23.02 | 5328 | 1.0361 | 0.8152 | | 0.2558 | 24.02 | 5550 | 0.7830 | 0.8237 | | 0.0125 | 25.02 | 5772 | 0.8728 | 0.8171 | | 0.4184 | 26.02 | 5994 | 0.8413 | 0.8265 | | 0.2566 | 27.02 | 6216 | 1.0644 | 0.8009 | | 0.1257 | 28.02 | 6438 | 0.8641 | 0.8265 | | 0.1326 | 29.02 | 6660 | 0.8444 | 0.8417 | | 0.0436 | 30.02 | 6882 | 0.8615 | 0.8322 | | 0.0408 | 31.02 | 7104 | 0.8075 | 0.8332 | | 0.0316 | 32.02 | 7326 | 0.8699 | 0.8341 | | 0.2235 | 33.02 | 7548 | 0.8151 | 0.8455 | | 0.0079 | 34.02 | 7770 | 0.8099 | 0.8550 | | 0.001 | 35.02 | 7992 | 0.8640 | 0.8370 | | 0.0007 | 36.02 | 8214 | 0.7146 | 0.8483 | | 0.464 | 37.02 | 8436 | 0.7917 | 0.8464 | | 0.0005 | 38.02 | 8658 | 0.7239 | 0.8531 | | 0.0004 | 39.02 | 8880 | 0.7702 | 0.8701 | | 0.1705 | 40.02 | 9102 | 0.7543 | 0.8521 | | 0.0039 | 41.02 | 9324 | 0.7456 | 0.8673 | | 0.0168 | 42.02 | 9546 | 0.7255 | 0.8730 | | 0.2615 | 43.02 | 9768 | 0.7453 | 0.8758 | | 0.0004 | 44.02 | 9990 | 0.6824 | 0.8806 | | 0.236 | 45.02 | 10212 | 0.6624 | 0.8825 | | 0.0007 | 46.02 | 10434 | 0.6727 | 0.8815 | | 0.0004 | 47.02 | 10656 | 0.6478 | 0.8863 | | 0.268 | 48.02 | 10878 | 0.6309 | 0.8900 | | 0.0025 | 49.02 | 11100 | 0.6284 | 0.8900 | ### Framework versions - Transformers 4.33.2 - Pytorch 1.12.1+cu113 - Datasets 2.14.5 - Tokenizers 0.13.3

annahaz/xlm-roberta-base-misogyny-sexism-tweets

annahaz

2023-09-21T17:11:00Z

104

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-08-19T17:14:54Z

This model was an experiment BUT NOT THE FINAL MODEL. The final model was ***annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal*** (https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal) Please consider using/trying that model instead. This model was an experiment for the following paper BUT THIS MODEL IS NOT THE FINAL MODEL: ``` @InProceedings{10.1007/978-3-031-43129-6_9, author="Chang, Rong-Ching and May, Jonathan and Lerman, Kristina", editor="Thomson, Robert and Al-khateeb, Samer and Burger, Annetta and Park, Patrick and A. Pyke, Aryn", title="Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions", booktitle="Social, Cultural, and Behavioral Modeling", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="85--94", abstract="Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model (https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.", isbn="978-3-031-43129-6" } ``` --- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: xlm-roberta-base-misogyny-sexism-tweets results: [] ---  # xlm-roberta-base-misogyny-sexism-tweets This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5009 - Accuracy: 0.796 - F1: 0.8132 - Precision: 0.75 - Recall: 0.888 - Mae: 0.204 - Tn: 352 - Fp: 148 - Fn: 56 - Tp: 444 ## 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 | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----:|:---:|:---:|:--:|:---:| | 0.4947 | 1.0 | 1646 | 0.4683 | 0.765 | 0.7866 | 0.7205 | 0.866 | 0.235 | 332 | 168 | 67 | 433 | | 0.4285 | 2.0 | 3292 | 0.4514 | 0.779 | 0.8004 | 0.7298 | 0.886 | 0.221 | 336 | 164 | 57 | 443 | | 0.3721 | 3.0 | 4938 | 0.4430 | 0.781 | 0.8060 | 0.7234 | 0.91 | 0.219 | 326 | 174 | 45 | 455 | | 0.3127 | 4.0 | 6584 | 0.5009 | 0.796 | 0.8132 | 0.75 | 0.888 | 0.204 | 352 | 148 | 56 | 444 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

newronai/clma2-13b-Chat-Adapter-NasdaqBalanced-1epoch

newronai

2023-09-21T17:05:34Z

2

0

peft

[ "peft", "region:us" ]

null

2023-09-21T17:05:26Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

calculater/looking-up

calculater

2023-09-21T17:03:38Z

0

4

null

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

text-to-image

2023-09-21T15:55:59Z

--- pipeline_tag: text-to-image license: creativeml-openrail-m --- [looking-up](https://huggingface.co/hhpoo/looking-up/blob/main/looking-up.safetensors) 視線を上げるloraです。ファイルをwebuiのlora指定フォルダ内に入れてご使用ください。トリガータグ等は指定しておらず、適用するだけで画面上方を見上げるはずです。 weightをマイナスにすることで下を見るようにもできるかもしれません。 ![sampleimage](tmpcplve8dm-2.png)

amirabdullah19852020/pythia-410m_utility_reward

amirabdullah19852020

2023-09-21T17:03:36Z

59

0

transformers

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

reinforcement-learning

2023-09-21T15:26:30Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="amirabdullah19852020//tmp/tmplbhw73mv/amirabdullah19852020/pythia-410m_utility_reward") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("amirabdullah19852020//tmp/tmplbhw73mv/amirabdullah19852020/pythia-410m_utility_reward") model = AutoModelForCausalLMWithValueHead.from_pretrained("amirabdullah19852020//tmp/tmplbhw73mv/amirabdullah19852020/pythia-410m_utility_reward") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

TheBlokeAI/jackfram_llama-68m-GPTQ

TheBlokeAI

2023-09-21T16:33:08Z

91

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]

text-generation

2023-09-21T16:30:27Z

A 4-bit, 128g, act_order=True GPTQ quantisation of JackFram/llama-68m, a 68 million parameter Llama1 model; created on request for software testing. Not for normal usage!

gpadam/autotrain-prospero-query-training-87679143506

gpadam

2023-09-21T16:23:36Z

114

0

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autotrain", "summarization", "unk", "dataset:gpadam/autotrain-data-prospero-query-training", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-09-07T11:44:46Z

--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain" datasets: - gpadam/autotrain-data-prospero-query-training co2_eq_emissions: emissions: 16.811591021038232 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 87679143506 - CO2 Emissions (in grams): 16.8116 ## Validation Metrics - Loss: 1.544 - Rouge1: 26.107 - Rouge2: 12.267 - RougeL: 22.582 - RougeLsum: 22.590 - Gen Len: 19.956 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/gpadam/autotrain-prospero-query-training-87679143506 ```

Panchovix/airoboros-l2-70b-gpt4-1.4.1_4bit-bpw_variants_h6-exl2

Panchovix

2023-09-21T16:21:13Z

5

0

transformers

[ "transformers", "llama", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-09-13T05:59:53Z

--- license: other --- 4bit variants quantizations of airoboros 70b 1.4.1 (https://huggingface.co/jondurbin/airoboros-l2-70b-gpt4-1.4.1), using exllama2. You can find 4.25bpw (main branch), 4.5bpw and 4.75bpw in each branch. Update 21/09/2023 Re-quanted all variants with latest exllamav2 version, which fixed some measurement issues.

