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remg1997/dynabench-sdxl10
remg1997
2023-09-08T05:56:16Z
37
1
diffusers
[ "diffusers", "onnx", "safetensors", "text-to-image", "stable-diffusion", "arxiv:2307.01952", "arxiv:2211.01324", "arxiv:2108.01073", "arxiv:2112.10752", "license:openrail++", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2023-09-07T23:08:36Z
--- license: openrail++ tags: - text-to-image - stable-diffusion duplicated_from: stabilityai/stable-diffusion-xl-base-1.0 --- # SD-XL 1.0-base Model Card ![row01](01.png) ## Model ![pipeline](pipeline.png) [SDXL](https://arxiv.org/abs/2307.01952) consists of an [ensemble of experts](https://arxiv.org/abs/2211.01324) pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module. Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. Source code is available at https://github.com/Stability-AI/generative-models . ### Model Description - **Developed by:** Stability AI - **Model type:** Diffusion-based text-to-image generative model - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)). - **Resources for more information:** Check out our [GitHub Repository](https://github.com/Stability-AI/generative-models) and the [SDXL report on arXiv](https://arxiv.org/abs/2307.01952). ### Model Sources For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. [Clipdrop](https://clipdrop.co/stable-diffusion) provides free SDXL inference. - **Repository:** https://github.com/Stability-AI/generative-models - **Demo:** https://clipdrop.co/stable-diffusion ## Evaluation ![comparison](comparison.png) The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. ### 🧨 Diffusers Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```py from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" images = pipe(prompt=prompt).images[0] ``` To use the whole base + refiner pipeline as an ensemble of experts you can run: ```py from diffusers import DiffusionPipeline import torch # load both base & refiner base = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) base.to("cuda") refiner = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-refiner-1.0", text_encoder_2=base.text_encoder_2, vae=base.vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16", ) refiner.to("cuda") # Define how many steps and what % of steps to be run on each experts (80/20) here n_steps = 40 high_noise_frac = 0.8 prompt = "A majestic lion jumping from a big stone at night" # run both experts image = base( prompt=prompt, num_inference_steps=n_steps, denoising_end=high_noise_frac, output_type="latent", ).images image = refiner( prompt=prompt, num_inference_steps=n_steps, denoising_start=high_noise_frac, image=image, ).images[0] ``` When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline: ```py pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) ``` If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload` instead of `.to("cuda")`: ```diff - pipe.to("cuda") + pipe.enable_model_cpu_offload() ``` For more information on how to use Stable Diffusion XL with `diffusers`, please have a look at [the Stable Diffusion XL Docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl). ### Optimum [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with both [OpenVINO](https://docs.openvino.ai/latest/index.html) and [ONNX Runtime](https://onnxruntime.ai/). #### OpenVINO To install Optimum with the dependencies required for OpenVINO : ```bash pip install optimum[openvino] ``` To load an OpenVINO model and run inference with OpenVINO Runtime, you need to replace `StableDiffusionXLPipeline` with Optimum `OVStableDiffusionXLPipeline`. In case you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, you can set `export=True`. ```diff - from diffusers import StableDiffusionPipeline + from optimum.intel import OVStableDiffusionPipeline model_id = "stabilityai/stable-diffusion-xl-base-1.0" - pipeline = StableDiffusionPipeline.from_pretrained(model_id) + pipeline = OVStableDiffusionPipeline.from_pretrained(model_id) prompt = "A majestic lion jumping from a big stone at night" image = pipeline(prompt).images[0] ``` You can find more examples (such as static reshaping and model compilation) in optimum [documentation](https://huggingface.co/docs/optimum/main/en/intel/inference#stable-diffusion-xl). #### ONNX To install Optimum with the dependencies required for ONNX Runtime inference : ```bash pip install optimum[onnxruntime] ``` To load an ONNX model and run inference with ONNX Runtime, you need to replace `StableDiffusionXLPipeline` with Optimum `ORTStableDiffusionXLPipeline`. In case you want to load a PyTorch model and convert it to the ONNX format on-the-fly, you can set `export=True`. ```diff - from diffusers import StableDiffusionPipeline + from optimum.onnxruntime import ORTStableDiffusionPipeline model_id = "stabilityai/stable-diffusion-xl-base-1.0" - pipeline = StableDiffusionPipeline.from_pretrained(model_id) + pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id) prompt = "A majestic lion jumping from a big stone at night" image = pipeline(prompt).images[0] ``` You can find more examples in optimum [documentation](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models#stable-diffusion-xl). ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
skk412/rrsk
skk412
2023-09-08T05:42:55Z
0
0
null
[ "arxiv:1910.09700", "region:us" ]
null
2023-09-08T05:40:05Z
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
nfliu/deberta-v3-large_boolq
nfliu
2023-09-08T05:40:57Z
209,595
2
transformers
[ "transformers", "pytorch", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "dataset:boolq", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T05:55:24Z
--- license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer datasets: - boolq metrics: - accuracy model-index: - name: deberta-v3-large_boolq results: - task: name: Text Classification type: text-classification dataset: name: boolq type: boolq config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8834862385321101 --- <!-- 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. --> # deberta-v3-large_boolq This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the boolq dataset. It achieves the following results on the evaluation set: - Loss: 0.4601 - Accuracy: 0.8835 ## Model description More information needed ## Example ``` import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("nfliu/deberta-v3-large_boolq") tokenizer = AutoTokenizer.from_pretrained("nfliu/deberta-v3-large_boolq") # Each example is a (question, context) pair. examples = [ ("Lake Tahoe is in California", "Lake Tahoe is a popular tourist spot in California."), ("Water is wet", "Contrary to popular belief, water is not wet.") ] encoded_input = tokenizer(examples, padding=True, truncation=True, return_tensors="pt") with torch.no_grad(): model_output = model(**encoded_input) probabilities = torch.softmax(model_output.logits, dim=-1).cpu().tolist() probability_no = [round(prob[0], 2) for prob in probabilities] probability_yes = [round(prob[1], 2) for prob in probabilities] for example, p_no, p_yes in zip(examples, probability_no, probability_yes): print(f"Question: {example[0]}") print(f"Context: {example[1]}") print(f"p(No | question, context): {p_no}") print(f"p(Yes | question, context): {p_yes}") print() ``` ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.85 | 250 | 0.5306 | 0.8823 | | 0.1151 | 1.69 | 500 | 0.4601 | 0.8835 | | 0.1151 | 2.54 | 750 | 0.5897 | 0.8792 | | 0.0656 | 3.39 | 1000 | 0.6477 | 0.8804 | | 0.0656 | 4.24 | 1250 | 0.6847 | 0.8838 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
sensenova/piccolo-base-zh
sensenova
2023-09-08T05:38:47Z
1,330
33
transformers
[ "transformers", "pytorch", "bert", "feature-extraction", "mteb", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2023-09-04T07:04:26Z
--- tags: - mteb model-index: - name: piccolo-base-zh results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 49.16558217326158 - type: cos_sim_spearman value: 51.4049475858823 - type: euclidean_pearson value: 49.85853741070363 - type: euclidean_spearman value: 51.501428092542234 - type: manhattan_pearson value: 49.746099634926296 - type: manhattan_spearman value: 51.41081804320127 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 52.385361699031854 - type: cos_sim_spearman value: 52.59114913702212 - type: euclidean_pearson value: 54.994530439418355 - type: euclidean_spearman value: 52.54102886188004 - type: manhattan_pearson value: 54.9503071669608 - type: manhattan_spearman value: 52.51465652540901 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.236 - type: f1 value: 39.43040092463147 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 60.98952187211432 - type: cos_sim_spearman value: 62.68189713123115 - type: euclidean_pearson value: 61.089426749761344 - type: euclidean_spearman value: 62.41743375544581 - type: manhattan_pearson value: 61.14747216341409 - type: manhattan_spearman value: 62.488918956547046 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 38.36392300667918 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 35.645927581489175 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 85.25085782849087 - type: mrr value: 87.77154761904762 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 86.15357754080844 - type: mrr value: 88.53547619047617 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 23.683 - type: map_at_10 value: 35.522999999999996 - type: map_at_100 value: 37.456 - type: map_at_1000 value: 37.576 - type: map_at_3 value: 31.584 - type: map_at_5 value: 33.684999999999995 - type: mrr_at_1 value: 36.459 - type: mrr_at_10 value: 44.534 - type: mrr_at_100 value: 45.6 - type: mrr_at_1000 value: 45.647 - type: mrr_at_3 value: 42.186 - type: mrr_at_5 value: 43.482 - type: ndcg_at_1 value: 36.459 - type: ndcg_at_10 value: 42.025 - type: ndcg_at_100 value: 49.754 - type: ndcg_at_1000 value: 51.815999999999995 - type: ndcg_at_3 value: 37.056 - type: ndcg_at_5 value: 38.962 - type: precision_at_1 value: 36.459 - type: precision_at_10 value: 9.485000000000001 - type: precision_at_100 value: 1.567 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 21.13 - type: precision_at_5 value: 15.209 - type: recall_at_1 value: 23.683 - type: recall_at_10 value: 52.190999999999995 - type: recall_at_100 value: 84.491 - type: recall_at_1000 value: 98.19600000000001 - type: recall_at_3 value: 37.09 - type: recall_at_5 value: 43.262 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 74.20324714371618 - type: cos_sim_ap value: 82.32631646194994 - type: cos_sim_f1 value: 76.64052827073876 - type: cos_sim_precision value: 68.58725761772854 - type: cos_sim_recall value: 86.83656768763151 - type: dot_accuracy value: 70.33072760072159 - type: dot_ap value: 77.46972172609794 - type: dot_f1 value: 73.6668924804026 - type: dot_precision value: 62.84676354029062 - type: dot_recall value: 88.98760813654431 - type: euclidean_accuracy value: 74.78051713770296 - type: euclidean_ap value: 82.65778389584023 - type: euclidean_f1 value: 77.1843623157445 - type: euclidean_precision value: 71.05211406096362 - type: euclidean_recall value: 84.47509936871639 - type: manhattan_accuracy value: 74.76849067949489 - type: manhattan_ap value: 82.55694030572194 - type: manhattan_f1 value: 77.1776459569154 - type: manhattan_precision value: 69.5423855963991 - type: manhattan_recall value: 86.69628244096329 - type: max_accuracy value: 74.78051713770296 - type: max_ap value: 82.65778389584023 - type: max_f1 value: 77.1843623157445 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 72.99799999999999 - type: map_at_10 value: 81.271 - type: map_at_100 value: 81.53399999999999 - type: map_at_1000 value: 81.535 - type: map_at_3 value: 80.049 - type: map_at_5 value: 80.793 - type: mrr_at_1 value: 73.13 - type: mrr_at_10 value: 81.193 - type: mrr_at_100 value: 81.463 - type: mrr_at_1000 value: 81.464 - type: mrr_at_3 value: 80.067 - type: mrr_at_5 value: 80.741 - type: ndcg_at_1 value: 73.34 - type: ndcg_at_10 value: 84.503 - type: ndcg_at_100 value: 85.643 - type: ndcg_at_1000 value: 85.693 - type: ndcg_at_3 value: 82.135 - type: ndcg_at_5 value: 83.401 - type: precision_at_1 value: 73.34 - type: precision_at_10 value: 9.536 - type: precision_at_100 value: 1.004 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 29.54 - type: precision_at_5 value: 18.398 - type: recall_at_1 value: 72.99799999999999 - type: recall_at_10 value: 94.31 - type: recall_at_100 value: 99.368 - type: recall_at_1000 value: 99.789 - type: recall_at_3 value: 87.935 - type: recall_at_5 value: 90.991 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.537 - type: map_at_10 value: 81.292 - type: map_at_100 value: 84.031 - type: map_at_1000 value: 84.066 - type: map_at_3 value: 56.571000000000005 - type: map_at_5 value: 71.082 - type: mrr_at_1 value: 91.2 - type: mrr_at_10 value: 93.893 - type: mrr_at_100 value: 93.955 - type: mrr_at_1000 value: 93.95700000000001 - type: mrr_at_3 value: 93.61699999999999 - type: mrr_at_5 value: 93.767 - type: ndcg_at_1 value: 91.2 - type: ndcg_at_10 value: 88.255 - type: ndcg_at_100 value: 90.813 - type: ndcg_at_1000 value: 91.144 - type: ndcg_at_3 value: 87.435 - type: ndcg_at_5 value: 85.961 - type: precision_at_1 value: 91.2 - type: precision_at_10 value: 42.14 - type: precision_at_100 value: 4.817 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 78.467 - type: precision_at_5 value: 65.75999999999999 - type: recall_at_1 value: 26.537 - type: recall_at_10 value: 89.262 - type: recall_at_100 value: 97.783 - type: recall_at_1000 value: 99.49799999999999 - type: recall_at_3 value: 58.573 - type: recall_at_5 value: 75.154 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 48.5 - type: map_at_10 value: 57.898 - type: map_at_100 value: 58.599000000000004 - type: map_at_1000 value: 58.616 - type: map_at_3 value: 55.1 - type: map_at_5 value: 56.80500000000001 - type: mrr_at_1 value: 48.5 - type: mrr_at_10 value: 57.898 - type: mrr_at_100 value: 58.599000000000004 - type: mrr_at_1000 value: 58.616 - type: mrr_at_3 value: 55.1 - type: mrr_at_5 value: 56.80500000000001 - type: ndcg_at_1 value: 48.5 - type: ndcg_at_10 value: 62.876 - type: ndcg_at_100 value: 66.00200000000001 - type: ndcg_at_1000 value: 66.467 - type: ndcg_at_3 value: 57.162 - type: ndcg_at_5 value: 60.263999999999996 - type: precision_at_1 value: 48.5 - type: precision_at_10 value: 7.870000000000001 - type: precision_at_100 value: 0.927 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 21.032999999999998 - type: precision_at_5 value: 14.14 - type: recall_at_1 value: 48.5 - type: recall_at_10 value: 78.7 - type: recall_at_100 value: 92.7 - type: recall_at_1000 value: 96.39999999999999 - type: recall_at_3 value: 63.1 - type: recall_at_5 value: 70.7 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 44.34782608695652 - type: f1 value: 36.401426200836205 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 84.25891181988743 - type: ap value: 50.54636280166089 - type: f1 value: 78.55080202541332 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 70.02878561337955 - type: cos_sim_spearman value: 75.39509553139982 - type: euclidean_pearson value: 73.92598696939956 - type: euclidean_spearman value: 75.5471147196853 - type: manhattan_pearson value: 73.88049486090739 - type: manhattan_spearman