Parkhat/llama2-qlora-finetunined-french

Parkhat

2023-09-21T16:20:44Z

0

peft

[ "peft", "region:us" ]

null

2023-09-21T16:20:38Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

salesforce/blipdiffusion-controlnet

salesforce

2023-09-21T15:55:24Z

85

2

diffusers

[ "diffusers", "en", "arxiv:2305.14720", "license:apache-2.0", "diffusers:BlipDiffusionControlNetPipeline", "region:us" ]

null

2023-09-21T15:55:24Z

--- license: apache-2.0 language: - en library_name: diffusers --- # BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing  Model card for BLIP-Diffusion, a text to image Diffusion model which enables zero-shot subject-driven generation and control-guided zero-shot generation. The abstract from the paper is: *Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications.* The model is created by Dongxu Li, Junnan Li, Steven C.H. Hoi. ### Model Sources  - **Original Repository:** https://github.com/salesforce/LAVIS/tree/main - **Project Page:** https://dxli94.github.io/BLIP-Diffusion-website/ ## Uses ### Zero-Shot Subject Driven Generation ```python from diffusers.pipelines import BlipDiffusionPipeline from diffusers.utils import load_image import torch blip_diffusion_pipe = BlipDiffusionPipeline.from_pretrained( "Salesforce/blipdiffusion", torch_dtype=torch.float16 ).to("cuda") cond_subject = "dog" tgt_subject = "dog" text_prompt_input = "swimming underwater" cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" ) iter_seed = 88888 guidance_scale = 7.5 num_inference_steps = 25 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt_input, cond_image, cond_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" style="width:500px;"/> Generatred Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog_underwater.png" style="width:500px;"/> ### Controlled subject-driven generation ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import CannyDetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet", torch_dtype=torch.float16 ).to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "teapot" # subject to generate. text_prompt = "on a marble table" cldm_cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" ).resize((512, 512)) canny = CannyDetector() cldm_cond_image = canny(cldm_cond_image, 30, 70, output_type="pil") style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Canny Edge Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/canny_generated.png" style="width:500px;"/> ### Controlled subject-driven generation Scribble ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import HEDdetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet" ) controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble") blip_diffusion_pipe.controlnet = controlnet blip_diffusion_pipe.to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "bag" # subject to generate. text_prompt = "on a table" cldm_cond_image = load_image( "https://huggingface.co/lllyasviel/sd-controlnet-scribble/resolve/main/images/bag.png" ).resize((512, 512)) hed = HEDdetector.from_pretrained("lllyasviel/Annotators") cldm_cond_image = hed(cldm_cond_image) style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Scribble Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble.png" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble_output.png" style="width:500px;"/> ## Model Architecture Blip-Diffusion learns a **pre-trained subject representation**. uch representation aligns with text embeddings and in the meantime also encodes the subject appearance. This allows efficient fine-tuning of the model for high-fidelity subject-driven applications, such as text-to-image generation, editing and style transfer. To this end, they design a two-stage pre-training strategy to learn generic subject representation. In the first pre-training stage, they perform multimodal representation learning, which enforces BLIP-2 to produce text-aligned visual features based on the input image. In the second pre-training stage, they design a subject representation learning task, called prompted context generation, where the diffusion model learns to generate novel subject renditions based on the input visual features. To achieve this, they curate pairs of input-target images with the same subject appearing in different contexts. Specifically, they synthesize input images by composing the subject with a random background. During pre-training, they feed the synthetic input image and the subject class label through BLIP-2 to obtain the multimodal embeddings as subject representation. The subject representation is then combined with a text prompt to guide the generation of the target image. ![img](https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch.jpg) The architecture is also compatible to integrate with established techniques built on top of the diffusion model, such as ControlNet. They attach the U-Net of the pre-trained ControlNet to that of BLIP-Diffusion via residuals. In this way, the model takes into account the input structure condition, such as edge maps and depth maps, in addition to the subject cues. Since the model inherits the architecture of the original latent diffusion model, they observe satisfying generations using off-the-shelf integration with pre-trained ControlNet without further training. <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch_controlnet.png" style="width:50%;"/> ## Citation **BibTeX:** If you find this repository useful in your research, please cite: ``` @misc{li2023blipdiffusion, title={BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing}, author={Dongxu Li and Junnan Li and Steven C. H. Hoi}, year={2023}, eprint={2305.14720}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

am-infoweb/QA_SYNTH_19_SEPT_FINETUNE_1.0

am-infoweb

2023-09-21T15:51:29Z

105

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2023-09-21T15:00:50Z

--- tags: - generated_from_trainer model-index: - name: QA_SYNTH_19_SEPT_FINETUNE_1.0 results: [] ---  # QA_SYNTH_19_SEPT_FINETUNE_1.0 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1182 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.1211 | 1.0 | 1350 | 0.1318 | | 0.0599 | 2.0 | 2700 | 0.1617 | | 0.0571 | 3.0 | 4050 | 0.0833 | | 0.0248 | 4.0 | 5400 | 0.0396 | | 0.0154 | 5.0 | 6750 | 0.0911 | | 0.0 | 6.0 | 8100 | 0.1054 | | 0.0 | 7.0 | 9450 | 0.1086 | | 0.0 | 8.0 | 10800 | 0.1224 | | 0.0002 | 9.0 | 12150 | 0.1155 | | 0.0025 | 10.0 | 13500 | 0.1182 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

ShivamMangale/XLM-Roberta-base-allhiweakdap_5th_iteration_d5_d4

ShivamMangale

2023-09-21T15:44:38Z

122

0

transformers

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

question-answering

2023-09-21T14:53:27Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-allhiweakdap_5th_iteration_d5_d4 results: [] ---  # XLM-Roberta-base-allhiweakdap_5th_iteration_d5_d4 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.4580000000000001e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

tim-d/CurtGPT

tim-d

2023-09-21T15:39:01Z

17

1

peft

[ "peft", "text-generation", "en", "dataset:LDJnr/Puffin", "dataset:pvduy/rm_hh_helpful_only", "arxiv:2305.14314", "arxiv:2305.18290", "license:other", "region:us" ]

text-generation

2023-09-21T15:24:37Z

--- license: other language: - en pipeline_tag: text-generation datasets: - LDJnr/Puffin - pvduy/rm_hh_helpful_only library_name: peft widget: - text: "USER: What's better, farming, or using computers (which suck)\nASSISTANT:" --- <table> <tr> <td style="width: 30%; text-align: left; vertical-align: middle"> # CurtGPT Using Microsoft's Phi 1.5 model like it was never intended. </td> <td style="text-align: center;"> <img src="https://github.com/tim-a-davis/silly_little_language_modeling_thing_at_utd/blob/main/curtgpt%20logo.png?raw=true" width="300" height="auto"> </td> </tr> </table> # Main Procedure This model is an adapter on [puffin phi v2](https://huggingface.co/teknium/Puffin-Phi-v2) trained using [QLoRA](https://arxiv.org/pdf/2305.14314.pdf) and [DPO](https://arxiv.org/pdf/2305.18290.pdf) on 60,000 samples from the [anthropic helpful only](https://huggingface.co/datasets/pvduy/rm_hh_helpful_only) dataset. --- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0

ryatora/distilbert-base-uncased-finetuned-emotion

ryatora

2023-09-21T15:36:40Z

105

0

transformers

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

text-classification

2023-09-19T12:44:18Z

--- 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 args: split metrics: - name: Accuracy type: accuracy value: 0.9225 - name: F1 type: f1 value: 0.9224787080842691 ---  # 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: 0.2185 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8423 | 1.0 | 250 | 0.3084 | 0.9065 | 0.9049 | | 0.2493 | 2.0 | 500 | 0.2185 | 0.9225 | 0.9225 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