value: 75.51361990583285 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 64.739 - type: map_at_10 value: 74.039 - type: map_at_100 value: 74.38 - type: map_at_1000 value: 74.39099999999999 - type: map_at_3 value: 72.074 - type: map_at_5 value: 73.29299999999999 - type: mrr_at_1 value: 66.92 - type: mrr_at_10 value: 74.636 - type: mrr_at_100 value: 74.94 - type: mrr_at_1000 value: 74.95 - type: mrr_at_3 value: 72.911 - type: mrr_at_5 value: 73.981 - type: ndcg_at_1 value: 66.92 - type: ndcg_at_10 value: 77.924 - type: ndcg_at_100 value: 79.471 - type: ndcg_at_1000 value: 79.73400000000001 - type: ndcg_at_3 value: 74.17200000000001 - type: ndcg_at_5 value: 76.236 - type: precision_at_1 value: 66.92 - type: precision_at_10 value: 9.5 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 27.989000000000004 - type: precision_at_5 value: 17.874000000000002 - type: recall_at_1 value: 64.739 - type: recall_at_10 value: 89.324 - type: recall_at_100 value: 96.342 - type: recall_at_1000 value: 98.38900000000001 - type: recall_at_3 value: 79.378 - type: recall_at_5 value: 84.28099999999999 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.97108271687962 - type: f1 value: 66.8625981386677 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.32212508406187 - type: f1 value: 73.33875034670166 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 49.0 - type: map_at_10 value: 55.022999999999996 - type: map_at_100 value: 55.550999999999995 - type: map_at_1000 value: 55.608000000000004 - type: map_at_3 value: 53.417 - type: map_at_5 value: 54.372 - type: mrr_at_1 value: 49.3 - type: mrr_at_10 value: 55.176 - type: mrr_at_100 value: 55.703 - type: mrr_at_1000 value: 55.76 - type: mrr_at_3 value: 53.567 - type: mrr_at_5 value: 54.522000000000006 - type: ndcg_at_1 value: 49.0 - type: ndcg_at_10 value: 58.089999999999996 - type: ndcg_at_100 value: 60.988 - type: ndcg_at_1000 value: 62.580999999999996 - type: ndcg_at_3 value: 54.803000000000004 - type: ndcg_at_5 value: 56.508 - type: precision_at_1 value: 49.0 - type: precision_at_10 value: 6.78 - type: precision_at_100 value: 0.8210000000000001 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 19.6 - type: precision_at_5 value: 12.58 - type: recall_at_1 value: 49.0 - type: recall_at_10 value: 67.80000000000001 - type: recall_at_100 value: 82.1 - type: recall_at_1000 value: 94.8 - type: recall_at_3 value: 58.8 - type: recall_at_5 value: 62.9 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 28.87237408060796 - type: mrr value: 27.83015873015873 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 70.25 - type: f1 value: 70.29055400149645 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 65.56578234975636 - type: cos_sim_ap value: 70.89354058570412 - type: cos_sim_f1 value: 71.21024370095002 - type: cos_sim_precision value: 58.48032564450475 - type: cos_sim_recall value: 91.02428722280888 - type: dot_accuracy value: 64.86193827828912 - type: dot_ap value: 70.17697803463875 - type: dot_f1 value: 70.68676716917922 - type: dot_precision value: 58.57043719639139 - type: dot_recall value: 89.1235480464625 - type: euclidean_accuracy value: 64.86193827828912 - type: euclidean_ap value: 70.26847152773904 - type: euclidean_f1 value: 70.9984152139461 - type: euclidean_precision value: 56.81674064679771 - type: euclidean_recall value: 94.61457233368532 - type: manhattan_accuracy value: 65.40335679480238 - type: manhattan_ap value: 70.22941558736018 - type: manhattan_f1 value: 71.09712937475423 - type: manhattan_precision value: 56.64160401002506 - type: manhattan_recall value: 95.45934530095037 - type: max_accuracy value: 65.56578234975636 - type: max_ap value: 70.89354058570412 - type: max_f1 value: 71.21024370095002 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 89.92999999999999 - type: ap value: 87.16059195012956 - type: f1 value: 89.90917477839415 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 27.74161502387672 - type: cos_sim_spearman value: 31.58353529723325 - type: euclidean_pearson value: 32.43729673844635 - type: euclidean_spearman value: 31.59527486602242 - type: manhattan_pearson value: 32.37467059678786 - type: manhattan_spearman value: 31.44408004951894 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 36.233749845501194 - type: cos_sim_spearman value: 36.47808586229587 - type: euclidean_pearson value: 32.663447466546806 - type: euclidean_spearman value: 34.45830454037139 - type: manhattan_pearson value: 32.80239212096335 - type: manhattan_spearman value: 34.581060433895125 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: None metrics: - type: cos_sim_pearson value: 63.05131937664673 - type: cos_sim_spearman value: 66.51353746725948 - type: euclidean_pearson value: 61.24016998745561 - type: euclidean_spearman value: 66.07115266049276 - type: manhattan_pearson value: 64.55660243659054 - type: manhattan_spearman value: 66.80282149562386 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 70.45533692882996 - type: cos_sim_spearman value: 70.6045637565602 - type: euclidean_pearson value: 72.75588977483554 - type: euclidean_spearman value: 73.36630581886473 - type: manhattan_pearson value: 72.72517409326954 - type: manhattan_spearman value: 73.35358940437355 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 66.45779474032288 - type: mrr value: 76.0782192023729 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.458 - type: map_at_10 value: 74.355 - type: map_at_100 value: 78.158 - type: map_at_1000 value: 78.233 - type: map_at_3 value: 52.2 - type: map_at_5 value: 64.14 - type: mrr_at_1 value: 88.37 - type: mrr_at_10 value: 91.117 - type: mrr_at_100 value: 91.231 - type: mrr_at_1000 value: 91.23599999999999 - type: mrr_at_3 value: 90.645 - type: mrr_at_5 value: 90.948 - type: ndcg_at_1 value: 88.37 - type: ndcg_at_10 value: 82.384 - type: ndcg_at_100 value: 86.431 - type: ndcg_at_1000 value: 87.163 - type: ndcg_at_3 value: 83.993 - type: ndcg_at_5 value: 82.411 - type: precision_at_1 value: 88.37 - type: precision_at_10 value: 41.131 - type: precision_at_100 value: 4.9799999999999995 - type: precision_at_1000 value: 0.515 - type: precision_at_3 value: 73.651 - type: precision_at_5 value: 61.634 - type: recall_at_1 value: 26.458 - type: recall_at_10 value: 81.3 - type: recall_at_100 value: 94.342 - type: recall_at_1000 value: 98.103 - type: recall_at_3 value: 54.020999999999994 - type: recall_at_5 value: 67.781 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 46.814 - type: f1 value: 45.580027683507666 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 61.43613064816144 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 53.01838461793776 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 59.3 - type: map_at_10 value: 69.158 - type: map_at_100 value: 69.60300000000001 - type: map_at_1000 value: 69.611 - type: map_at_3 value: 67.467 - type: map_at_5 value: 68.432 - type: mrr_at_1 value: 59.199999999999996 - type: mrr_at_10 value: 69.108 - type: mrr_at_100 value: 69.553 - type: mrr_at_1000 value: 69.56099999999999 - type: mrr_at_3 value: 67.417 - type: mrr_at_5 value: 68.382 - type: ndcg_at_1 value: 59.3 - type: ndcg_at_10 value: 73.54 - type: ndcg_at_100 value: 75.652 - type: ndcg_at_1000 value: 75.868 - type: ndcg_at_3 value: 70.074 - type: ndcg_at_5 value: 71.808 - type: precision_at_1 value: 59.3 - type: precision_at_10 value: 8.709999999999999 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 25.867 - type: precision_at_5 value: 16.36 - type: recall_at_1 value: 59.3 - type: recall_at_10 value: 87.1 - type: recall_at_100 value: 96.89999999999999 - type: recall_at_1000 value: 98.6 - type: recall_at_3 value: 77.60000000000001 - type: recall_at_5 value: 81.8 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 84.69999999999999 - type: ap value: 66.65020528563207 - type: f1 value: 83.00542769081453 --- ## piccolo-base-zh piccolo是一个通用embedding模型(中文), 由来自商汤科技的通用模型组完成训练。piccolo借鉴了E5以及GTE的训练流程,采用了两阶段的训练方式。 在第一阶段中,我们搜集和爬取了4亿的中文文本对(可视为弱监督文本对数据),并采用二元组的softmax对比学习损失来优化模型。 在第二阶段中,我们搜集整理了2000万人工标注的中文文本对(精标数据),并采用带有难负样本的三元组的softmax对比学习损失来帮助模型更好地优化。 目前,我们提供了piccolo-base-zh和piccolo-large-zh两个模型。 piccolo is a general text embedding model(chinese), powered by General Model Group from SenseTime Research. Inspired from E5 and GTE, piccolo is trained using a two stage pipeline. On the first stage, we collect and crawl 400 million weakly supervised Chinese text pairs from the Internet, and train the model with the pair(text and text pos) softmax contrastive loss. On the second stage, we collect 20 million human labeled chinese text pairs dataset, and finetune the model with tiplet (text, text_pos, text_neg) contrastive loss. Currently here we offer two different sizes of models, including piccolo-base-zh, piccolo-large-zh. ## Metric 我们将piccolo与其他的开源embedding模型在CMTEB榜单上进行了比较,请参考CMTEB榜单。我们在eval文件夹中提供了复现结果的脚本。 We compared the performance of the piccolo with other embedding models on the C-MTEB benchmark. please refer to the C-MTEB leaderboard. we provide scripts in "eval" folder for results reproducing. | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**piccolo-large-zh**] | 0.65 | 1024 | 512 | **64.11** | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 | | [bge-large-zh]| 1.3 | 1024| 512 | 63.96 | 68.32 | 48.39 | 78.94 | 65.11 | 71.52 | 54.98 | | [**piccolo-base-zh**]| 0.2 | 768 | 512 | **63.66** | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 | | [bge-large-zh-no-instruct]| 1.3 | 1024 | 512 | 63.4 | 68.58 | 50.01 | 76.77 | 64.9 | 70.54 | 53 | | [bge-base-zh]| 0.41 | 768 | 512 | 62.8 | 67.07 | 47.64 | 77.5 | 64.91 | 69.53 | 54.12 | ## Usage 在sentence-transformer package中可以很容易地调用piccolo模型 ```python # for s2s dataset, you can use piccolo as below # 对于短对短数据集,下面是通用的使用方式 from sentence_transformers import SentenceTransformer sentences = ["数据1", "数据2"] model = SentenceTransformer('sensenova/piccolo-base-zh') embeddings_1 = model.encode(sentences, normalize_embeddings=True) embeddings_2 = model.encode(sentences, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p dataset, we recommend to add instruction for passage retrieval # 对于短对长数据集,我们推荐添加instruction,来帮助模型更好地进行检索。 from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["doc_1", "doc_2"] model = SentenceTransformer('sensenova/piccolo-base-zh') q_embeddings = model.encode(["查询:" + q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(["结果:" + p for p in passages], normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` ## Training Detail ### pretrain pretrain 通常不需要太大的max length, 推荐128。小的max length用以提高batch size,加快训练速度,从而适应大规模数据。 pretrain 损失我们采用二元组contrastive loss,不加入hard negative, 直接采用inbatch negative,在实际训练中,我们使用了32张40G A100进行训练,单卡的batch size为1024。 Pretrain usually does not require a large max length, and 128 is recommended. A small max length is used to increase batch size and speed up training to adapt to large-scale data. We use binary contrastive loss for pretrain loss, without adding hard negative, and directly use inbatch negative. In actual training, we used 32 40G A100 for training, and the batch size of a single card is 1024. ### finetune finetune 通常会将 max length扩增到512。用以适应更大长度的文本输入,finetune时会多sample S2P的数据,以增强模型在retrieval任务上的性能。 finetune 损失采用三元组contrastive loss,加入hard negative,neg num通常设置为2-7,loss计算方式可以参考GTE里的improved contrastive loss。 注意: 我们给query和passage设置了不同的max length,query的max length始终保持在64。 For finetuning, we usually expands the max length to 512. To adapt to larger length text input, finetune will sample more S2P data to enhance the performance of the model on retrieval tasks. The finetune loss uses triple contrastive loss, adding hard negative. Neg num is usually set to 2-7. The loss calculation method can refer to the improved contrastive loss in GTE. Note: We set different max lengths for query and passage, and the max length of query is always kept at 64. ### Others 一些有用的trick: 1. 减小显存的方式: fp16 + gradient checkpointing + ZERO STAGE1 (stage2 不支持双塔结构下的gradient checkpointing) 相关issue见: https://github.com/microsoft/DeepSpeed/issues/988 2. dataset sampler,我们采用了M3E的dataset sampler,用以保证每个batch里的样本均来自于一个dataset,负样本更有价值。 3. instruction。instruction在我们的实验中对retrieval任务有非常大的性能提升,我们在每个训练样本前都加入'查询: '和'结果: '这样的instruction。 some useful tricks: 1. The way to reduce memory usage: fp16 + gradient checkpointing + ZERO STAGE1 (stage2 does not support gradient checkpointing under the double-tower structure) For related issues, see: https://github.com/microsoft/DeepSpeed/issues/ 988 2. Dataset sampler, we use M3E's dataset sampler to ensure that the samples in each batch come from a dataset, and negative samples are more valuable. 3. instruction. Instruction has greatly improved the performance of the retrieval task in our experiments. We added instructions like 'query: ' and 'result: ' before each training sample. ## Reference 这里我们列出了我们参考过的embedding项目和论文 1. [M3E](https://github.com/wangyuxinwhy/uniem)。非常棒的中文开源embedding项目,收集和整理了较多的中文高质量数据集,uniem也是一个不错的框架。 2. [Text2vec](https://github.com/shibing624/text2vec)。另一个一个非常棒的中文开源embedding项目。 3. [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)。智源AI开源的embedding模型,收集和整理了CMTEB benchmark,填补了中文embedding系统性评测的空缺。 4. [E5](https://github.com/microsoft/unilm/tree/master/e5)。来自微软的一篇文章,有非常详细的消融实验以及数据处理过滤细节。 5. [GTE](https://huggingface.co/thenlper/gte-base)。一篇来自阿里达摩的embedding论文。 Here we list the embedding projects and papers we have referenced 1. [M3E](https://github.com/wangyuxinwhy/uniem). A great Chinese open source embedding project that collects and organizes a large number of high-quality Chinese datasets. Uniem is also a good framework. 2. [Text2vec](https://github.com/shibing624/text2vec). Another great Chinese open source embedding project. 3. [Flag Embedding](https://github.com/FlagOpen/FlagEmbedding). Zhiyuan AI’s open source embedding model.They collect and organize CMTEB benchmark, filling the gap in systematic evaluation of Chinese embeddings. 4. [E5](https://github.com/microsoft/unilm/tree/master/e5). Powerd by microsoft,producing very detailed ablation experiments and data processing filtering details. 5. [GTE](https://huggingface.co/thenlper/gte-base). An embedding paper from Alibaba Damo. ## License Piccolo 使用 MIT License,免费商用。 Piccolo use MIT License. It can be used for commercial purposes free of charge. ## Acknowledgement piccolo 由来自商汤科技研究院的通用模型组完成训练,[Jinkin](https://huggingface.co/Jinkin) 完成了代码实现和模型训练, [Jinkin](https://huggingface.co/Jinkin), [CCCCxxx](https://huggingface.co/CCCCxxx) 一起完成了数据搜集、整理和评测工作. 项目由 [Gaomengya](https://huggingface.co/gaomengya) 和 [chaorenwu111](https://huggingface.co/chaorenwu111) 主导。 同时,感谢[lux0933](https://huggingface.co/lux0933)以及[yangkai001](https://huggingface.co/yangkai001)的交流与讨论,提供了非常多有用的建议。 piccolo is powered by Genral Model group from SenseTime Research. [Jinkin](https://huggingface.co/Jinkin) complete code implementation and model training. [Jinkin](https://huggingface.co/Jinkin), [CCCCxxx](https://huggingface.co/CCCCxxx) completed the data collection、processing and model evaluation together. Project is led by [Gaomengya](https://huggingface.co/gaomengya) and [chaorenwu111](https://huggingface.co/chaorenwu111). At the same time, thank [lux0933](https://huggingface.co/lux0933) and [yangkai001](https://huggingface.co/yangkai001) for the discussion, which provide a lot of useful suggestions.
omthkkr/whisper-tiny-en-US
omthkkr
2023-09-08T05:28:42Z
76
0
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-08T04:36:36Z
--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-finetuned-en-US results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: MINDS14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.36363636363636365 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-finetuned-en-US This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the MINDS14 dataset. It achieves the following results on the evaluation set: - Loss: 0.7138 - Wer Ortho: 0.3652 - Wer: 0.3636 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0007 | 17.86 | 500 | 0.6509 | 0.3455 | 0.3412 | | 0.0002 | 35.71 | 1000 | 0.7138 | 0.3652 | 0.3636 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
jasmineplows/ppo-LunarLander-v2
jasmineplows
2023-09-08T04:55:08Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-08T04:54:47Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 237.96 +/- 38.11 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 ... ```
Onutoa/1_7e-3_1_0.9
Onutoa
2023-09-08T04:42:59Z
47
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-08T01:42:38Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: 1_7e-3_1_0.9 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. --> # 1_7e-3_1_0.9 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2572 - Accuracy: 0.7505 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.007 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0455 | 1.0 | 590 | 1.6132 | 0.3786 | | 0.9655 | 2.0 | 1180 | 0.6681 | 0.6217 | | 0.7392 | 3.0 | 1770 | 0.5308 | 0.4557 | | 0.7812 | 4.0 | 2360 | 0.4957 | 0.5654 | | 0.7422 | 5.0 | 2950 | 1.2018 | 0.6217 | | 0.7053 | 6.0 | 3540 | 0.7295 | 0.4804 | | 0.7016 | 7.0 | 4130 | 1.1783 | 0.3804 | | 0.6381 | 8.0 | 4720 | 0.3895 | 0.6541 | | 0.5364 | 9.0 | 5310 | 0.5057 | 0.6768 | | 0.5598 | 10.0 | 5900 | 0.3659 | 0.6798 | | 0.5779 | 11.0 | 6490 | 0.5754 | 0.6740 | | 0.4901 | 12.0 | 7080 | 0.3128 | 0.7055 | | 0.5212 | 13.0 | 7670 | 0.2977 | 0.7083 | | 0.479 | 14.0 | 8260 | 1.0718 | 0.6352 | | 0.4701 | 15.0 | 8850 | 0.4170 | 0.7138 | | 0.4286 | 16.0 | 9440 | 0.3207 | 0.6985 | | 0.4164 | 17.0 | 10030 | 0.2996 | 0.7086 | | 0.3649 | 18.0 | 10620 | 0.3665 | 0.6823 | | 0.4102 | 19.0 | 11210 | 0.2847 | 0.7300 | | 0.3819 | 20.0 | 11800 | 0.3577 | 0.6731 | | 0.3755 | 21.0 | 12390 | 0.5441 | 0.6058 | | 0.3373 | 22.0 | 12980 | 0.6394 | 0.5657 | | 0.3512 | 23.0 | 13570 | 0.2683 | 0.7159 | | 0.3124 | 24.0 | 14160 | 0.2775 | 0.7269 | | 0.3029 | 25.0 | 14750 | 0.3565 | 0.7333 | | 0.2864 | 26.0 | 15340 | 0.5595 | 0.6318 | | 0.3107 | 27.0 | 15930 | 0.8309 | 0.5557 | | 0.2674 | 28.0 | 16520 | 0.2615 | 0.7394 | | 0.2927 | 29.0 | 17110 | 0.6786 | 0.7049 | | 0.2672 | 30.0 | 17700 | 0.2945 | 0.7407 | | 0.2595 | 31.0 | 18290 | 0.3927 | 0.7327 | | 0.2646 | 32.0 | 18880 | 0.2765 | 0.7162 | | 0.2604 | 33.0 | 19470 | 0.2854 | 0.7199 | | 0.2364 | 34.0 | 20060 | 0.3032 | 0.7034 | | 0.2465 | 35.0 | 20650 | 0.3092 | 0.7456 | | 0.2334 | 36.0 | 21240 | 0.5941 | 0.7248 | | 0.2392 | 37.0 | 21830 | 0.3794 | 0.6875 | | 0.2303 | 38.0 | 22420 | 0.3033 | 0.7235 | | 0.2258 | 39.0 | 23010 | 0.3078 | 0.7266 | | 0.2189 | 40.0 | 23600 | 0.3052 | 0.7425 | | 0.2126 | 41.0 | 24190 | 0.3418 | 0.7352 | | 0.2213 | 42.0 | 24780 | 0.2660 | 0.7382 | | 0.2115 | 43.0 | 25370 | 0.4016 | 0.7364 | | 0.2109 | 44.0 | 25960 | 0.3010 | 0.7456 | | 0.2391 | 45.0 | 26550 | 0.4426 | 0.7303 | | 0.2115 | 46.0 | 27140 | 0.2762 | 0.7407 | | 0.2014 | 47.0 | 27730 | 0.2864 | 0.7437 | | 0.1925 | 48.0 | 28320 | 0.2657 | 0.7382 | | 0.2017 | 49.0 | 28910 | 0.2866 | 0.7505 | | 0.2145 | 50.0 | 29500 | 0.3055 | 0.7202 | | 0.1933 | 51.0 | 30090 | 0.5254 | 0.6550 | | 0.2115 | 52.0 | 30680 | 0.2996 | 0.7477 | | 0.1893 | 53.0 | 31270 | 0.2759 | 0.7471 | | 0.1834 | 54.0 | 31860 | 0.2543 | 0.7440 | | 0.1828 | 55.0 | 32450 | 0.2676 | 0.7492 | | 0.1801 | 56.0 | 33040 | 0.2680 | 0.7505 | | 0.1699 | 57.0 | 33630 | 0.2554 | 0.7440 | | 0.1748 | 58.0 | 34220 | 0.3117 | 0.7505 | | 0.1842 | 59.0 | 34810 | 0.3374 | 0.7483 | | 0.1684 | 60.0 | 35400 | 0.2781 | 0.7471 | | 0.1695 | 61.0 | 35990 | 0.3007 | 0.7434 | | 0.177 | 62.0 | 36580 | 0.2816 | 0.7443 | | 0.1586 | 63.0 | 37170 | 0.2587 | 0.7422 | | 0.1643 | 64.0 | 37760 | 0.2751 | 0.7450 | | 0.1719 | 65.0 | 38350 | 0.2875 | 0.7489 | | 0.167 | 66.0 | 38940 | 0.2729 | 0.7434 | | 0.1644 | 67.0 | 39530 | 0.2623 | 0.7373 | | 0.16 | 68.0 | 40120 | 0.2534 | 0.7407 | | 0.156 | 69.0 | 40710 | 0.2525 | 0.7419 | | 0.1549 | 70.0 | 41300 | 0.2565 | 0.7297 | | 0.1598 | 71.0 | 41890 | 0.2479 | 0.7425 | | 0.1666 | 72.0 | 42480 | 0.3158 | 0.7462 | | 0.1498 | 73.0 | 43070 | 0.2722 | 0.7456 | | 0.1495 | 74.0 | 43660 | 0.3985 | 0.7428 | | 0.153 | 75.0 | 44250 | 0.3153 | 0.7477 | | 0.1576 | 76.0 | 44840 | 0.3075 | 0.7459 | | 0.1536 | 77.0 | 45430 | 0.2629 | 0.7468 | | 0.1508 | 78.0 | 46020 | 0.2489 | 0.7434 | | 0.1502 | 79.0 | 46610 | 0.2671 | 0.7523 | | 0.1509 | 80.0 | 47200 | 0.2771 | 0.7523 | | 0.1352 | 81.0 | 47790 | 0.2611 | 0.7425 | | 0.1438 | 82.0 | 48380 | 0.2556 | 0.7388 | | 0.1407 | 83.0 | 48970 | 0.2809 | 0.7263 | | 0.1417 | 84.0 | 49560 | 0.2580 | 0.7459 | | 0.1404 | 85.0 | 50150 | 0.2557 | 0.7486 | | 0.1437 | 86.0 | 50740 | 0.2821 | 0.7498 | | 0.1368 | 87.0 | 51330 | 0.2766 | 0.7508 | | 0.14 | 88.0 | 51920 | 0.2664 | 0.7498 | | 0.1351 | 89.0 | 52510 | 0.2592 | 0.7450 | | 0.1338 | 90.0 | 53100 | 0.2895 | 0.7514 | | 0.1361 | 91.0 | 53690 | 0.2638 | 0.7526 | | 0.1356 | 92.0 | 54280 | 0.2470 | 0.7468 | | 0.1356 | 93.0 | 54870 | 0.2694 | 0.7511 | | 0.1349 | 94.0 | 55460 | 0.2833 | 0.7502 | | 0.1331 | 95.0 | 56050 | 0.2940 | 0.7477 | | 0.131 | 96.0 | 56640 | 0.2760 | 0.7492 | | 0.1311 | 97.0 | 57230 | 0.2520 | 0.7465 | | 0.1282 | 98.0 | 57820 | 0.2604 | 0.7489 | | 0.1258 | 99.0 | 58410 | 0.2518 | 0.7459 | | 0.1331 | 100.0 | 59000 | 0.2572 | 0.7505 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
rideadragon/ppo-Huggy
rideadragon
2023-09-08T04:40:25Z
10
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-09-08T04:40:21Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: rideadragon/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Gayathri142214002/Finetune_Pegasus_1
Gayathri142214002
2023-09-08T04:39:25Z
5
0
transformers
[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-07-18T05:19:37Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Finetune_Pegasus_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Finetune_Pegasus_1 This model is a fine-tuned version of [tuner007/pegasus_paraphrase](https://huggingface.co/tuner007/pegasus_paraphrase) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0942 ## Model description More information needed ## Intended uses & 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: 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7293 | 0.21 | 10 | 1.2156 | | 1.3661 | 0.41 | 20 | 1.1203 | | 1.3897 | 0.62 | 30 | 1.0665 | | 1.3356 | 0.82 | 40 | 1.0304 | | 1.171 | 1.03 | 50 | 1.0098 | | 0.8665 | 1.23 | 60 | 1.0062 | | 0.7864 | 1.44 | 70 | 1.0266 | | 0.8785 | 1.64 | 80 | 1.0190 | | 1.0596 | 1.85 | 90 | 1.0218 | | 1.0386 | 2.05 | 100 | 1.0213 | | 0.7452 | 2.26 | 110 | 1.0639 | | 0.6807 | 2.46 | 120 | 1.0619 | | 0.5764 | 2.67 | 130 | 1.0530 | | 0.87 | 2.87 | 140 | 1.0571 | | 0.7724 | 3.08 | 150 | 1.0563 | | 0.5847 | 3.28 | 160 | 1.0692 | | 0.6053 | 3.49 | 170 | 1.0652 | | 0.6416 | 3.69 | 180 | 1.0531 | | 0.6392 | 3.9 | 190 | 1.0416 | | 0.6138 | 4.1 | 200 | 1.0489 | | 0.6093 | 4.31 | 210 | 1.0668 | | 0.5484 | 4.51 | 220 | 1.0843 | | 0.6082 | 4.72 | 230 | 1.0771 | | 0.56 | 4.92 | 240 | 1.0745 | | 0.5796 | 5.13 | 250 | 1.0770 | | 0.6597 | 5.33 | 260 | 1.0722 | | 0.4834 | 5.54 | 270 | 1.0726 | | 0.4232 | 5.74 | 280 | 1.0682 | | 0.5432 | 5.95 | 290 | 1.0769 | | 0.5944 | 6.15 | 300 | 1.0851 | | 0.4663 | 6.36 | 310 | 1.0884 | | 0.4568 | 6.56 | 320 | 1.0915 | | 0.4565 | 6.77 | 330 | 1.0942 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
lightthief/tenoch
lightthief
2023-09-08T04:20:36Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-09-08T04:20:36Z
--- license: creativeml-openrail-m ---
yunhuan929/falcon_180b
yunhuan929
2023-09-08T04:00:07Z
33
0
transformers
[ "transformers", "safetensors", "falcon", "text-generation", "en", "de", "es", "fr", "dataset:tiiuae/falcon-refinedweb", "arxiv:1911.02150", "arxiv:2101.00027", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2205.14135", "arxiv:2306.01116", "license:unknown", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-09-08T03:58:21Z
--- datasets: - tiiuae/falcon-refinedweb language: - en - de - es - fr inference: false license: unknown extra_gated_heading: "Acknowledge license to access the repository" extra_gated_prompt: "You agree to the [Falcon-180B TII license](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/ACCEPTABLE_USE_POLICY.txt)." extra_gated_button_content: "I agree to the terms and conditions of the Falcon-180B TII license and to the acceptable use policy" --- # 🚀 Falcon-180B **Falcon-180B is a 180B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 3,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the [Falcon-180B TII License](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt) and [Acceptable Use Policy](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/ACCEPTABLE_USE_POLICY.txt).** *Paper coming soon* 😊 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost from HF](https://hf.co/blog/falcon-180b) or this [one](https://huggingface.co/blog/falcon) from the release of the 40B! Note that since the 180B is larger than what can easily be handled with `transformers`+`acccelerate`, we recommend using [Text Generation Inference](https://github.com/huggingface/text-generation-inference). You will need **at least 400GB of memory** to swiftly run inference with Falcon-180B. ## Why use Falcon-180B? * **It is the best open-access model currently available, and one of the best model overall.** Falcon-180B outperforms [LLaMA-2](https://huggingface.co/meta-llama/Llama-2-70b-hf), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * **It features an architecture optimized for inference**, with multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). * **It is made available under a permissive license allowing for commercial use**. * ⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-180B-Chat](https://huggingface.co/tiiuae/falcon-180b-chat). 💸 **Looking for a smaller, less expensive model?** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) are Falcon-180B's little brothers! 💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!** # Model Card for Falcon-180B ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish); - **License:** [Falcon-180B TII License](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) and [Acceptable Use Policy](https://huggingface.co/tiiuae/falcon-180B/blob/main/ACCEPTABLE_USE_POLICY.txt). ### Model Source - **Paper:** *coming soon*. ## Uses See the [acceptable use policy](https://huggingface.co/tiiuae/falcon-180B/blob/main/ACCEPTABLE_USE_POLICY.txt). ### Direct Use Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.) ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-180B is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-180B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use. ## How to Get Started with the Model To run inference with the model in full `bfloat16` precision you need approximately 8xA100 80GB or equivalent. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-180b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-180B was trained on 3,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)). | **Data source** | **Fraction** | **Tokens** | **Sources** | |--------------------|--------------|------------|-----------------------------------| | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 75% | 750B | massive web crawl | | RefinedWeb-Europe | 7% | 70B | European massive web crawl | | Books | 6% | 60B | | | Conversations | 5% | 50B | Reddit, StackOverflow, HackerNews | | Code | 5% | 50B | | | Technical | 2% | 20B | arXiv, PubMed, USPTO, etc. | RefinedWeb-Europe is made of the following languages: | **Language** | **Fraction of multilingual data** | **Tokens** | |--------------|-----------------------------------|------------| | German | 26% | 18B | | Spanish | 24% | 17B | | French | 23% | 16B | | _Italian_ | 7% | 5B | | _Portuguese_ | 4% | 3B | | _Polish_ | 4% | 3B | | _Dutch_ | 4% | 3B | | _Romanian_ | 3% | 2B | | _Czech_ | 3% | 2B | | _Swedish_ | 2% | 1B | The data was tokenized with the Falcon tokenizer. ### Training Procedure Falcon-180B was trained on up to 4,096 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=8, DP=64) combined with ZeRO. #### Training Hyperparameters | **Hyperparameter** | **Value** | **Comment** | |--------------------|------------|-------------------------------------------| | Precision | `bfloat16` | | | Optimizer | AdamW | | | Learning rate | 1.25e-4 | 4B tokens warm-up, cosine decay to 1.25e-5 | | Weight decay | 1e-1 | | | Z-loss | 1e-4 | | | Batch size | 2048 | 100B tokens ramp-up | #### Speeds, Sizes, Times Training started in early 2023. ## Evaluation *Paper coming soon.* See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. ## Technical Specifications ### Model Architecture and Objective Falcon-180B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * **Decoder-block:** parallel attention/MLP with two layer norms. For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree (so-called multigroup). | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 80 | | | `d_model` | 14848 | | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-180B was trained on AWS SageMaker, on up to 4,096 A100 40GB GPUs in P4d instances. #### Software Falcon-180B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation *Paper coming soon* 😊 (actually this time). In the meanwhile, you can use the following information to cite: ``` @article{falcon, title={The Falcon Series of Language Models: Towards Open Frontier Models}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Alhammadi, Maitha and Daniele, Mazzotta and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## Contact falconllm@tii.ae