ShivamMangale/XLM-Roberta-base-allhiweakdap_5th_iteration_d5

ShivamMangale

2023-09-21T15:35:52Z

122

0

transformers

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

question-answering

2023-09-21T14:45:26Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-allhiweakdap_5th_iteration_d5 results: [] ---  # XLM-Roberta-base-allhiweakdap_5th_iteration_d5 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.3122e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

neksjgg/rav3nus

neksjgg

2023-09-21T15:22:16Z

0

null

[ "streamer", "twitch", "ru", "region:us" ]

null

2023-09-21T15:15:09Z

--- language: - ru tags: - streamer - twitch ---

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1

ShivamMangale

2023-09-21T14:52:39Z

122

0

transformers

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

question-answering

2023-09-21T14:34:47Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1 results: [] ---  # XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

mann-e/mann-e_5.4

mann-e

2023-09-21T14:52:30Z

3

0

diffusers

[ "diffusers", "text-to-image", "region:us" ]

text-to-image

2023-09-21T12:47:14Z

--- library_name: diffusers pipeline_tag: text-to-image --- # Mann-E 5.4 This repository represents what is the main brain of [Mann-E](https://manne.ir) artificial intelligence platform. ## Features 1. _LoRa support_. In previous versions, most of LoRa models weren't working perfectly with the model. 2. _More coherent results_. Compared to the old versions, this version has more "midjourney" feel to its outputs. 3. _New License_. Unlike old versions this one isn't licensed undet MIT, we decided to go with our own license. ## Samples <span align="center"> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-1.png" width=512px /> <br/> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-2.png" width=512px /> <br/> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-3.png" width=512px /> <br/> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-4.png" width=512px /> <br/> <img src="https://huggingface.co/mann-e/mann-e_5.4/resolve/main/grid-5.png" width=512px /> </span> ## License This software and associated checkpoints are provided by Mann-E for educational and non-commercial use only. By accessing or using this software and checkpoints, you agree to the following terms and conditions: 1. Access and Use: - You are granted the right to access and use the source code and checkpoints for educational and non-commercial purposes. 2. Modification and Distribution: - You may modify and distribute the source code and checkpoints solely for educational and non-commercial purposes, provided that you retain this license notice. 3. Commercial Use: - Commercial use of this software and checkpoints is strictly prohibited without the explicit written consent of the Copyright Holder. 4. Fine-tuning of Checkpoints: - You may not fine-tune or modify the provided checkpoints without obtaining the express written consent of the Copyright Holder. 5. No Warranty: - This software and checkpoints are provided "as is" without any warranty. The Copyright Holder shall not be liable for any damages or liabilities arising out of the use or inability to use the software and checkpoints. 6. Termination: - This license is effective until terminated by the Copyright Holder. Your rights under this license will terminate automatically without notice from the Copyright Holder if you fail to comply with any term or condition of this license. If you do not agree to these terms and conditions or do not have the legal authority to bind yourself, you may not use, modify, or distribute this software and checkpoints. For inquiries regarding commercial use or fine-tuning of checkpoints, please contact Mann-E.

nickypro/tinyllama-15M-fp32

nickypro

2023-09-21T14:50:50Z

152

0

transformers

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

text-generation

2023-09-16T17:23:46Z

--- license: mit --- This is the Float32 15M parameter Llama 2 architecture model trained on the TinyStories dataset. These are converted from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas). See the [llama2.c](https://github.com/karpathy/llama2.c) project for more details.

anjakuzev/13b_200

anjakuzev

2023-09-21T14:50:40Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-21T14:50:37Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0

yunosuken/results

yunosuken

2023-09-21T14:50:34Z

103

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:tohoku-nlp/bert-large-japanese-v2", "base_model:finetune:tohoku-nlp/bert-large-japanese-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-13T14:15:12Z

--- license: apache-2.0 base_model: cl-tohoku/bert-large-japanese-v2 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bert-large-japanease-v2-gpt4-relevance-learned results: [] ---  # bert-large-japanease-v2-gpt4-relevance-learned This model is a fine-tuned version of [cl-tohoku/bert-large-japanese-v2](https://huggingface.co/cl-tohoku/bert-large-japanese-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2693 - Accuracy: 0.885 - F1: 0.8788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 3.3692 | 1.0 | 563 | 3.2122 | 0.872 | 0.8560 | | 3.0963 | 2.0 | 1126 | 3.1045 | 0.866 | 0.8625 | | 2.8698 | 3.0 | 1689 | 3.1410 | 0.882 | 0.8755 | | 2.6212 | 4.0 | 2252 | 3.2119 | 0.876 | 0.8702 | | 2.407 | 5.0 | 2815 | 3.2693 | 0.885 | 0.8788 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

nickypro/tinyllama-110M-fp32

nickypro

2023-09-21T14:50:10Z

166

0

transformers

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

text-generation

2023-09-16T17:26:31Z

--- license: mit --- This is the float32 110M parameter Llama 2 architecture model trained on the TinyStories dataset. These are converted from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas). See the [llama2.c](https://github.com/karpathy/llama2.c) project for more details.

ayushtues/blipdiffusion

ayushtues

2023-09-21T14:44:10Z

7

0

diffusers

[ "diffusers", "safetensors", "en", "arxiv:2305.14720", "license:apache-2.0", "diffusers:BlipDiffusionPipeline", "region:us" ]