guidoivetta/Peppa-Pig
guidoivetta
2023-09-08T03:55:51Z
57
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:DeepESP/gpt2-spanish", "base_model:finetune:DeepESP/gpt2-spanish", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-08T03:54:57Z
--- license: mit base_model: DeepESP/gpt2-spanish tags: - generated_from_trainer model-index: - name: Peppa-Pig 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. --> # Peppa-Pig This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8259 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:| | 0.9432 | 1.0 | 280 | 0.8825 | | 0.7771 | 2.0 | 560 | 0.8466 | | 0.6387 | 3.0 | 840 | 0.8293 | | 0.5383 | 4.0 | 1120 | 0.8249 | | 0.5661 | 5.0 | 1400 | 0.8259 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
DataBindu/swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes
DataBindu
2023-09-08T03:47:50Z
82
0
transformers
[ "transformers", "pytorch", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft", "base_model:finetune:microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-09-06T19:51:57Z
--- license: apache-2.0 base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7129629629629629 --- <!-- 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. --> # swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0311 - Accuracy: 0.7130 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.8445 | 0.98 | 15 | 2.8535 | 0.3194 | | 2.1358 | 1.97 | 30 | 1.9654 | 0.4491 | | 1.5947 | 2.95 | 45 | 1.4172 | 0.6204 | | 1.045 | 4.0 | 61 | 1.1698 | 0.6806 | | 0.985 | 4.98 | 76 | 1.1927 | 0.6852 | | 0.775 | 5.97 | 91 | 1.1012 | 0.6898 | | 0.7207 | 6.95 | 106 | 1.0311 | 0.7130 | | 0.6611 | 7.87 | 120 | 1.0311 | 0.6991 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3
Rebecca19990101/vicuna-7b-instruct-ft-adapters-chemical-v2
Rebecca19990101
2023-09-08T03:16:20Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-08T03:16:00Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
mmnga/cyberagent-open-calm-3b-gguf
mmnga
2023-09-08T03:09:01Z
271
0
null
[ "gguf", "gpt-neox", "ja", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]
null
2023-08-21T10:20:13Z
--- license: cc-by-sa-4.0 language: - ja tags: - gpt-neox --- # cyberagent-open-calm-3b-gguf [cyberagentさんが公開しているopen-calm-3b](https://huggingface.co/cyberagent/open-calm-3b)のggufフォーマット変換版です。 他モデルはこちら [mmnga/cyberagent-open-calm-7b-gguf](https://huggingface.co/mmnga/cyberagent-open-calm-7b-gguf) [mmnga/cyberagent-open-calm-3b-gguf](https://huggingface.co/mmnga/cyberagent-open-calm-3b-gguf) [mmnga/cyberagent-open-calm-1b-gguf](https://huggingface.co/mmnga/cyberagent-open-calm-1b-gguf) 注意:こちらはブランチで試用になります。llama.cpp本家にgptneoxが実装された時に、このggufファイルが使用できない可能性があります。 ***[GitHubリポジトリの readme はこちら](https://github.com/mmnga/llama.cpp/tree/mmnga-dev)*** ## Usage (試用) ``` git clone --branch mmnga-dev https://github.com/mmnga/llama.cpp.git cd llama.cpp make -j ./main -m 'cyberagent-open-calm-3b-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' --top_p 0.9 --temp 0.7 --repeat-penalty 1.1 ``` **CUBLAS** ``` LLAMA_CUBLAS=1 make -j ./main -m 'cyberagent-open-calm-3b-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' -ngl 32 ```
mmnga/cyberagent-open-calm-7b-gguf
mmnga
2023-09-08T03:08:46Z
372
2
null
[ "gguf", "gpt-neox", "ja", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]
null
2023-08-21T09:55:24Z
--- license: cc-by-sa-4.0 language: - ja tags: - gpt-neox --- # cyberagent-open-calm-7b-gguf [cyberagentさんが公開しているopen-calm-7b](https://huggingface.co/cyberagent/open-calm-7b)のggufフォーマット変換版です。 他モデルはこちら [mmnga/cyberagent-open-calm-7b-gguf](https://huggingface.co/mmnga/cyberagent-open-calm-7b-gguf) [mmnga/cyberagent-open-calm-3b-gguf](https://huggingface.co/mmnga/cyberagent-open-calm-3b-gguf) [mmnga/cyberagent-open-calm-1b-gguf](https://huggingface.co/mmnga/cyberagent-open-calm-1b-gguf) 注意:こちらはブランチで試用になります。llama.cpp本家にgptneoxが実装された時に、このggufファイルが使用できない可能性があります。 ***[GitHubリポジトリの readme はこちら](https://github.com/mmnga/llama.cpp/tree/mmnga-dev)*** ## Usage (試用) ``` git clone --branch mmnga-dev https://github.com/mmnga/llama.cpp.git cd llama.cpp make -j ./main -m 'cyberagent-open-calm-7b-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' --top_p 0.9 --temp 0.7 --repeat-penalty 1.1 ``` **CUBLAS** ``` LLAMA_CUBLAS=1 make -j ./main -m 'cyberagent-open-calm-7b-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' -ngl 40 ```
chgenly/q-FrozenLake-v1-4x4-noSlippery
chgenly
2023-09-08T03:06:58Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-09-08T03:06:56Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="chgenly/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
tmnam20/codellama_instruct_spider_e10
tmnam20
2023-09-08T02:59:22Z
0
0
null
[ "generated_from_trainer", "dataset:tmnam20/SpiderInstruct", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:finetune:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "region:us" ]
null
2023-09-05T15:37:12Z
--- license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - generated_from_trainer datasets: - tmnam20/SpiderInstruct model-index: - name: codellama_instruct_spider_e10 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. --> # codellama_instruct_spider_e10 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the tmnam20/SpiderInstruct dataset. It achieves the following results on the evaluation set: - Loss: 0.2393 ## Model description More information needed ## Intended uses & 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.822 | 0.37 | 100 | 0.5313 | | 0.3014 | 0.74 | 200 | 0.2763 | | 0.2091 | 1.11 | 300 | 0.2469 | | 0.1697 | 1.48 | 400 | 0.2401 | | 0.1495 | 1.85 | 500 | 0.2395 | | 0.1256 | 2.22 | 600 | 0.2525 | | 0.1097 | 2.59 | 700 | 0.2641 | | 0.1107 | 2.96 | 800 | 0.2617 | | 0.0951 | 3.33 | 900 | 0.2683 | | 0.0882 | 3.7 | 1000 | 0.2892 | | 0.0818 | 4.06 | 1100 | 0.3134 | | 0.075 | 4.43 | 1200 | 0.2978 | | 0.0745 | 4.8 | 1300 | 0.3095 | | 0.0642 | 5.17 | 1400 | 0.3261 | | 0.0622 | 5.54 | 1500 | 0.3201 | | 0.0573 | 5.91 | 1600 | 0.3343 | | 0.0552 | 6.28 | 1700 | 0.3396 | | 0.0523 | 6.65 | 1800 | 0.3602 | | 0.0538 | 7.02 | 1900 | 0.3464 | | 0.0467 | 7.39 | 2000 | 0.3622 | | 0.0465 | 7.76 | 2100 | 0.3697 | | 0.044 | 8.13 | 2200 | 0.3890 | | 0.043 | 8.5 | 2300 | 0.3785 | | 0.0375 | 8.87 | 2400 | 0.3860 | | 0.0384 | 9.24 | 2500 | 0.3952 | | 0.0363 | 9.61 | 2600 | 0.3940 | | 0.0352 | 9.98 | 2700 | 0.3985 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
thainq107/bert-base-banking77-pt2
thainq107
2023-09-08T02:58:57Z
68
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:banking77", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T16:32:14Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - banking77 metrics: - f1 model-index: - name: bert-base-banking77-pt2 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 config: default split: test args: default metrics: - name: F1 type: f1 value: 0.9311734343722707 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-banking77-pt2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.2844 - F1: 0.9312 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.2215 | 1.0 | 313 | 1.1811 | 0.7646 | | 0.6252 | 2.0 | 626 | 0.4665 | 0.9120 | | 0.3323 | 3.0 | 939 | 0.3294 | 0.9281 | | 0.1446 | 4.0 | 1252 | 0.3051 | 0.9267 | | 0.0994 | 5.0 | 1565 | 0.2844 | 0.9312 | ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.1 - Datasets 2.9.0 - Tokenizers 0.13.3
BiaDd/emotion-model
BiaDd
2023-09-08T02:56:47Z
54
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
2023-09-08T02:36:31Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: emotion-model results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9335 - name: F1 type: f1 value: 0.9336283314309987 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion-model 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: 0.2131 - Accuracy: 0.9335 - F1: 0.9336 ## Model description Predicts the emotions of provided text ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64453722e1fd8d65b2798cb2/5ay2OdQL2twuLpLS3PQF1.png) ## Intended uses & limitations For sentiment analysis ## Training and evaluation data Data from "emotion" dataset ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3058 | 0.9125 | 0.9098 | | 0.5417 | 2.0 | 500 | 0.2131 | 0.9335 | 0.9336 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
mmnga/line-corp-japanese-large-lm-3.6b-gguf
mmnga
2023-09-08T02:53:05Z
60
0
null
[ "gguf", "ja", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-09-02T18:18:41Z
--- license: apache-2.0 language: - ja --- # line-corporation/japanese-large-lm-3.6b [line-corporationさんが公開しているjapanese-large-lm-3.6b](https://huggingface.co/line-corporation/japanese-large-lm-3.6b)のgguf変換版です。 他モデルはこちら GPT-NEOX [mmnga/line-corp-japanese-large-lm-3.6b-gguf](https://huggingface.co/mmnga/line-corp-japanese-large-lm-3.6b-gguf) [mmnga/line-corp-japanese-large-lm-3.6b-instruction-sft-gguf](https://huggingface.co/mmnga/line-corp-japanese-large-lm-3.6b-instruction-sft-gguf) GPT-2 [mmnga/line-corp-japanese-large-lm-1.7b-gguf](https://huggingface.co/mmnga/line-corp-japanese-large-lm-1.7b-gguf) [mmnga/line-corp-japanese-large-lm-1.7b-instruction-sft-gguf](https://huggingface.co/mmnga/line-corp-japanese-large-lm-1.7b-instruction-sft-gguf) *注意:こちらはブランチで試用になります。llama.cpp本家にgptneox, gpt2が実装された時に、このggufファイルが使用できない可能性があります。* ***[GitHubリポジトリの readme はこちら](https://github.com/mmnga/llama.cpp/tree/mmnga-dev)*** ## Usage (試用) ``` git clone --branch mmnga-dev https://github.com/mmnga/llama.cpp.git cd llama.cpp make -j ./main -m 'line-corp-japanese-large-lm-3.6b-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' --top_p 0.9 --temp 0.7 --repeat-penalty 1.1 ``` **CUBLAS** ``` LLAMA_CUBLAS=1 make -j ./main -m 'line-corp-japanese-large-lm-3.6b-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' -ngl 32 ``` **従来のCPU実行** ~~~~bash git clone --branch mmnga-dev https://github.com/mmnga/llama.cpp.git cd llama.cpp make -j gptneox ./gptneox -m 'line-corp-japanese-large-lm-3.6b-q4_0.gguf' -n 128 -p 'ユーザー: 吾輩って猫ですか? システム: ' ~~~~
mmnga/line-corp-japanese-large-lm-3.6b-instruction-sft-gguf
mmnga
2023-09-08T02:52:29Z
89
2
null
[ "gguf", "ja", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-09-02T18:01:40Z
--- license: apache-2.0 language: - ja --- # line-corporation/japanese-large-lm-3.6b-instruction-sft [line-corporationさんが公開しているjapanese-large-lm-3.6b-instruction-sft](https://huggingface.co/line-corporation/japanese-large-lm-3.6b-instruction-sft)のgguf変換版です。 他モデルはこちら GPT-NEOX [mmnga/line-corp-japanese-large-lm-3.6b-gguf](https://huggingface.co/mmnga/line-corp-japanese-large-lm-3.6b-gguf) [mmnga/line-corp-japanese-large-lm-3.6b-instruction-sft-gguf](https://huggingface.co/mmnga/line-corp-japanese-large-lm-3.6b-instruction-sft-gguf) GPT-2 [mmnga/line-corp-japanese-large-lm-1.7b-gguf](https://huggingface.co/mmnga/line-corp-japanese-large-lm-1.7b-gguf) [mmnga/line-corp-japanese-large-lm-1.7b-instruction-sft-gguf](https://huggingface.co/mmnga/line-corp-japanese-large-lm-1.7b-instruction-sft-gguf) *注意:こちらはブランチで試用になります。llama.cpp本家にgptneox, gpt2が実装された時に、このggufファイルが使用できない可能性があります。* ***[GitHubリポジトリの readme はこちら](https://github.com/mmnga/llama.cpp/tree/mmnga-dev)*** ## Usage (試用) ``` git clone --branch mmnga-dev https://github.com/mmnga/llama.cpp.git cd llama.cpp make -j ./main -m 'line-corp-japanese-large-lm-3.6b-instruction-sft-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' --top_p 0.9 --temp 0.7 --repeat-penalty 1.1 ``` **CUBLAS** ``` LLAMA_CUBLAS=1 make -j ./main -m 'line-corp-japanese-large-lm-3.6b-instruction-sft-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' -ngl 32 ``` **従来のCPU実行** ~~~~bash git clone --branch mmnga-dev https://github.com/mmnga/llama.cpp.git cd llama.cpp make -j gptneox ./gptneox -m 'line-corp-japanese-large-lm-3.6b-instruction-sft-q4_0.gguf' -n 128 -p 'ユーザー: 吾輩って猫ですか? システム: ' ~~~~
kasperchen/Reinforce-CartPole-v1
kasperchen
2023-09-08T02:51:59Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-08-16T05:30:02Z
--- 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
hw2942/chinese-macbert-base-SSEC
hw2942
2023-09-08T02:46:03Z
49
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:hfl/chinese-macbert-base", "base_model:finetune:hfl/chinese-macbert-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-08T02:39:46Z
--- license: apache-2.0 base_model: hfl/chinese-macbert-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: chinese-macbert-base-wallstreetcn-morning-news-market-overview-SSEC-v6 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. --> # chinese-macbert-base-wallstreetcn-morning-news-market-overview-SSEC-v6 This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co/hfl/chinese-macbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4847 - Accuracy: 0.7188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 34 | 0.6893 | 0.4375 | | No log | 2.0 | 68 | 0.6156 | 0.6562 | | No log | 3.0 | 102 | 0.8698 | 0.6562 | | No log | 4.0 | 136 | 0.6379 | 0.6562 | | No log | 5.0 | 170 | 0.8517 | 0.7188 | | No log | 6.0 | 204 | 1.1949 | 0.6875 | | No log | 7.0 | 238 | 1.2695 | 0.6875 | | No log | 8.0 | 272 | 1.3954 | 0.7188 | | No log | 9.0 | 306 | 1.5019 | 0.6875 | | No log | 10.0 | 340 | 1.4847 | 0.7188 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Onutoa/1_9e-3_5_0.5
Onutoa
2023-09-08T02:43:57Z
46
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T23:43:56Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: 1_9e-3_5_0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 1_9e-3_5_0.5 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8603 - Accuracy: 0.7489 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.009 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.7616 | 1.0 | 590 | 2.7583 | 0.3798 | | 2.2507 | 2.0 | 1180 | 1.8432 | 0.6294 | | 2.5953 | 3.0 | 1770 | 3.4928 | 0.4532 | | 2.3305 | 4.0 | 2360 | 1.5737 | 0.6486 | | 1.9577 | 5.0 | 2950 | 2.6604 | 0.6263 | | 1.7557 | 6.0 | 3540 | 1.2734 | 0.6761 | | 1.6227 | 7.0 | 4130 | 3.4140 | 0.5119 | | 1.4961 | 8.0 | 4720 | 1.2029 | 0.7043 | | 1.3331 | 9.0 | 5310 | 1.2170 | 0.7092 | | 1.3007 | 10.0 | 5900 | 1.7625 | 0.6725 | | 1.2049 | 11.0 | 6490 | 1.0667 | 0.7070 | | 1.1087 | 12.0 | 7080 | 0.9915 | 0.7156 | | 1.1023 | 13.0 | 7670 | 1.0683 | 0.6924 | | 1.0404 | 14.0 | 8260 | 1.1711 | 0.7248 | | 1.0287 | 15.0 | 8850 | 1.0966 | 0.7297 | | 0.9405 | 16.0 | 9440 | 0.9352 | 0.7107 | | 0.8558 | 17.0 | 10030 | 0.9269 | 0.7205 | | 0.8273 | 18.0 | 10620 | 0.9574 | 0.7235 | | 0.7798 | 19.0 | 11210 | 0.9598 | 0.7385 | | 0.7646 | 20.0 | 11800 | 0.9004 | 0.7287 | | 0.7505 | 21.0 | 12390 | 0.9389 | 0.7174 | | 0.7273 | 22.0 | 12980 | 0.9234 | 0.7358 | | 0.6971 | 23.0 | 13570 | 0.9055 | 0.7315 | | 0.6815 | 24.0 | 14160 | 0.8711 | 0.7352 | | 0.6729 | 25.0 | 14750 | 1.0923 | 0.7437 | | 0.6151 | 26.0 | 15340 | 0.8950 | 0.7254 | | 0.6291 | 27.0 | 15930 | 1.1086 | 0.6945 | | 0.6243 | 28.0 | 16520 | 0.9179 | 0.7410 | | 0.609 | 29.0 | 17110 | 1.0778 | 0.7410 | | 0.5733 | 30.0 | 17700 | 0.9548 | 0.7422 | | 0.5742 | 31.0 | 18290 | 1.1436 | 0.7413 | | 0.5675 | 32.0 | 18880 | 0.8956 | 0.7450 | | 0.5578 | 33.0 | 19470 | 0.9040 | 0.7382 | | 0.5339 | 34.0 | 20060 | 0.8730 | 0.7453 | | 0.5284 | 35.0 | 20650 | 1.0258 | 0.7486 | | 0.5116 | 36.0 | 21240 | 1.2775 | 0.7382 | | 0.5215 | 37.0 | 21830 | 0.9275 | 0.7477 | | 0.5038 | 38.0 | 22420 | 0.8780 | 0.7394 | | 0.5073 | 39.0 | 23010 | 0.9095 | 0.7468 | | 0.4897 | 40.0 | 23600 | 0.8864 | 0.7410 | | 0.4927 | 41.0 | 24190 | 1.1312 | 0.7391 | | 0.4941 | 42.0 | 24780 | 0.8809 | 0.7339 | | 0.4629 | 43.0 | 25370 | 1.1564 | 0.7419 | | 0.4754 | 44.0 | 25960 | 0.9223 | 0.7413 | | 0.457 | 45.0 | 26550 | 0.8677 | 0.7422 | | 0.4398 | 46.0 | 27140 | 1.0571 | 0.7471 | | 0.4612 | 47.0 | 27730 | 0.8773 | 0.7401 | | 0.4464 | 48.0 | 28320 | 0.9260 | 0.7477 | | 0.4779 | 49.0 | 28910 | 0.8712 | 0.7425 | | 0.443 | 50.0 | 29500 | 0.8886 | 0.7413 | | 0.4445 | 51.0 | 30090 | 0.8968 | 0.7431 | | 0.4274 | 52.0 | 30680 | 0.9516 | 0.7495 | | 0.4239 | 53.0 | 31270 | 0.8773 | 0.7443 | | 0.4143 | 54.0 | 31860 | 1.0295 | 0.7401 | | 0.4359 | 55.0 | 32450 | 0.8879 | 0.7453 | | 0.4197 | 56.0 | 33040 | 0.8712 | 0.7489 | | 0.397 | 57.0 | 33630 | 1.0037 | 0.7544 | | 0.402 | 58.0 | 34220 | 0.8789 | 0.7554 | | 0.4015 | 59.0 | 34810 | 0.8532 | 0.7523 | | 0.4008 | 60.0 | 35400 | 0.8840 | 0.7523 | | 0.3943 | 61.0 | 35990 | 0.9475 | 0.7462 | | 0.3968 | 62.0 | 36580 | 0.9413 | 0.7465 | | 0.394 | 63.0 | 37170 | 0.8878 | 0.7480 | | 0.3914 | 64.0 | 37760 | 0.8737 | 0.7511 | | 0.3959 | 65.0 | 38350 | 0.8553 | 0.7486 | | 0.3881 | 66.0 | 38940 | 0.8905 | 0.7495 | | 0.379 | 67.0 | 39530 | 0.8956 | 0.7489 | | 0.3821 | 68.0 | 40120 | 0.8711 | 0.7514 | | 0.3764 | 69.0 | 40710 | 0.9552 | 0.7557 | | 0.3841 | 70.0 | 41300 | 0.9638 | 0.7523 | | 0.3758 | 71.0 | 41890 | 0.8728 | 0.7453 | | 0.376 | 72.0 | 42480 | 0.9654 | 0.7450 | | 0.364 | 73.0 | 43070 | 1.0121 | 0.7477 | | 0.3567 | 74.0 | 43660 | 1.0070 | 0.7508 | | 0.3723 | 75.0 | 44250 | 0.9271 | 0.7508 | | 0.3673 | 76.0 | 44840 | 0.8824 | 0.7450 | | 0.3656 | 77.0 | 45430 | 0.8812 | 0.7477 | | 0.3722 | 78.0 | 46020 | 0.8728 | 0.7502 | | 0.3719 | 79.0 | 46610 | 0.8551 | 0.7465 | | 0.3502 | 80.0 | 47200 | 0.8913 | 0.7523 | | 0.3467 | 81.0 | 47790 | 0.8476 | 0.7489 | | 0.348 | 82.0 | 48380 | 0.8885 | 0.7517 | | 0.3498 | 83.0 | 48970 | 0.8690 | 0.7443 | | 0.3457 | 84.0 | 49560 | 0.8824 | 0.7480 | | 0.3463 | 85.0 | 50150 | 0.8450 | 0.7453 | | 0.3465 | 86.0 | 50740 | 0.8760 | 0.7459 | | 0.3418 | 87.0 | 51330 | 0.8702 | 0.7437 | | 0.3394 | 88.0 | 51920 | 0.8782 | 0.7434 | | 0.3371 | 89.0 | 52510 | 0.8950 | 0.7474 | | 0.3309 | 90.0 | 53100 | 0.8568 | 0.7398 | | 0.3321 | 91.0 | 53690 | 0.8973 | 0.7495 | | 0.3385 | 92.0 | 54280 | 0.8401 | 0.7431 | | 0.3264 | 93.0 | 54870 | 0.8658 | 0.7462 | | 0.3382 | 94.0 | 55460 | 0.8652 | 0.7483 | | 0.3279 | 95.0 | 56050 | 0.8785 | 0.7465 | | 0.3274 | 96.0 | 56640 | 0.8666 | 0.7477 | | 0.3272 | 97.0 | 57230 | 0.8666 | 0.7489 | | 0.3147 | 98.0 | 57820 | 0.8641 | 0.7498 | | 0.3172 | 99.0 | 58410 | 0.8616 | 0.7486 | | 0.3256 | 100.0 | 59000 | 0.8603 | 0.7489 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
MochaPixel/MagicMix
MochaPixel
2023-09-08T02:37:17Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-05-05T09:51:55Z
--- license: creativeml-openrail-m ---
fastbond/llama-2-7b-guanaco-viggo-long1
fastbond
2023-09-08T02:28:19Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-08T02:28:12Z
--- 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
osieosie/bloom-mnli-4bit-1b7-bnb-seed65
osieosie
2023-09-08T02:27:08Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-07T06:29:21Z
--- 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: fp4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
pamixsun/swinv2_tiny_for_glaucoma_classification
pamixsun
2023-09-08T02:23:00Z
101
3
transformers
[ "transformers", "pytorch", "safetensors", "swinv2", "image-classification", "vision", "fundus", "glaucoma", "REFUGE", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-06-14T01:30:31Z
--- license: apache-2.0 tags: - image-classification - vision - fundus - glaucoma - REFUGE widget: - src: >- https://huggingface.co/pamixsun/swinv2_tiny_for_glaucoma_classification/resolve/main/example.jpg example_title: fundus image --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This model utilizes a Swin Transformer architecture and has undergone supervised fine-tuning on retinal fundus images from the [REFUGE challenge dataset](https://refuge.grand-challenge.org/). It is specialized in automated analysis of retinal fundus photographs for glaucoma detection. By extracting hierarchical visual features from input fundus images in a cross-scale manner, the model is able to effectively categorize each image as either glaucoma or non-glaucoma. Extensive experiments demonstrate that this model architecture achieves state-of-the-art performance on the REFUGE benchmark for fundus image-based glaucoma classification. To obtain optimal predictions, it is recommended to provide this model with standardized retinal fundus photographs captured using typical fundus imaging protocols. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [Xu Sun](https://pamixsun.github.io) - **Shared by:** [Xu Sun](https://pamixsun.github.io) - **Model type:** Image classification - **License:** Apache-2.0 ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> The pretrained model provides glaucoma classification functionality solely based on analysis of retinal fundus images. You may directly utilize the raw model without modification to categorize fundus images as either glaucoma or non-glaucoma. This model is specialized in extracting discriminative features from fundus images to identify glaucoma manifestations. However, to achieve optimal performance, it is highly recommended to fine-tune the model on a representative fundus image dataset prior to deployment in real-world applications. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> The model is specialized in analyzing retinal fundus images, and is trained exclusively on fundus image datasets to classify images as glaucoma or non-glaucoma. Therefore, to obtain accurate predictions, it is crucial to only input fundus images when using this model. Feeding other types of images may lead to meaningless results. In summary, this model expects fundus images as input for glaucoma classification. For the best performance, please adhere strictly to this input specification. ## How to Get Started with the Model Use the code below to get started with the model. ```python import cv2 import torch from transformers import AutoImageProcessor, Swinv2ForImageClassification image = cv2.imread('./example.jpg') image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) processor = AutoImageProcessor.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification") model = Swinv2ForImageClassification.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification") inputs = processor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts either glaucoma or non-glaucoma. predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) ``` ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Model Card Contact - pamixsun@gmail.com
pypy/blip2-opt-2.7b-pokemon
pypy
2023-09-08T02:22:03Z
2
0
peft
[ "peft", "region:us" ]
null
2023-09-08T02:21:56Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - 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
wangrongsheng/Generate-News-Title-7b-chat
wangrongsheng
2023-09-08T01:57:14Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-08T01:56:20Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
wangrongsheng/Generate-News-Abstract-7b-chat
wangrongsheng
2023-09-08T01:50:47Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-07T11:08:11Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
Onutoa/1_7e-3_10_0.5
Onutoa
2023-09-08T01:42:08Z
39