null

2023-08-07T05:45:01Z

--- license: apache-2.0 language: - en library_name: diffusers --- # BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing  Model card for BLIP-Diffusion, a text to image Diffusion model which enables zero-shot subject-driven generation and control-guided zero-shot generation. The abstract from the paper is: *Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications.* The model is created by Dongxu Li, Junnan Li, Steven C.H. Hoi. ### Model Sources  - **Original Repository:** https://github.com/salesforce/LAVIS/tree/main - **Project Page:** https://dxli94.github.io/BLIP-Diffusion-website/ ## Uses ### Zero-Shot Subject Driven Generation ```python from diffusers.pipelines import BlipDiffusionPipeline from diffusers.utils import load_image import torch blip_diffusion_pipe = BlipDiffusionPipeline.from_pretrained( "Salesforce/blipdiffusion", torch_dtype=torch.float16 ).to("cuda") cond_subject = "dog" tgt_subject = "dog" text_prompt_input = "swimming underwater" cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" ) iter_seed = 88888 guidance_scale = 7.5 num_inference_steps = 25 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt_input, cond_image, cond_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" style="width:500px;"/> Generatred Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog_underwater.png" style="width:500px;"/> ### Controlled subject-driven generation ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import CannyDetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet", torch_dtype=torch.float16 ).to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "teapot" # subject to generate. text_prompt = "on a marble table" cldm_cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" ).resize((512, 512)) canny = CannyDetector() cldm_cond_image = canny(cldm_cond_image, 30, 70, output_type="pil") style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Canny Edge Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/canny_generated.png" style="width:500px;"/> ### Controlled subject-driven generation Scribble ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import HEDdetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet" ) controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble") blip_diffusion_pipe.controlnet = controlnet blip_diffusion_pipe.to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "bag" # subject to generate. text_prompt = "on a table" cldm_cond_image = load_image( "https://huggingface.co/lllyasviel/sd-controlnet-scribble/resolve/main/images/bag.png" ).resize((512, 512)) hed = HEDdetector.from_pretrained("lllyasviel/Annotators") cldm_cond_image = hed(cldm_cond_image) style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Scribble Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble.png" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble_output.png" style="width:500px;"/> ## Model Architecture Blip-Diffusion learns a **pre-trained subject representation**. uch representation aligns with text embeddings and in the meantime also encodes the subject appearance. This allows efficient fine-tuning of the model for high-fidelity subject-driven applications, such as text-to-image generation, editing and style transfer. To this end, they design a two-stage pre-training strategy to learn generic subject representation. In the first pre-training stage, they perform multimodal representation learning, which enforces BLIP-2 to produce text-aligned visual features based on the input image. In the second pre-training stage, they design a subject representation learning task, called prompted context generation, where the diffusion model learns to generate novel subject renditions based on the input visual features. To achieve this, they curate pairs of input-target images with the same subject appearing in different contexts. Specifically, they synthesize input images by composing the subject with a random background. During pre-training, they feed the synthetic input image and the subject class label through BLIP-2 to obtain the multimodal embeddings as subject representation. The subject representation is then combined with a text prompt to guide the generation of the target image. ![img](https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch.jpg) The architecture is also compatible to integrate with established techniques built on top of the diffusion model, such as ControlNet. They attach the U-Net of the pre-trained ControlNet to that of BLIP-Diffusion via residuals. In this way, the model takes into account the input structure condition, such as edge maps and depth maps, in addition to the subject cues. Since the model inherits the architecture of the original latent diffusion model, they observe satisfying generations using off-the-shelf integration with pre-trained ControlNet without further training. <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch_controlnet.png" style="width:50%;"/> ## Citation **BibTeX:** If you find this repository useful in your research, please cite: ``` @misc{li2023blipdiffusion, title={BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing}, author={Dongxu Li and Junnan Li and Steven C. H. Hoi}, year={2023}, eprint={2305.14720}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1_d0-hq

ShivamMangale

2023-09-21T14:42:50Z

105

0

transformers

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

question-answering

2023-09-21T14:20:18Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1_d0-hq results: [] ---  # XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1_d0-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

newronai/clma2-13b-Chat-Adapter-NasdaqBalanced-3epoch

newronai

2023-09-21T14:41:07Z

0

peft

[ "peft", "region:us" ]

null

2023-09-21T14:41:00Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

jonas-luehrs/chembert_cased-textCLS-RHEOLOGY

jonas-luehrs

2023-09-21T14:40:21Z

105

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:jiangg/chembert_cased", "base_model:finetune:jiangg/chembert_cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-21T14:34:07Z

--- base_model: jiangg/chembert_cased tags: - generated_from_trainer metrics: - f1 - precision - recall - accuracy model-index: - name: chembert_cased-textCLS-RHEOLOGY results: [] ---  # chembert_cased-textCLS-RHEOLOGY This model is a fine-tuned version of [jiangg/chembert_cased](https://huggingface.co/jiangg/chembert_cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6766 - F1: 0.7253 - Precision: 0.7446 - Recall: 0.7407 - Accuracy: 0.7407 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| | 1.2479 | 1.0 | 46 | 0.9758 | 0.6185 | 0.5919 | 0.6605 | 0.6605 | | 0.8039 | 2.0 | 92 | 0.7210 | 0.7277 | 0.7472 | 0.7407 | 0.7407 | | 0.5982 | 3.0 | 138 | 0.6766 | 0.7253 | 0.7446 | 0.7407 | 0.7407 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

LarryAIDraw/takina_inoue_v1

LarryAIDraw

2023-09-21T14:35:35Z

0

null

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

null

2023-09-21T13:33:54Z

--- license: creativeml-openrail-m --- https://civitai.com/models/148903/takina-inoue-or-lycoris-recoil-5-outfits

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2

ShivamMangale

2023-09-21T14:34:44Z

133

0

transformers

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

question-answering

2023-09-21T14:23:28Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2 results: [] ---  # XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.8e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

FredericProtat/output

FredericProtat

2023-09-21T14:33:01Z

31

0

transformers

[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "dataset:decision_transformer_gym_replay", "endpoints_compatible", "region:us" ]

null

2023-09-21T14:32:59Z

--- tags: - generated_from_trainer datasets: - decision_transformer_gym_replay model-index: - name: output results: [] ---  # output This model is a fine-tuned version of [](https://huggingface.co/) on the decision_transformer_gym_replay 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: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 120 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

ramboind/infra

ramboind

2023-09-21T14:26:54Z

0

null

[ "license:cc-by-nc-nd-3.0", "region:us" ]

null

2023-09-21T14:26:54Z

--- license: cc-by-nc-nd-3.0 ---

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1-hq

ShivamMangale

2023-09-21T14:20:17Z

105

0

transformers

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

question-answering

2023-09-21T14:06:26Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1-hq results: [] ---  # XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3_d2_d1-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

yagizergil/llama2-yagiz

yagizergil

2023-09-21T14:12:17Z

0

peft

[ "peft", "region:us" ]

null

2023-09-21T12:49:31Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0

ShivamMangale

2023-09-21T14:02:28Z

122

0

transformers

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

question-answering

2023-09-21T09:57:35Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0 results: [] ---  # XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3-hq

ShivamMangale

2023-09-21T13:56:46Z

105

0

transformers

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

question-answering

2023-09-21T13:48:23Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3-hq results: [] ---  # XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4_d3-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.62e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

jmgb0127/bloom-lotr

jmgb0127

2023-09-21T13:56:28Z

1

0

peft

[ "peft", "base_model:bigscience/bloom-3b", "base_model:adapter:bigscience/bloom-3b", "region:us" ]

null

2023-08-28T00:11:48Z

--- library_name: peft base_model: bigscience/bloom-3b --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0

cedric7ginobili/margaux

cedric7ginobili

2023-09-21T13:56:23Z

2

1

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-09-20T12:04:11Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a margauxmafille person tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

swastikhurana/q-Taxi-v1

swastikhurana

2023-09-21T13:54:37Z

0

null

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

reinforcement-learning

2023-09-21T13:54:35Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 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="swastikhurana/q-Taxi-v1", 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"]) ```