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T22:41:17Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: 1_7e-3_10_0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 1_7e-3_10_0.5 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9382 - Accuracy: 0.7557 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.007 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.7912 | 1.0 | 590 | 2.5545 | 0.3872 | | 3.233 | 2.0 | 1180 | 2.8480 | 0.6217 | | 2.7249 | 3.0 | 1770 | 2.7584 | 0.4037 | | 2.5026 | 4.0 | 2360 | 1.8755 | 0.6113 | | 2.235 | 5.0 | 2950 | 1.6668 | 0.6661 | | 1.9303 | 6.0 | 3540 | 1.6441 | 0.6346 | | 1.9491 | 7.0 | 4130 | 2.1352 | 0.5789 | | 1.6294 | 8.0 | 4720 | 2.2811 | 0.6572 | | 1.6591 | 9.0 | 5310 | 1.5834 | 0.6896 | | 1.5251 | 10.0 | 5900 | 1.7600 | 0.6716 | | 1.5112 | 11.0 | 6490 | 1.2400 | 0.6905 | | 1.3972 | 12.0 | 7080 | 1.2023 | 0.7165 | | 1.3804 | 13.0 | 7670 | 1.1972 | 0.7009 | | 1.3085 | 14.0 | 8260 | 1.6154 | 0.7101 | | 1.2559 | 15.0 | 8850 | 1.1741 | 0.7 | | 1.2292 | 16.0 | 9440 | 1.1551 | 0.7028 | | 1.1711 | 17.0 | 10030 | 1.9400 | 0.6242 | | 1.1356 | 18.0 | 10620 | 1.1234 | 0.7165 | | 1.0466 | 19.0 | 11210 | 1.0939 | 0.7312 | | 1.1043 | 20.0 | 11800 | 1.2564 | 0.7183 | | 0.9875 | 21.0 | 12390 | 1.1273 | 0.7135 | | 0.9788 | 22.0 | 12980 | 1.0513 | 0.7187 | | 0.9086 | 23.0 | 13570 | 1.0497 | 0.7312 | | 0.9327 | 24.0 | 14160 | 1.1127 | 0.7046 | | 0.8835 | 25.0 | 14750 | 1.3732 | 0.7235 | | 0.8652 | 26.0 | 15340 | 1.6447 | 0.6511 | | 0.843 | 27.0 | 15930 | 1.1686 | 0.7425 | | 0.8072 | 28.0 | 16520 | 1.0110 | 0.7446 | | 0.7735 | 29.0 | 17110 | 1.1610 | 0.7401 | | 0.7717 | 30.0 | 17700 | 0.9851 | 0.7352 | | 0.7746 | 31.0 | 18290 | 1.4960 | 0.7223 | | 0.7439 | 32.0 | 18880 | 0.9772 | 0.7358 | | 0.7534 | 33.0 | 19470 | 1.0034 | 0.7456 | | 0.6874 | 34.0 | 20060 | 0.9894 | 0.7407 | | 0.6877 | 35.0 | 20650 | 1.4460 | 0.6771 | | 0.6816 | 36.0 | 21240 | 1.0221 | 0.7489 | | 0.7158 | 37.0 | 21830 | 1.3579 | 0.7425 | | 0.6694 | 38.0 | 22420 | 1.1472 | 0.7517 | | 0.6586 | 39.0 | 23010 | 1.0499 | 0.7523 | | 0.6418 | 40.0 | 23600 | 1.0344 | 0.7459 | | 0.6366 | 41.0 | 24190 | 1.2582 | 0.7422 | | 0.6289 | 42.0 | 24780 | 0.9833 | 0.7370 | | 0.6065 | 43.0 | 25370 | 1.0209 | 0.7529 | | 0.6053 | 44.0 | 25960 | 1.0147 | 0.7287 | | 0.5958 | 45.0 | 26550 | 0.9454 | 0.7456 | | 0.5637 | 46.0 | 27140 | 0.9789 | 0.7535 | | 0.5818 | 47.0 | 27730 | 1.0014 | 0.7529 | | 0.5743 | 48.0 | 28320 | 0.9380 | 0.7526 | | 0.592 | 49.0 | 28910 | 0.9494 | 0.7385 | | 0.5591 | 50.0 | 29500 | 0.9728 | 0.7523 | | 0.5431 | 51.0 | 30090 | 0.9528 | 0.7502 | | 0.5537 | 52.0 | 30680 | 0.9995 | 0.7410 | | 0.5444 | 53.0 | 31270 | 0.9815 | 0.7538 | | 0.5372 | 54.0 | 31860 | 0.9556 | 0.7517 | | 0.5491 | 55.0 | 32450 | 0.9824 | 0.7459 | | 0.5294 | 56.0 | 33040 | 0.9625 | 0.7391 | | 0.5074 | 57.0 | 33630 | 0.9761 | 0.7538 | | 0.5127 | 58.0 | 34220 | 1.1065 | 0.7587 | | 0.5095 | 59.0 | 34810 | 0.9373 | 0.7434 | | 0.5079 | 60.0 | 35400 | 0.9822 | 0.7532 | | 0.4886 | 61.0 | 35990 | 1.0654 | 0.7627 | | 0.5143 | 62.0 | 36580 | 0.9688 | 0.7520 | | 0.4822 | 63.0 | 37170 | 0.9816 | 0.7373 | | 0.4956 | 64.0 | 37760 | 0.9746 | 0.7477 | | 0.4953 | 65.0 | 38350 | 0.9493 | 0.7544 | | 0.4794 | 66.0 | 38940 | 1.0795 | 0.7532 | | 0.4794 | 67.0 | 39530 | 0.9915 | 0.7575 | | 0.48 | 68.0 | 40120 | 0.9385 | 0.7498 | | 0.4633 | 69.0 | 40710 | 1.0949 | 0.7526 | | 0.4749 | 70.0 | 41300 | 1.0207 | 0.7557 | | 0.4657 | 71.0 | 41890 | 0.9383 | 0.7428 | | 0.465 | 72.0 | 42480 | 1.0948 | 0.7581 | | 0.4558 | 73.0 | 43070 | 0.9506 | 0.7492 | | 0.4516 | 74.0 | 43660 | 1.0518 | 0.7606 | | 0.4577 | 75.0 | 44250 | 1.0124 | 0.7575 | | 0.4642 | 76.0 | 44840 | 0.9293 | 0.7526 | | 0.4497 | 77.0 | 45430 | 0.9862 | 0.7541 | | 0.4614 | 78.0 | 46020 | 0.9403 | 0.7566 | | 0.4442 | 79.0 | 46610 | 0.9599 | 0.7581 | | 0.4483 | 80.0 | 47200 | 0.9766 | 0.7593 | | 0.4223 | 81.0 | 47790 | 0.9297 | 0.7547 | | 0.4416 | 82.0 | 48380 | 0.9614 | 0.7587 | | 0.4279 | 83.0 | 48970 | 0.9403 | 0.7587 | | 0.4159 | 84.0 | 49560 | 1.0827 | 0.7569 | | 0.4319 | 85.0 | 50150 | 0.9250 | 0.7505 | | 0.427 | 86.0 | 50740 | 0.9475 | 0.7517 | | 0.427 | 87.0 | 51330 | 0.9429 | 0.7523 | | 0.4233 | 88.0 | 51920 | 0.9721 | 0.7581 | | 0.4167 | 89.0 | 52510 | 0.9387 | 0.7557 | | 0.4162 | 90.0 | 53100 | 0.9282 | 0.7544 | | 0.4163 | 91.0 | 53690 | 0.9785 | 0.7566 | | 0.4214 | 92.0 | 54280 | 0.9217 | 0.7517 | | 0.4038 | 93.0 | 54870 | 0.9470 | 0.7584 | | 0.4258 | 94.0 | 55460 | 0.9254 | 0.7550 | | 0.4206 | 95.0 | 56050 | 0.9380 | 0.7569 | | 0.4086 | 96.0 | 56640 | 0.9379 | 0.7578 | | 0.3973 | 97.0 | 57230 | 0.9425 | 0.7557 | | 0.3971 | 98.0 | 57820 | 0.9461 | 0.7572 | | 0.3899 | 99.0 | 58410 | 0.9388 | 0.7557 | | 0.4033 | 100.0 | 59000 | 0.9382 | 0.7557 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
Onutoa/1_5e-3_10_0.5
Onutoa
2023-09-08T01:33:04Z
52
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T22:33:53Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: 1_5e-3_10_0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 1_5e-3_10_0.5 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9119 - Accuracy: 0.7446 ## Model description More information needed ## Intended uses & 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: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.6814 | 1.0 | 590 | 2.2524 | 0.6128 | | 2.6474 | 2.0 | 1180 | 2.2889 | 0.6217 | | 2.7373 | 3.0 | 1770 | 3.8911 | 0.4401 | | 2.7048 | 4.0 | 2360 | 2.6859 | 0.6214 | | 2.3193 | 5.0 | 2950 | 3.0408 | 0.6217 | | 2.0191 | 6.0 | 3540 | 2.0926 | 0.5706 | | 1.9595 | 7.0 | 4130 | 1.7082 | 0.6908 | | 1.833 | 8.0 | 4720 | 1.7816 | 0.6092 | | 1.7395 | 9.0 | 5310 | 1.6251 | 0.6281 | | 1.7038 | 10.0 | 5900 | 2.6889 | 0.6554 | | 1.7975 | 11.0 | 6490 | 1.5326 | 0.6994 | | 1.5534 | 12.0 | 7080 | 2.6513 | 0.5554 | | 1.5833 | 13.0 | 7670 | 1.5617 | 0.6410 | | 1.4585 | 14.0 | 8260 | 1.8289 | 0.6171 | | 1.4375 | 15.0 | 8850 | 1.6306 | 0.6517 | | 1.3418 | 16.0 | 9440 | 1.2628 | 0.7153 | | 1.2576 | 17.0 | 10030 | 1.4116 | 0.7098 | | 1.2068 | 18.0 | 10620 | 1.1643 | 0.7089 | | 1.1781 | 19.0 | 11210 | 1.4702 | 0.7083 | | 1.1497 | 20.0 | 11800 | 1.1550 | 0.6988 | | 1.0552 | 21.0 | 12390 | 1.0861 | 0.7284 | | 1.047 | 22.0 | 12980 | 1.0821 | 0.7205 | | 1.0036 | 23.0 | 13570 | 1.1193 | 0.7193 | | 0.9589 | 24.0 | 14160 | 1.3591 | 0.7135 | | 0.9604 | 25.0 | 14750 | 1.0030 | 0.7229 | | 0.9283 | 26.0 | 15340 | 1.1469 | 0.7031 | | 0.9242 | 27.0 | 15930 | 1.0466 | 0.7318 | | 0.8703 | 28.0 | 16520 | 1.0736 | 0.7343 | | 0.858 | 29.0 | 17110 | 1.0357 | 0.7183 | | 0.8267 | 30.0 | 17700 | 0.9936 | 0.7339 | | 0.8148 | 31.0 | 18290 | 0.9989 | 0.7321 | | 0.7981 | 32.0 | 18880 | 1.0559 | 0.7404 | | 0.7956 | 33.0 | 19470 | 1.0207 | 0.7217 | | 0.7817 | 34.0 | 20060 | 0.9636 | 0.7361 | | 0.7545 | 35.0 | 20650 | 0.9415 | 0.7324 | | 0.7372 | 36.0 | 21240 | 1.0793 | 0.7413 | | 0.7317 | 37.0 | 21830 | 1.2911 | 0.7315 | | 0.7411 | 38.0 | 22420 | 0.9517 | 0.7364 | | 0.7093 | 39.0 | 23010 | 1.0133 | 0.7382 | | 0.6838 | 40.0 | 23600 | 1.1835 | 0.7401 | | 0.6773 | 41.0 | 24190 | 0.9180 | 0.7379 | | 0.6776 | 42.0 | 24780 | 0.9410 | 0.7367 | | 0.6486 | 43.0 | 25370 | 0.9836 | 0.7419 | | 0.6527 | 44.0 | 25960 | 0.9721 | 0.7309 | | 0.6465 | 45.0 | 26550 | 0.9508 | 0.7388 | | 0.6245 | 46.0 | 27140 | 0.9273 | 0.7434 | | 0.6258 | 47.0 | 27730 | 0.9763 | 0.7330 | | 0.6086 | 48.0 | 28320 | 0.9135 | 0.7388 | | 0.6417 | 49.0 | 28910 | 1.0037 | 0.7446 | | 0.6064 | 50.0 | 29500 | 0.9751 | 0.7398 | | 0.5938 | 51.0 | 30090 | 0.9801 | 0.7453 | | 0.5951 | 52.0 | 30680 | 0.9515 | 0.7370 | | 0.5718 | 53.0 | 31270 | 0.9160 | 0.7419 | | 0.5751 | 54.0 | 31860 | 0.9263 | 0.7462 | | 0.5839 | 55.0 | 32450 | 0.9170 | 0.7376 | | 0.5707 | 56.0 | 33040 | 0.9787 | 0.7431 | | 0.564 | 57.0 | 33630 | 0.9822 | 0.7431 | | 0.5539 | 58.0 | 34220 | 0.9335 | 0.7407 | | 0.5567 | 59.0 | 34810 | 1.0004 | 0.7370 | | 0.5555 | 60.0 | 35400 | 0.9554 | 0.7446 | | 0.5344 | 61.0 | 35990 | 0.9199 | 0.7483 | | 0.5494 | 62.0 | 36580 | 0.9970 | 0.7456 | | 0.5226 | 63.0 | 37170 | 0.9454 | 0.7434 | | 0.5275 | 64.0 | 37760 | 0.9771 | 0.7361 | | 0.5186 | 65.0 | 38350 | 1.0032 | 0.7517 | | 0.52 | 66.0 | 38940 | 0.9263 | 0.7440 | | 0.5209 | 67.0 | 39530 | 1.0130 | 0.7443 | | 0.528 | 68.0 | 40120 | 0.9466 | 0.7422 | | 0.5146 | 69.0 | 40710 | 0.9790 | 0.7456 | | 0.5026 | 70.0 | 41300 | 0.9880 | 0.7489 | | 0.5204 | 71.0 | 41890 | 0.9132 | 0.7373 | | 0.5049 | 72.0 | 42480 | 0.9589 | 0.7480 | | 0.4969 | 73.0 | 43070 | 0.9564 | 0.7446 | | 0.4911 | 74.0 | 43660 | 0.9255 | 0.7336 | | 0.4961 | 75.0 | 44250 | 0.9983 | 0.7502 | | 0.4986 | 76.0 | 44840 | 0.9003 | 0.7376 | | 0.4979 | 77.0 | 45430 | 0.8937 | 0.7385 | | 0.4941 | 78.0 | 46020 | 0.9082 | 0.7422 | | 0.487 | 79.0 | 46610 | 0.9231 | 0.7471 | | 0.4773 | 80.0 | 47200 | 0.9673 | 0.7437 | | 0.4665 | 81.0 | 47790 | 0.9598 | 0.7462 | | 0.4824 | 82.0 | 48380 | 0.9110 | 0.7410 | | 0.4795 | 83.0 | 48970 | 0.9222 | 0.7425 | | 0.4654 | 84.0 | 49560 | 0.9369 | 0.7459 | | 0.4605 | 85.0 | 50150 | 0.9379 | 0.7502 | | 0.477 | 86.0 | 50740 | 0.8911 | 0.7437 | | 0.4644 | 87.0 | 51330 | 0.9287 | 0.7434 | | 0.4539 | 88.0 | 51920 | 0.9421 | 0.7422 | | 0.4582 | 89.0 | 52510 | 0.9248 | 0.7437 | | 0.4488 | 90.0 | 53100 | 0.9152 | 0.7425 | | 0.4554 | 91.0 | 53690 | 0.9511 | 0.7471 | | 0.4547 | 92.0 | 54280 | 0.9064 | 0.7419 | | 0.4534 | 93.0 | 54870 | 0.9404 | 0.7471 | | 0.463 | 94.0 | 55460 | 0.9346 | 0.7453 | | 0.4482 | 95.0 | 56050 | 0.9191 | 0.7437 | | 0.4518 | 96.0 | 56640 | 0.9154 | 0.7431 | | 0.4326 | 97.0 | 57230 | 0.9055 | 0.7440 | | 0.4291 | 98.0 | 57820 | 0.9072 | 0.7437 | | 0.4278 | 99.0 | 58410 | 0.9101 | 0.7437 | | 0.4397 | 100.0 | 59000 | 0.9119 | 0.7446 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
CyberHarem/caren_hortensia_fatekaleidlinerprismaillya
CyberHarem
2023-09-08T01:23:45Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/caren_hortensia_fatekaleidlinerprismaillya", "license:mit", "region:us" ]
text-to-image
2023-09-08T01:15:34Z
--- license: mit datasets: - CyberHarem/caren_hortensia_fatekaleidlinerprismaillya pipeline_tag: text-to-image tags: - art --- # Lora of caren_hortensia_fatekaleidlinerprismaillya 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/caren_hortensia_fatekaleidlinerprismaillya.pt` as the embedding and `4080/caren_hortensia_fatekaleidlinerprismaillya.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 0.929. The trigger words are: 1. `caren_hortensia_fatekaleidlinerprismaillya` 2. `long_hair, white_hair, yellow_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.922 | [Download](5100/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.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 | 0.916 | [Download](4760/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.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 | 0.919 | [Download](4420/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.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** | **0.929** | [**Download**](4080/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.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 | 0.912 | [Download](3740/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.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 | 0.924 | [Download](3400/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.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 | 0.906 | [Download](3060/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.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 | 0.876 | [Download](2720/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.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 | 0.891 | [Download](2380/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.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 | 0.895 | [Download](2040/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.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 | 0.872 | [Download](1700/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.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 | 0.783 | [Download](1360/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.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 | 0.704 | [Download](1020/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.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 | 0.675 | [Download](680/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.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 | 0.507 | [Download](340/caren_hortensia_fatekaleidlinerprismaillya.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.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) |
johaanm/test-planner-alpha-V7.4
johaanm
2023-09-08T01:16:48Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-08T01:16:44Z
--- 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 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 - PEFT 0.4.0
mespinosami/synthetic-cloud-removal-sd-1_5
mespinosami
2023-09-08T01:10:35Z
4
0
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-07T10:15:39Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-mespinosami/synthetic-cloud-removal-sd-1_5 These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. prompt: cloudless satellite image, remove all clouds from satellite image, no clouds ![images_0)](./images_0.png) prompt: cloudless satellite image, remove all clouds from satellite image, no clouds ![images_1)](./images_1.png) prompt: cloudless satellite image, remove all clouds from satellite image, no clouds ![images_2)](./images_2.png)