Coroseven/TEST

Coroseven

2023-09-21T13:49:30Z

0

null

[ "region:us" ]

null

2023-09-07T14:18:20Z

TEST 2 este un model combinat intre Primary model (A) - V3.0 Nordrin_little（诺德琳little）; Secondary model (B) - aamAnyloraAnimeMixAnime_v1 ; Tertiary model (C) - aingdiffusion_v92 la Multiplier (M) - 0.5 Weighted sum TEST 3 este un model combinat intre Primary model (A) - aamAnyloraAnimeMixAnime_v1 ; Secondary model (B) - V3.0 Nordrin_little（诺德琳little）; la Multiplier (M) - 0.5 Weighted sum TEST 5 este un model combinat intre Primary model (A) - aamAnyloraAnimeMixAnime_v1 ; Secondary model (B) - V3.0 Nordrin_little（诺德琳little）; Tertiary model (C) - aingdiffusion_v92 la Multiplier (M) - 0.3 Weighted sum TEST 6 este un model combinat intre Primary model (A) - aamAnyloraAnimeMixAnime_v1 ; Secondary model (B) - BlueAilandMix (blueailandmix_v11) ; la Multiplier (M) - 0.4 Weighted sum TEST 12 este un model combinat intre Primary model (A) - aamAnyloraAnimeMixAnime_v1 ; Secondary model (B) - Sudachi (sudachi_v1.0) ; la Multiplier (M) - 0.5 Weighted sum TEST 13 este un model combinat intre Primary model (A) - TEST 12 ; Secondary model (B) - AingDiffusion (AingDiffusion_v9.2) ; la Multiplier (M) - 0.4 Weighted sum

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4-hq

ShivamMangale

2023-09-21T13:48:23Z

105

0

transformers

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

question-answering

2023-09-21T13:40:12Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4-hq results: [] ---  # XLM-Roberta-base-all_hi_weakdap_4th_iteration_d4-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.4580000000000001e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

nichonifroa/bert-finetuned-squad

nichonifroa

2023-09-21T13:47:34Z

122

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:nichonifroa/bert-finetuned-squad", "base_model:finetune:nichonifroa/bert-finetuned-squad", "endpoints_compatible", "region:us" ]

question-answering

2023-09-21T10:04:51Z

--- base_model: nichonifroa/bert-finetuned-squad tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] ---  # bert-finetuned-squad This model is a fine-tuned version of [nichonifroa/bert-finetuned-squad](https://huggingface.co/nichonifroa/bert-finetuned-squad) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Ibrahim-Alam/finetuning-bert-base-uncased-on-Cornell_sentiment

Ibrahim-Alam

2023-09-21T13:44:05Z

106

0

transformers

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

text-classification

2023-09-21T13:41:53Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-bert-base-uncased-on-Cornell_sentiment results: [] ---  # finetuning-bert-base-uncased-on-Cornell_sentiment This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3582 - Accuracy: 0.8626 - F1: 0.8542 ## 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: 1 ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

BigR/Lunar_lander

BigR

2023-09-21T13:41:04Z

0

stable-baselines3

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

reinforcement-learning

2023-09-21T13:40:44Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.02 +/- 15.24 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 ... ```

fsuarez/autotrain-logo-identifier-90194144191

fsuarez

2023-09-21T13:38:51Z

184

0

transformers

[ "transformers", "pytorch", "safetensors", "swin", "image-classification", "autotrain", "vision", "dataset:fsuarez/autotrain-data-logo-identifier", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-19T14:59:47Z

--- tags: - autotrain - vision - image-classification datasets: - fsuarez/autotrain-data-logo-identifier widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.060824697813101125 --- # 📒 logo-identifier-model This model has been trained on a dataset called "LogoIdentifier" for multi-class classification of logos from 57 renowned brands and companies. These brands encompass a wide spectrum of industries and recognition, ranging from global giants like Coca-Cola, Coleman, Google, IBM, Nike, Pepsi, and many others. Each brand is thoughtfully organized into its designated subfolder, housing a comprehensive set of logo images for precise and accurate classification. Whether you're identifying iconic logos or exploring the branding diversity of these 57 famous names, this model is your go-to solution for logo recognition and classification. # 🧪 Dataset Content - The dataset includes logos from various brands and companies. - The dataset is organized into subfolders, each corresponding to a specific brand or company. - It contains a wide range of brand logos, including Acer, Acura, Adidas, Samsung, Lenovo, McDonald's, Java, and many more. - Each brand or company in the dataset is associated with a numerical value, likely representing the number of images available for that brand. The model has been trained to recognize and classify logos into their respective brand categories based on the images provided in the dataset. | Company | Quantity of images | | ----------------- | ------------------ | | Acer | 67 | | Acura | 74 | | Addidas | 90 | | Ades | 36 | | Adio | 63 | | Cadillac | 69 | | CalvinKlein | 65 | | Canon | 59 | | Cocacola | 40 | | CocaColaZero | 91 | | Coleman | 57 | | Converse | 60 | | CornFlakes | 62 | | DominossPizza | 99 | | Excel | 88 | | Gillette | 86 | | GMC | 75 | | Google | 93 | | HardRockCafe | 93 | | HBO | 103 | | Heineken | 84 | | HewlettPackard | 81 | | Hp | 87 | | Huawei | 84 | | Hyundai | 84 | | IBM | 84 | | Java | 62 | | KFC | 84 | | Kia | 76 | | Kingston | 79 | | Lenovo | 82 | | LG | 95 | | Lipton | 94 | | Mattel | 77 | | McDonalds | 98 | | MercedesBenz | 94 | | Motorola | 86 | | Nestle | 94 | | Nickelodeon | 74 | | Nike | 50 | | Pennzoil | 82 | | Pepsi | 93 | | Peugeot | 60 | | Porsche | 71 | | Samsung | 96 | | SchneiderElectric | 42 | | Shell | 58 | To use this model for brand logo identification, you can make use of the Hugging Face Transformers library and load the model using its model ID (90194144191). You can then input an image of a brand logo, and the model should be able to predict the brand it belongs to based on its training. # 🤗 Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 90194144191 - CO2 Emissions (in grams): 0.0608 ## 📐 Validation Metrics - Loss: 0.300 - Accuracy: 0.924 - Macro F1: 0.924 - Micro F1: 0.924 - Weighted F1: 0.922 - Macro Precision: 0.930 - Micro Precision: 0.924 - Weighted Precision: 0.928 - Macro Recall: 0.924 - Micro Recall: 0.924 - Weighted Recall: 0.924

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0-hq

ShivamMangale

2023-09-21T13:37:37Z

106

0

transformers

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

question-answering

2023-09-21T13:15:08Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0-hq results: [] ---  # XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2_d1_d0-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

LarryAIDraw/Char_Honkai_Raiden_Mei_adult

LarryAIDraw

2023-09-21T13:36:19Z

0

null

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

null

2023-09-21T13:34:19Z

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

sanctia/lora-sd-finesse

sanctia

2023-09-21T13:33:39Z

1

0

diffusers

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

text-to-image

2023-09-20T02:37:21Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - sanctia/lora-sd-finesse These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the sanctia/finesse-image-generation dataset. You can find some example images in the following. - Model and architecture details: https://www.notion.so/Design-document-Finesse-Generative-Challenge-4ed87ea624f84ff5a9ac09dc21885366 - Wandb report: https://wandb.ai/hpml3/text2image-fine-tune/runs/cdyy9un3?workspace=user-sanctia ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

srushtibhavsar/squad_bloom_3b

srushtibhavsar

2023-09-21T13:29:25Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-21T13:29:23Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0

chanifrusydi/t5-dialogue-summarization

chanifrusydi

2023-09-21T13:27:14Z

134

2

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "summarization", "dataset:samsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2022-06-08T05:08:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - accuracy pipeline_tag: summarization base_model: t5-small model-index: - name: t5-dialogue-summarization results: [] ---  # t5-dialogue-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. dataset: type: {summarization} name: {samsum} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