shengqin/bloomz-xss-sqli-2
shengqin
2023-09-08T00:48:12Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-07T21:41:19Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
daochf/Lora-MetaLlama2-7b-chat-hf-PuceDs03-v01
daochf
2023-09-07T23:23:43Z
2
0
peft
[ "peft", "region:us" ]
null
2023-09-07T23:23:26Z
--- 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
facebook/mms-cclms
facebook
2023-09-07T23:20:56Z
0
0
null
[ "mms", "arxiv:2207.04672", "arxiv:2305.13516", "license:cc-by-nc-4.0", "region:us" ]
null
2023-06-06T18:48:58Z
--- license: cc-by-nc-4.0 tags: - mms --- # Massively Multilingual Speech (MMS) - Common Crawl Language Models This repository consists of the n-gram language models trained on Common Crawl data ([Conneau et al. 2020b](https://aclanthology.org/2020.acl-main.747/), [NLLB_Team et al. 2022](https://arxiv.org/abs/2207.04672)) using [KenLM library](https://github.com/kpu/kenlm). For the following languages, the LMs are not present in the repository (due to 50GB limit on HuggingFace) and can be downloaded using the link provided here. Mandarin Chinese (Simplified) - [Download LM](https://dl.fbaipublicfiles.com/mms/lms/cmn-script_simplified/char_20gram.bin) Japanese - [Download LM](https://dl.fbaipublicfiles.com/mms/lms/jpn/char_20gram.bin) Thai - [Download LM](https://dl.fbaipublicfiles.com/mms/lms/tha/char_20gram.bin) Cantonese(Traditional) - [Download LM](https://dl.fbaipublicfiles.com/mms/lms/yue-script_traditional/char_20gram.bin) ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example Checkout the code here - https://huggingface.co/spaces/mms-meta/MMS/blob/main/asr.py which uses LMs for decoding the output from ASR models. ## Supported Languages We support language models in 102 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). <details> <summary>Click to toggle</summary> - afr - amh - ara - asm - ast - azj - bel - ben - bos - bul - cat - ceb - ces - ckb - cmn - cym - dan - deu - ell - eng - est - fas - fin - fra - ful - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ibo - ind - isl - ita - jav - jpn - kam - kan - kat - kaz - kea - khm - kir - kor - lao - lav - lin - lit - ltz - lug - luo - mal - mar - mkd - mlt - mon - mri - mya - nld - nob - npi - nso - nya - oci - orm - ory - pan - pol - por - pus - ron - rus - slk - slv - sna - snd - som - spa - srp - swe - swh - tam - tel - tgk - tgl - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zlm - zul </details> ## Model details - **Developed by:** Vineel Pratap et al. - **Model type:** Multi-Lingual Automatic Speech Recognition model - **Language(s):** 126 languages, see [supported languages](#supported-languages) - **License:** CC-BY-NC 4.0 license - **Num parameters**: 1 billion - **Audio sampling rate**: 16,000 kHz - **Cite as:** @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other **MMS** checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)
JohnyQuest/endo_llama2
JohnyQuest
2023-09-07T23:13:13Z
0
0
null
[ "license:llama2", "region:us" ]
null
2023-09-07T23:12:21Z
--- license: llama2 --- Endocrinology question answer finetuned LLAMA2. Version 1 - tuned only on hypothyroidism
federicochiarello/ppo-LunarLander-v2
federicochiarello
2023-09-07T23:12:53Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-07T23:12:29Z
--- 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: 250.84 +/- 21.26 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 ... ```
sarwarbeing/wm-04-aws-contrastive-learning
sarwarbeing
2023-09-07T23:03:12Z
5
0
sentence-transformers
[ "sentence-transformers", "pytorch", "deberta-v2", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-09-07T20:21:44Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # sarwarbeing/wm-04-aws-contrastive-learning 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("sarwarbeing/wm-04-aws-contrastive-learning") # 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} } ```
OmkarB/SQL-GQL-Finetuned-Instruct-Tune
OmkarB
2023-09-07T23:00:19Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-05T21:26:58Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
badhorse666/lunar-lander
badhorse666
2023-09-07T22:54:41Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-07T22:52:58Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 251.58 +/- 19.22 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 ... ```
Echiguerkh/rinna-AraBert-qa-ar3
Echiguerkh
2023-09-07T22:46:59Z
72
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:arcd", "base_model:aubmindlab/bert-base-arabertv2", "base_model:finetune:aubmindlab/bert-base-arabertv2", "endpoints_compatible", "region:us" ]
question-answering
2023-09-07T22:11:43Z
--- base_model: aubmindlab/bert-base-arabertv2 tags: - generated_from_trainer datasets: - arcd model-index: - name: rinna-AraBert-qa-ar3 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. --> # rinna-AraBert-qa-ar3 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the arcd dataset. It achieves the following results on the evaluation set: - Loss: 3.7678 ## Model description More information needed ## Intended uses & 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: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.0074 | 6.88 | 150 | 3.1727 | | 1.3286 | 13.75 | 300 | 3.2007 | | 0.7605 | 20.63 | 450 | 3.5414 | | 0.5722 | 27.51 | 600 | 3.7678 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Onutoa/1_5e-3_5_0.5
Onutoa
2023-09-07T22:33:20Z
62
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T19:34:50Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: 1_5e-3_5_0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 1_5e-3_5_0.5 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9516 - Accuracy: 0.7450 ## Model description More information needed ## Intended uses & 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: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.4372 | 1.0 | 590 | 1.8593 | 0.6177 | | 2.3953 | 2.0 | 1180 | 3.6910 | 0.3786 | | 2.3694 | 3.0 | 1770 | 2.1033 | 0.4694 | | 2.0494 | 4.0 | 2360 | 1.7694 | 0.6006 | | 2.034 | 5.0 | 2950 | 1.7949 | 0.6355 | | 1.8146 | 6.0 | 3540 | 1.7374 | 0.6159 | | 1.896 | 7.0 | 4130 | 1.8850 | 0.5624 | | 1.7794 | 8.0 | 4720 | 2.8405 | 0.6245 | | 1.8298 | 9.0 | 5310 | 2.6985 | 0.4349 | | 1.7892 | 10.0 | 5900 | 2.2049 | 0.6352 | | 1.6916 | 11.0 | 6490 | 1.6606 | 0.6272 | | 1.6384 | 12.0 | 7080 | 1.5955 | 0.6394 | | 1.6382 | 13.0 | 7670 | 1.6722 | 0.6596 | | 1.6078 | 14.0 | 8260 | 1.4874 | 0.6587 | | 1.5373 | 15.0 | 8850 | 1.4382 | 0.6642 | | 1.4655 | 16.0 | 9440 | 1.4120 | 0.6700 | | 1.4354 | 17.0 | 10030 | 2.0067 | 0.6532 | | 1.4021 | 18.0 | 10620 | 1.7860 | 0.5875 | | 1.3537 | 19.0 | 11210 | 1.4043 | 0.6853 | | 1.3638 | 20.0 | 11800 | 1.3726 | 0.6875 | | 1.3061 | 21.0 | 12390 | 1.3332 | 0.6740 | | 1.3052 | 22.0 | 12980 | 1.2831 | 0.6939 | | 1.4056 | 23.0 | 13570 | 1.4235 | 0.6835 | | 1.3389 | 24.0 | 14160 | 1.5395 | 0.6817 | | 1.2294 | 25.0 | 14750 | 1.2364 | 0.6994 | | 1.2213 | 26.0 | 15340 | 1.1806 | 0.7012 | | 1.203 | 27.0 | 15930 | 1.3771 | 0.6538 | | 1.1667 | 28.0 | 16520 | 1.3193 | 0.6820 | | 1.1516 | 29.0 | 17110 | 1.3490 | 0.6621 | | 1.1657 | 30.0 | 17700 | 1.1866 | 0.7015 | | 1.1212 | 31.0 | 18290 | 1.2403 | 0.6991 | | 1.0632 | 32.0 | 18880 | 1.1608 | 0.7138 | | 1.0702 | 33.0 | 19470 | 1.3606 | 0.6642 | | 1.0609 | 34.0 | 20060 | 1.1448 | 0.6972 | | 1.0407 | 35.0 | 20650 | 1.2761 | 0.6838 | | 1.0151 | 36.0 | 21240 | 2.0245 | 0.6862 | | 1.0246 | 37.0 | 21830 | 1.0999 | 0.7012 | | 0.9971 | 38.0 | 22420 | 1.1661 | 0.6997 | | 0.9732 | 39.0 | 23010 | 1.1978 | 0.7187 | | 0.9642 | 40.0 | 23600 | 1.0760 | 0.7245 | | 0.9628 | 41.0 | 24190 | 1.2119 | 0.7223 | | 0.9605 | 42.0 | 24780 | 1.0589 | 0.7245 | | 0.9297 | 43.0 | 25370 | 1.0496 | 0.7297 | | 0.9282 | 44.0 | 25960 | 1.0384 | 0.7324 | | 0.8927 | 45.0 | 26550 | 1.0954 | 0.7284 | | 0.8753 | 46.0 | 27140 | 1.0344 | 0.7343 | | 0.8787 | 47.0 | 27730 | 1.0238 | 0.7162 | | 0.8397 | 48.0 | 28320 | 1.0650 | 0.7162 | | 0.9109 | 49.0 | 28910 | 1.0901 | 0.7297 | | 0.8609 | 50.0 | 29500 | 1.0152 | 0.7300 | | 0.823 | 51.0 | 30090 | 1.1109 | 0.7128 | | 0.8029 | 52.0 | 30680 | 1.0899 | 0.7113 | | 0.8142 | 53.0 | 31270 | 1.0185 | 0.7339 | | 0.7967 | 54.0 | 31860 | 0.9917 | 0.7336 | | 0.7919 | 55.0 | 32450 | 1.0096 | 0.7352 | | 0.7883 | 56.0 | 33040 | 1.0033 | 0.7355 | | 0.7794 | 57.0 | 33630 | 1.0478 | 0.7336 | | 0.7444 | 58.0 | 34220 | 1.0485 | 0.7284 | | 0.7646 | 59.0 | 34810 | 1.0046 | 0.7242 | | 0.7493 | 60.0 | 35400 | 0.9997 | 0.7300 | | 0.7126 | 61.0 | 35990 | 0.9838 | 0.7398 | | 0.7303 | 62.0 | 36580 | 0.9983 | 0.7300 | | 0.7184 | 63.0 | 37170 | 1.1151 | 0.7156 | | 0.711 | 64.0 | 37760 | 1.0758 | 0.7220 | | 0.6963 | 65.0 | 38350 | 0.9884 | 0.7281 | | 0.6972 | 66.0 | 38940 | 0.9688 | 0.7336 | | 0.6927 | 67.0 | 39530 | 0.9794 | 0.7339 | | 0.6923 | 68.0 | 40120 | 0.9681 | 0.7379 | | 0.6829 | 69.0 | 40710 | 1.0167 | 0.7440 | | 0.6705 | 70.0 | 41300 | 0.9709 | 0.7358 | | 0.6717 | 71.0 | 41890 | 1.0276 | 0.7226 | | 0.6683 | 72.0 | 42480 | 0.9858 | 0.7324 | | 0.6405 | 73.0 | 43070 | 0.9954 | 0.7336 | | 0.6423 | 74.0 | 43660 | 0.9730 | 0.7339 | | 0.6628 | 75.0 | 44250 | 1.0100 | 0.7388 | | 0.6528 | 76.0 | 44840 | 0.9663 | 0.7398 | | 0.6327 | 77.0 | 45430 | 0.9619 | 0.7358 | | 0.6434 | 78.0 | 46020 | 0.9671 | 0.7361 | | 0.6261 | 79.0 | 46610 | 0.9778 | 0.7248 | | 0.6312 | 80.0 | 47200 | 0.9802 | 0.7343 | | 0.6098 | 81.0 | 47790 | 0.9736 | 0.7431 | | 0.6221 | 82.0 | 48380 | 0.9820 | 0.7330 | | 0.6166 | 83.0 | 48970 | 0.9587 | 0.7431 | | 0.6072 | 84.0 | 49560 | 0.9671 | 0.7370 | | 0.5986 | 85.0 | 50150 | 0.9629 | 0.7385 | | 0.5959 | 86.0 | 50740 | 0.9576 | 0.7407 | | 0.5858 | 87.0 | 51330 | 0.9793 | 0.7428 | | 0.5846 | 88.0 | 51920 | 0.9722 | 0.7404 | | 0.5879 | 89.0 | 52510 | 0.9822 | 0.7394 | | 0.582 | 90.0 | 53100 | 0.9625 | 0.7422 | | 0.5805 | 91.0 | 53690 | 0.9856 | 0.7443 | | 0.5767 | 92.0 | 54280 | 0.9560 | 0.7404 | | 0.5711 | 93.0 | 54870 | 0.9629 | 0.7440 | | 0.5769 | 94.0 | 55460 | 0.9560 | 0.7431 | | 0.557 | 95.0 | 56050 | 0.9562 | 0.7434 | | 0.5706 | 96.0 | 56640 | 0.9565 | 0.7440 | | 0.5691 | 97.0 | 57230 | 0.9515 | 0.7425 | | 0.5496 | 98.0 | 57820 | 0.9570 | 0.7410 | | 0.5643 | 99.0 | 58410 | 0.9512 | 0.7434 | | 0.5539 | 100.0 | 59000 | 0.9516 | 0.7450 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
CyberHarem/gakumazawa_tatsuko_fatekaleidlinerprismaillya
CyberHarem
2023-09-07T22:32:40Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/gakumazawa_tatsuko_fatekaleidlinerprismaillya", "license:mit", "region:us" ]
text-to-image
2023-09-07T22:23:39Z
--- license: mit datasets: - CyberHarem/gakumazawa_tatsuko_fatekaleidlinerprismaillya pipeline_tag: text-to-image tags: - art --- # Lora of gakumazawa_tatsuko_fatekaleidlinerprismaillya 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 3740, you need to download `3740/gakumazawa_tatsuko_fatekaleidlinerprismaillya.pt` as the embedding and `3740/gakumazawa_tatsuko_fatekaleidlinerprismaillya.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 3740**, with the score of 0.577. The trigger words are: 1. `gakumazawa_tatsuko_fatekaleidlinerprismaillya` 2. `hair_bun, blonde_hair, double_bun, ahoge, short_hair, open_mouth, brown_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-----------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.560 | [Download](5100/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.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 | 0.535 | [Download](4760/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.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 | 0.550 | [Download](4420/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.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 | 0.572 | [Download](4080/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.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** | **0.577** | [**Download**](3740/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.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 | 0.549 | [Download](3400/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.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 | 0.555 | [Download](3060/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.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 | 0.550 | [Download](2720/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.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 | 0.441 | [Download](2380/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.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 | 0.476 | [Download](2040/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.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 | 0.480 | [Download](1700/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.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 | 0.488 | [Download](1360/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.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 | 0.314 | [Download](1020/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.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 | 0.323 | [Download](680/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.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 | 0.211 | [Download](340/gakumazawa_tatsuko_fatekaleidlinerprismaillya.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.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) |