CyberHarem/nishikawa_honami_idolmastercinderellagirls

CyberHarem

2023-09-21T13:13:40Z

0

null

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

text-to-image

2023-09-21T13:02:46Z

--- license: mit datasets: - CyberHarem/nishikawa_honami_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of nishikawa_honami_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4080, you need to download `4080/nishikawa_honami_idolmastercinderellagirls.pt` as the embedding and `4080/nishikawa_honami_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4080**, with the score of 0.919. The trigger words are: 1. `nishikawa_honami_idolmastercinderellagirls` 2. `long_hair, brown_hair, green_eyes, earrings, jewelry, smile, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.903 | [Download](5100/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.895 | [Download](4760/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.912 | [Download](4420/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | **4080** | **0.919** | [**Download**](4080/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.831 | [Download](3740/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.872 | [Download](3400/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.882 | [Download](3060/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.909 | [Download](2720/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.857 | [Download](2380/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.893 | [Download](2040/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.874 | [Download](1700/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.860 | [Download](1360/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.877 | [Download](1020/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.753 | [Download](680/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.579 | [Download](340/nishikawa_honami_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2

ShivamMangale

2023-09-21T13:12:54Z

123

0

transformers

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

question-answering

2023-09-21T09:29:28Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2 results: [] ---  # XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3_d2 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.8e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3

ShivamMangale

2023-09-21T13:01:35Z

122

0

transformers

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

question-answering

2023-09-21T09:20:45Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3 results: [] ---  # XLM-Roberta-base-all_hi_weakdap_3rd_iteration_d3 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.62e-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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

aminh/squad-falcon-7b

aminh

2023-09-21T12:57:14Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-21T12:57:06Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

MattStammers/appo-mujoco-doublependulum

MattStammers

2023-09-21T12:56:15Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-21T12:56:12Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: mujoco_doublependulum type: mujoco_doublependulum metrics: - type: mean_reward value: 6568.13 +/- 4264.02 name: mean_reward verified: false --- A(n) **APPO** model trained on the **mujoco_doublependulum** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-mujoco-doublependulum ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_doublependulum --train_dir=./train_dir --experiment=appo-mujoco-doublependulum ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_doublependulum --train_dir=./train_dir --experiment=appo-mujoco-doublependulum --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

antphb/pretrain-gpt2-large

antphb

2023-09-21T12:53:55Z

132

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:NlpHUST/gpt2-vietnamese", "base_model:finetune:NlpHUST/gpt2-vietnamese", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-21T08:31:12Z

--- base_model: NlpHUST/gpt2-vietnamese tags: - generated_from_trainer model-index: - name: pretrain-gpt2-large results: [] ---  # pretrain-gpt2-large This model is a fine-tuned version of [NlpHUST/gpt2-vietnamese](https://huggingface.co/NlpHUST/gpt2-vietnamese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4155 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 70 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.0875 | 13.05 | 500 | 2.6828 | | 2.5739 | 26.1 | 1000 | 2.5363 | | 2.4573 | 39.15 | 1500 | 2.4643 | | 2.3962 | 52.2 | 2000 | 2.4294 | | 2.3662 | 65.25 | 2500 | 2.4155 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

duwuonline/my-ielts

duwuonline

2023-09-21T12:49:48Z

105

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-21T12:20:17Z

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: my-ielts results: [] ---  # my-ielts This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-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: 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: 100 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

BlahBlah1/LLama2

BlahBlah1

2023-09-21T12:45:20Z

2

0

null

[ "gguf", "license:apache-2.0", "region:us" ]

null

2023-09-21T12:10:06Z

--- license: apache-2.0 --- Openbuddy's conversion of GGML model to GGUF as per new AUG 21 update of llama.cpp supporting GGML files A quantised version of the model.

MThonar/Linkk

MThonar

2023-09-21T12:33:31Z

30

0

diffusers

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

text-to-image

2023-09-21T12:27:35Z

--- license: creativeml-openrail-m tags: - pytorch - diffusers - stable-diffusion - text-to-image - diffusion-models-class - dreambooth --- # DreamBooth model of Link trained by MThonar on the MThonar/link dataset. This is a Stable Diffusion model fine-tuned with Dreambooth on images of Linkk. It can be used by modifying the `instance_prompt`: **a photo of Linkk** ## Description This is a Stable Diffusion model fine-tuned on images of Linkk. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('MThonar/Linkk') image = pipeline().images[0] image ```

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1_d0

ShivamMangale

2023-09-21T12:32:06Z

122

0

transformers

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

question-answering

2023-09-21T08:46:47Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1_d0 results: [] ---  # XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1_d0 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

jphme/phi-1_5_Wizard_Vicuna_uncensored

jphme

2023-09-21T12:23:23Z

69

27

transformers

[ "transformers", "pytorch", "mixformer-sequential", "text-generation", "phi", "phi-1_5", "english", "custom_code", "en", "dataset:ehartford/wizard_vicuna_70k_unfiltered", "autotrain_compatible", "region:us" ]

text-generation

2023-09-12T17:30:57Z

--- language: - en library_name: transformers pipeline_tag: text-generation inference: true tags: - pytorch - phi - phi-1_5 - english datasets: - ehartford/wizard_vicuna_70k_unfiltered --- # Phi 1.5 Wizard Vicuna Experimental Experimental Finetune on Microsoft's [Phi 1.5](https://huggingface.co/microsoft/phi-1_5). This is highly experimental, only trained on a subset of the 70k Wizard Vicuna dataset and not meant for production use. This model also runs reasonably fast on CPU! Will update with later checkpoints later. # Prompt Format ShareGPT / Vicuna (without newlines): ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: This is a question? ASSISTANT: Here is my answer" ``` # Code Example ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("jphme/phi-1_5_wizzard_vicuna_uncensored", trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda") tokenizer = AutoTokenizer.from_pretrained("jphme/phi-1_5_wizzard_vicuna_uncensored", trust_remote_code=True, torch_dtype=torch.bfloat16) prompt_template=("A chat between a curious user and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the user's questions. " "USER: {prompt} ASSISTANT:") inputs = tokenizer( prompt_template.format(prompt="What is 1+1?"), return_tensors="pt", return_attention_mask=False).to('cuda') outputs = model.generate( **inputs, max_length=200, do_sample=True, temperature=0.5, top_p=0.95, use_cache=True, repetition_penalty=1.1, #can try to uncomment this if model doesn't end #eos_token_id=tokenizer.eos_token_id ) text = tokenizer.batch_decode(outputs,skip_special_tokens=True)[0] print(text) ``` The model doesn't stop by itself (this could be an issue due to eos/bos tokens being identical), so you can try to explicitly set the `eos_token_id` (see above) or use a custom stopping criteria. You can remove `.to("cuda")` to let the model run without a GPU! # Examples > A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Who was president of the USA in 1995? ASSISTANT: The president of the United States at that time was Bill Clinton (...) > A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Write a python function that returns the fibonacci sequence ASSISTANT:The Fibonacci sequence is a series of numbers where each number after the first two are obtained by adding the two preceding ones together (starting from 0). So the sequence goes 0, 1, 1, 2, 3, 5, 8, 13, 21... Here's a Python function that generates the nth term in the Fibonacci sequence: ```python def fibonacci(n): if n <= 1: return n else: f = [0,1] for i in range(2, n+1): f.append(f[-1]+f[-2]) return f[-1] ``` > This function takes as input `n`, which represents the (...) (this function works with corrected intendation) > A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: What is (500+12)/4? ASSISTANT:The answer to this expression is 7.5. (...) Some potential here ;-)... # Thanks Thanks to Microsoft for releaseing Phi 1.5, [Eric Hartford](https://huggingface.co/ehartford) for releasing the Wizard Vicuna dataset, [Winglian](https://huggingface.co/winglian) for his great work on Axolotl that I use for finetuning and [Teknium](https://huggingface.co/teknium) for some Phi finetuning discussion. # License The original licenses of the dataset and model applies. No warranty whatsoever, this model is only intended for research purposes.

bavolesy/Reinforce-Cartpole-v1

bavolesy

2023-09-21T12:19:12Z

0

null

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

reinforcement-learning

2023-09-21T12:18:58Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 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

Volkan/photography-upscalers

Volkan

2023-09-21T12:15:58Z

0

null

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

null

2023-09-05T19:05:51Z

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

MattStammers/appo-mujoco-pendulum

MattStammers

2023-09-21T12:11:43Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-21T12:11:40Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: mujoco_pendulum type: mujoco_pendulum metrics: - type: mean_reward value: 1000.00 +/- 0.00 name: mean_reward verified: false --- A(n) **APPO** model trained on the **mujoco_pendulum** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-mujoco-pendulum ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_pendulum --train_dir=./train_dir --experiment=appo-mujoco-pendulum ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_pendulum --train_dir=./train_dir --experiment=appo-mujoco-pendulum --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

Rexe/Deci-Decicoder-1b-qlora-coder

Rexe

2023-09-21T12:07:27Z

3

0

peft

[ "peft", "base_model:Deci/DeciCoder-1b", "base_model:adapter:Deci/DeciCoder-1b", "region:us" ]

null

2023-09-19T01:30:55Z

--- library_name: peft base_model: Deci/DeciCoder-1b --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1

ShivamMangale

2023-09-21T12:00:23Z

133

0

transformers

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

question-answering

2023-09-21T08:29:28Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1 results: [] ---  # XLM-Roberta-base-all_hi_weakdap_2nd_iteration_d2_d1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

MattStammers/appo-mujoco-walker

MattStammers

2023-09-21T11:43:35Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-20T17:00:51Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: ATQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: mujoco_walker type: mujoco_walker metrics: - type: mean_reward value: 3553.55 +/- 944.12 name: mean_reward verified: false --- A(n) **ATQC** model trained on the **mujoco_walker** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-mujoco-swimmer ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=ATQC --env=mujoco_walker --train_dir=./train_dir --experiment=appo-mujoco-swimmer ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.mujoco.train_mujoco --algo=ATQC --env=mujoco_walker --train_dir=./train_dir --experiment=appo-mujoco-swimmer --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0-hq

ShivamMangale

2023-09-21T11:36:49Z

3

0

transformers

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

question-answering

2023-09-21T11:20:16Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0-hq results: [] ---  # XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0-hq This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

Ori/lama-2-13b-peft-strategyqa-no-retrieval-1-v2-seed-3

Ori

2023-09-21T11:36:27Z

3

0

peft

[ "peft", "safetensors", "region:us" ]

null

2023-09-21T11:34:07Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0

dss107/mp_base

dss107

2023-09-21T11:33:48Z

3

0

sentence-transformers

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

text-classification

2023-09-21T11:32:21Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # dss107/mp_base This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("dss107/mp_base") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

haseong8012/wav2vec2-large-xlsr-53_ko2

haseong8012

2023-09-21T11:30:32Z

105

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:zeroth_korean", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-20T11:27:30Z

--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - zeroth_korean metrics: - wer model-index: - name: wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: zeroth_korean type: zeroth_korean config: clean split: test args: clean metrics: - name: Wer type: wer value: 0.9067911459117602 ---  # wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the zeroth_korean dataset. It achieves the following results on the evaluation set: - Loss: 1.4929 - Wer: 0.9068 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.99 | 38 | 54.4059 | 1.0 | | No log | 2.0 | 77 | 38.8388 | 1.0 | | No log | 2.99 | 115 | 24.1740 | 1.0 | | No log | 4.0 | 154 | 16.4733 | 1.0 | | No log | 4.99 | 192 | 10.1900 | 1.0 | | No log | 6.0 | 231 | 6.0076 | 1.0 | | No log | 6.99 | 269 | 4.8990 | 1.0 | | No log | 8.0 | 308 | 4.8442 | 1.0 | | No log | 8.99 | 346 | 4.8284 | 1.0 | | No log | 10.0 | 385 | 4.8316 | 1.0 | | 16.886 | 10.99 | 423 | 4.8164 | 1.0 | | 16.886 | 12.0 | 462 | 4.7815 | 1.0 | | 16.886 | 12.99 | 500 | 4.7204 | 0.9989 | | 16.886 | 14.0 | 539 | 4.6842 | 0.9989 | | 16.886 | 14.99 | 577 | 4.6641 | 0.9994 | | 16.886 | 16.0 | 616 | 4.6527 | 1.0 | | 16.886 | 16.99 | 654 | 4.6745 | 0.9992 | | 16.886 | 18.0 | 693 | 4.6591 | 1.0 | | 16.886 | 18.99 | 731 | 4.6506 | 0.9997 | | 16.886 | 20.0 | 770 | 4.6719 | 0.9967 | | 4.4391 | 20.99 | 808 | 4.6067 | 0.9968 | | 4.4391 | 22.0 | 847 | 4.5748 | 0.9968 | | 4.4391 | 22.99 | 885 | 4.5166 | 0.9962 | | 4.4391 | 24.0 | 924 | 4.3783 | 0.9926 | | 4.4391 | 24.99 | 962 | 4.2711 | 0.9913 | | 4.4391 | 26.0 | 1001 | 3.6515 | 1.0030 | | 4.4391 | 26.99 | 1039 | 3.1057 | 1.0640 | | 4.4391 | 28.0 | 1078 | 2.6593 | 1.0742 | | 4.4391 | 28.99 | 1116 | 2.4071 | 1.0587 | | 4.4391 | 30.0 | 1155 | 2.2041 | 1.0379 | | 4.4391 | 30.99 | 1193 | 2.0495 | 1.0319 | | 3.1722 | 32.0 | 1232 | 1.9754 | 1.0459 | | 3.1722 | 32.99 | 1270 | 1.8658 | 0.9968 | | 3.1722 | 34.0 | 1309 | 1.7887 | 0.9883 | | 3.1722 | 34.99 | 1347 | 1.7560 | 0.9776 | | 3.1722 | 36.0 | 1386 | 1.6987 | 0.9675 | | 3.1722 | 36.99 | 1424 | 1.6513 | 0.9443 | | 3.1722 | 38.0 | 1463 | 1.6187 | 0.9473 | | 3.1722 | 38.99 | 1501 | 1.6210 | 0.9408 | | 3.1722 | 40.0 | 1540 | 1.5957 | 0.9458 | | 3.1722 | 40.99 | 1578 | 1.5673 | 0.9246 | | 1.2364 | 42.0 | 1617 | 1.5748 | 0.9286 | | 1.2364 | 42.99 | 1655 | 1.5333 | 0.9217 | | 1.2364 | 44.0 | 1694 | 1.5138 | 0.9100 | | 1.2364 | 44.99 | 1732 | 1.5244 | 0.9223 | | 1.2364 | 46.0 | 1771 | 1.5041 | 0.9080 | | 1.2364 | 46.99 | 1809 | 1.5151 | 0.9155 | | 1.2364 | 48.0 | 1848 | 1.4955 | 0.9077 | | 1.2364 | 48.99 | 1886 | 1.4924 | 0.9065 | | 1.2364 | 49.35 | 1900 | 1.4929 | 0.9068 | ### Framework versions - Transformers 4.33.2 - Pytorch 1.12.1 - Datasets 2.14.5 - Tokenizers 0.13.3

ashishpatel26/phi-1_5-finetuned-dialogstudio

ashishpatel26

2023-09-21T11:27:33Z

0

null

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

null

2023-09-21T10:43:09Z

--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer datasets: - dialogstudio model-index: - name: phi-1_5-finetuned-dialogstudio results: [] ---  # phi-1_5-finetuned-dialogstudio This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the dialogstudio dataset. It achieves the following results on the evaluation set: - Loss: 3.2430 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 3 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

lora-library/zac

lora-library

2023-09-21T11:27:25Z

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stablediffusionapi/majicmixrealistic", "base_model:adapter:stablediffusionapi/majicmixrealistic", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-21T11:27:25Z

--- license: creativeml-openrail-m base_model: stablediffusionapi/majicmixrealistic instance_prompt: z4c tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - zac These are LoRA adaption weights for [stablediffusionapi/majicmixrealistic](https://huggingface.co/stablediffusionapi/majicmixrealistic). The weights were trained on the instance prompt "z4c" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.

ldos/text_shortening_model_v47

ldos

2023-09-21T11:25:38Z

4

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-xsum", "base_model:finetune:facebook/bart-large-xsum", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-21T10:04:22Z

--- license: mit base_model: facebook/bart-large-xsum tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v47 results: [] ---  # text_shortening_model_v47 This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.3912 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Bert precision: 0.6047 - Bert recall: 0.5681 - Average word count: 1.0 - Max word count: 1 - Min word count: 1 - Average token count: 12.0 - % shortened texts with length > 12: 0.0 ## 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: 0.005 - 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| | 7.822 | 1.0 | 83 | 7.4737 | 0.0776 | 0.0 | 0.0775 | 0.0776 | 0.6348 | 0.6223 | 2.0 | 2 | 2 | 13.0 | 0.0 | | 3.2859 | 2.0 | 166 | 6.6585 | 0.1063 | 0.0 | 0.1063 | 0.1063 | 0.6469 | 0.608 | 5.0026 | 6 | 5 | 12.0 | 0.0 | | 3.0284 | 3.0 | 249 | 6.4761 | 0.116 | 0.0 | 0.116 | 0.1161 | 0.6479 | 0.6388 | 3.9974 | 4 | 3 | 14.0 | 0.0 | | 2.9681 | 4.0 | 332 | 6.4592 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6071 | 0.5723 | 1.0 | 1 | 1 | 12.0 | 0.0 | | 2.9377 | 5.0 | 415 | 6.4142 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6047 | 0.5681 | 1.0 | 1 | 1 | 12.0 | 0.0 | | 2.9168 | 6.0 | 498 | 6.4049 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6049 | 0.5685 | 1.0 | 1 | 1 | 12.0 | 0.0 | | 2.8964 | 7.0 | 581 | 6.3912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6047 | 0.5681 | 1.0 | 1 | 1 | 12.0 | 0.0 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

monsterapi/opt1.3B_codeinstruct

monsterapi

2023-09-21T11:23:26Z

0

peft

[ "peft", "facebook-opt-1.3b", "code", "instruct", "instruct-code", "code-alpaca", "alpaca-instruct", "alpaca", "opt-1.3b", "dataset:sahil2801/CodeAlpaca-20k", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]

null

2023-05-06T03:17:16Z

--- library_name: peft tags: - facebook-opt-1.3b - code - instruct - instruct-code - code-alpaca - alpaca-instruct - alpaca - opt-1.3b datasets: - sahil2801/CodeAlpaca-20k base_model: codellama/CodeLlama-7b-hf --- We finetuned Facebook/OPT-1.3B on Code-Alpaca-Instruct Dataset (sahil2801/CodeAlpaca-20k) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 1 hour and 30 minutes and costed us only `$6` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: facebook/opt-1.3b - Dataset: sahil2801/CodeAlpaca-20k - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---

monsterapi/opt125M_alpaca

monsterapi

2023-09-21T11:23:21Z

146

0

peft

[ "peft", "facebook/opt-125m", "code", "instruct", "alpaca-instruct", "alpaca", "dataset:tatsu-lab/alpaca", "base_model:facebook/opt-125m", "base_model:adapter:facebook/opt-125m", "region:us" ]

null

2023-05-13T05:38:51Z

--- library_name: peft tags: - facebook/opt-125m - code - instruct - alpaca-instruct - alpaca datasets: - tatsu-lab/alpaca base_model: facebook/opt-125m --- We finetuned facebook/opt-125m on tatsu-lab/alpaca Dataset for 10 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/tatsu-lab/alpaca unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 40 minutes and costed us only `$4` for the entire finetuning run! #### Hyperparameters & Run details: - Model: facebook/opt-125m - Dataset: tatsu-lab/alpaca - Learning rate: 0.0003 - Number of epochs: 10 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 - --- license: apache-2.0 ---

monsterapi/OpenPlatypus_LLAMA2_7b

monsterapi

2023-09-21T11:23:18Z

6

1

peft

[ "peft", "meta-llama/Llama-2-7b-hf", "code", "instruct", "instruct-code", "logical-reasoning", "Platypus2", "dataset:garage-bAInd/Open-Platypus", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

2023-09-05T10:13:05Z

--- library_name: peft tags: - meta-llama/Llama-2-7b-hf - code - instruct - instruct-code - logical-reasoning - Platypus2 datasets: - garage-bAInd/Open-Platypus base_model: meta-llama/Llama-2-7b-hf --- We finetuned Meta-Llama/Llama-2-7b-hf on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). #### About OpenPlatypus Dataset OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0. The finetuning session got completed in 1 hour and 30 minutes and costed us only `$15` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: meta-llama/Llama-2-7b-hf - Dataset: garage-bAInd/Open-Platypus - Learning rate: 0.0002 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---

alexalbala/llam2test

alexalbala

2023-09-21T11:23:16Z

0

peft

[ "peft", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

2023-09-21T08:49:01Z

--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- ## Training procedure ### Framework versions - PEFT 0.5.0

monsterapi/OpenPlatypus_Falcon_7b

monsterapi

2023-09-21T11:23:15Z

2

0

peft

[ "peft", "tiiuae/falcon-7b", "code", "instruct", "instruct-code", "logical-reasoning", "Platypus2", "dataset:garage-bAInd/Open-Platypus", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]

null

2023-09-05T11:28:00Z

--- library_name: peft tags: - tiiuae/falcon-7b - code - instruct - instruct-code - logical-reasoning - Platypus2 datasets: - garage-bAInd/Open-Platypus base_model: codellama/CodeLlama-7b-hf --- We finetuned TIIUAE/Falcon-7B on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 3 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). #### About OpenPlatypus Dataset OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0. The finetuning session got completed in ~ 3 hrs and costed us only `$14` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: tiiuae/falcon-7b - Dataset: garage-bAInd/Open-Platypus - Learning rate: 0.0003 - Number of epochs: 3 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---