MajorBehrad/pixelcopter
MajorBehrad
2023-09-07T22:30:35Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-09-07T22:30:32Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 14.00 +/- 19.38 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
CTFanon/ctf_lora_v9
CTFanon
2023-09-07T22:30:03Z
0
1
null
[ "region:us" ]
null
2023-08-25T22:23:12Z
# CTF LoRA This LoRA lets you generate cock transformation images. ![catbox image](https://files.catbox.moe/kmb1j1.png) The metadata in the above picture contains an example prompt. # About This LoRA was trained on a handful of actual images, and several images generated from previous iterations of the model. It has a monochrome bias, and some poses are overfitted. The LoRA is well suited for inpainting. You should bring your own style LoRA, as the inherent style in the LoRA is rough. # More examples All of these images are direct output from the LoRA using FutaFactor as the base model. Decent results will require inpainting and patience. ![catbox image](https://files.catbox.moe/w7t7a9.png) ![catbox image](https://files.catbox.moe/nn9d7s.png) ![catbox image](https://files.catbox.moe/83kyia.png) ![catbox image](https://files.catbox.moe/q2vwc7.png)
Chris808/bloom_prompt_tuning_1694123891.9409811
Chris808
2023-09-07T22:15:05Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-07T22:15:04Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
dmitvuk/SEMEVAL23_TASK3_SUBTASK1_MULTI
dmitvuk
2023-09-07T21:41:34Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlnet", "text-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" ]
text-classification
2023-07-25T12:07:13Z
--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: SEMEVAL23_TASK3_SUBTASK1_MULTI 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. --> # SEMEVAL23_TASK3_SUBTASK1_MULTI 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: 1.6313 - F1: 0.6299 ## Model description More information needed ## Intended uses & 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9669 | 1.0 | 160 | 0.9574 | 0.4075 | | 0.4214 | 2.0 | 320 | 0.6809 | 0.5769 | | 0.0096 | 3.0 | 480 | 1.3114 | 0.4152 | | 0.2681 | 4.0 | 640 | 0.7792 | 0.6122 | | 0.0007 | 5.0 | 800 | 1.3213 | 0.5765 | | 0.0005 | 6.0 | 960 | 1.7983 | 0.5749 | | 0.0011 | 7.0 | 1120 | 2.2000 | 0.5298 | | 0.0008 | 8.0 | 1280 | 1.3757 | 0.5812 | | 0.0007 | 9.0 | 1440 | 1.5493 | 0.5990 | | 0.001 | 10.0 | 1600 | 1.4796 | 0.6233 | | 0.0008 | 11.0 | 1760 | 1.4954 | 0.6251 | | 0.0002 | 12.0 | 1920 | 1.6313 | 0.6299 | | 0.0004 | 13.0 | 2080 | 1.5037 | 0.6296 | | 0.0008 | 14.0 | 2240 | 1.5526 | 0.6277 | | 0.0001 | 15.0 | 2400 | 1.5745 | 0.6254 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
thisisgamal/test101c
thisisgamal
2023-09-07T21:39:25Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-09-07T21:39:25Z
--- license: bigscience-openrail-m ---
Dyang0/ppo-LunarLander-v2
Dyang0
2023-09-07T21:34:11Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-07T21:33:51Z
--- 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: 263.98 +/- 13.73 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 ... ```
GesusFranca/qa_model
GesusFranca
2023-09-07T21:34:06Z
42
0
transformers
[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:mrm8488/bert-base-portuguese-cased-finetuned-squad-v1-pt", "base_model:finetune:mrm8488/bert-base-portuguese-cased-finetuned-squad-v1-pt", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-09-07T18:01:36Z
--- license: apache-2.0 base_model: mrm8488/bert-base-portuguese-cased-finetuned-squad-v1-pt tags: - generated_from_keras_callback model-index: - name: GesusFranca/qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # GesusFranca/qa_model This model is a fine-tuned version of [mrm8488/bert-base-portuguese-cased-finetuned-squad-v1-pt](https://huggingface.co/mrm8488/bert-base-portuguese-cased-finetuned-squad-v1-pt) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5147 - Validation Loss: 4.5895 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.0442 | 4.7063 | 0 | | 4.5147 | 4.5895 | 1 | ### Framework versions - Transformers 4.33.1 - TensorFlow 2.12.0 - Datasets 2.14.5 - Tokenizers 0.13.3
DrishtiSharma/roberta-large-hate-offensive-normal-speech-lr-2e-05
DrishtiSharma
2023-09-07T21:16:06Z
60
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T21:10:06Z
--- license: mit base_model: roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-large-hate-offensive-normal-speech-lr-2e-05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-hate-offensive-normal-speech-lr-2e-05 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0293 - Accuracy: 0.9837 - Weighted f1: 0.9837 - Weighted recall: 0.9837 - Weighted precision: 0.9839 - Micro f1: 0.9837 - Micro recall: 0.9837 - Micro precision: 0.9837 - Macro f1: 0.9832 - Macro recall: 0.9821 - Macro precision: 0.9845 ## Model description More information needed ## Intended uses & 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 | Weighted f1 | Weighted recall | Weighted precision | Micro f1 | Micro recall | Micro precision | Macro f1 | Macro recall | Macro precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:| | 0.5253 | 1.0 | 153 | 0.1270 | 0.9642 | 0.9647 | 0.9642 | 0.9681 | 0.9642 | 0.9642 | 0.9642 | 0.9633 | 0.9662 | 0.9633 | | 0.0921 | 2.0 | 306 | 0.0878 | 0.9805 | 0.9805 | 0.9805 | 0.9807 | 0.9805 | 0.9805 | 0.9805 | 0.9803 | 0.9791 | 0.9818 | | 0.0413 | 3.0 | 459 | 0.0590 | 0.9870 | 0.9870 | 0.9870 | 0.9875 | 0.9870 | 0.9870 | 0.9870 | 0.9860 | 0.9869 | 0.9857 | | 0.0261 | 4.0 | 612 | 0.0523 | 0.9902 | 0.9902 | 0.9902 | 0.9904 | 0.9902 | 0.9902 | 0.9902 | 0.9896 | 0.9896 | 0.9900 | | 0.012 | 5.0 | 765 | 0.0293 | 0.9837 | 0.9837 | 0.9837 | 0.9839 | 0.9837 | 0.9837 | 0.9837 | 0.9832 | 0.9821 | 0.9845 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3
CyberHarem/bazett_fraga_mcremitz_fatekaleidlinerprismaillya
CyberHarem
2023-09-07T21:13:20Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/bazett_fraga_mcremitz_fatekaleidlinerprismaillya", "license:mit", "region:us" ]
text-to-image
2023-09-07T21:01:09Z
--- license: mit datasets: - CyberHarem/bazett_fraga_mcremitz_fatekaleidlinerprismaillya pipeline_tag: text-to-image tags: - art --- # Lora of bazett_fraga_mcremitz_fatekaleidlinerprismaillya 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 7200, you need to download `7200/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.pt` as the embedding and `7200/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 7200**, with the score of 0.957. The trigger words are: 1. `bazett_fraga_mcremitz_fatekaleidlinerprismaillya` 2. `short_hair, purple_hair, purple_eyes, mole, mole_under_eye, formal, suit, red_hair, necktie` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | **7200** | **0.957** | [**Download**](7200/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.zip) | ![pattern_1-7200](7200/previews/pattern_1.png) | ![pattern_2-7200](7200/previews/pattern_2.png) | ![pattern_3-7200](7200/previews/pattern_3.png) | ![pattern_4-7200](7200/previews/pattern_4.png) | ![pattern_5-7200](7200/previews/pattern_5.png) | ![pattern_6-7200](7200/previews/pattern_6.png) | ![pattern_7-7200](7200/previews/pattern_7.png) | ![pattern_8-7200](7200/previews/pattern_8.png) | ![pattern_9-7200](7200/previews/pattern_9.png) | ![bikini-7200](7200/previews/bikini.png) | [<NSFW, click to see>](7200/previews/bondage.png) | ![free-7200](7200/previews/free.png) | ![maid-7200](7200/previews/maid.png) | ![miko-7200](7200/previews/miko.png) | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) | ![suit-7200](7200/previews/suit.png) | ![yukata-7200](7200/previews/yukata.png) | | 6720 | 0.957 | [Download](6720/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.zip) | ![pattern_1-6720](6720/previews/pattern_1.png) | ![pattern_2-6720](6720/previews/pattern_2.png) | ![pattern_3-6720](6720/previews/pattern_3.png) | ![pattern_4-6720](6720/previews/pattern_4.png) | ![pattern_5-6720](6720/previews/pattern_5.png) | ![pattern_6-6720](6720/previews/pattern_6.png) | ![pattern_7-6720](6720/previews/pattern_7.png) | ![pattern_8-6720](6720/previews/pattern_8.png) | ![pattern_9-6720](6720/previews/pattern_9.png) | ![bikini-6720](6720/previews/bikini.png) | [<NSFW, click to see>](6720/previews/bondage.png) | ![free-6720](6720/previews/free.png) | ![maid-6720](6720/previews/maid.png) | ![miko-6720](6720/previews/miko.png) | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) | ![suit-6720](6720/previews/suit.png) | ![yukata-6720](6720/previews/yukata.png) | | 6240 | 0.899 | [Download](6240/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.zip) | ![pattern_1-6240](6240/previews/pattern_1.png) | ![pattern_2-6240](6240/previews/pattern_2.png) | ![pattern_3-6240](6240/previews/pattern_3.png) | ![pattern_4-6240](6240/previews/pattern_4.png) | ![pattern_5-6240](6240/previews/pattern_5.png) | ![pattern_6-6240](6240/previews/pattern_6.png) | ![pattern_7-6240](6240/previews/pattern_7.png) | ![pattern_8-6240](6240/previews/pattern_8.png) | ![pattern_9-6240](6240/previews/pattern_9.png) | ![bikini-6240](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | 5760 | 0.875 | [Download](5760/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.zip) | ![pattern_1-5760](5760/previews/pattern_1.png) | ![pattern_2-5760](5760/previews/pattern_2.png) | 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![bikini-2400](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | ![free-2400](2400/previews/free.png) | ![maid-2400](2400/previews/maid.png) | ![miko-2400](2400/previews/miko.png) | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) | ![suit-2400](2400/previews/suit.png) | ![yukata-2400](2400/previews/yukata.png) | | 1920 | 0.859 | [Download](1920/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.zip) | ![pattern_1-1920](1920/previews/pattern_1.png) | ![pattern_2-1920](1920/previews/pattern_2.png) | ![pattern_3-1920](1920/previews/pattern_3.png) | ![pattern_4-1920](1920/previews/pattern_4.png) | ![pattern_5-1920](1920/previews/pattern_5.png) | ![pattern_6-1920](1920/previews/pattern_6.png) | ![pattern_7-1920](1920/previews/pattern_7.png) | ![pattern_8-1920](1920/previews/pattern_8.png) | ![pattern_9-1920](1920/previews/pattern_9.png) | ![bikini-1920](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) | ![free-1920](1920/previews/free.png) | ![maid-1920](1920/previews/maid.png) | ![miko-1920](1920/previews/miko.png) | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) | ![suit-1920](1920/previews/suit.png) | ![yukata-1920](1920/previews/yukata.png) | | 1440 | 0.762 | [Download](1440/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.zip) | ![pattern_1-1440](1440/previews/pattern_1.png) | ![pattern_2-1440](1440/previews/pattern_2.png) | ![pattern_3-1440](1440/previews/pattern_3.png) | ![pattern_4-1440](1440/previews/pattern_4.png) | ![pattern_5-1440](1440/previews/pattern_5.png) | ![pattern_6-1440](1440/previews/pattern_6.png) | ![pattern_7-1440](1440/previews/pattern_7.png) | ![pattern_8-1440](1440/previews/pattern_8.png) | ![pattern_9-1440](1440/previews/pattern_9.png) | ![bikini-1440](1440/previews/bikini.png) | [<NSFW, click to see>](1440/previews/bondage.png) | ![free-1440](1440/previews/free.png) | 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see>](960/previews/nude.png) | [<NSFW, click to see>](960/previews/nude2.png) | ![suit-960](960/previews/suit.png) | ![yukata-960](960/previews/yukata.png) | | 480 | 0.347 | [Download](480/bazett_fraga_mcremitz_fatekaleidlinerprismaillya.zip) | ![pattern_1-480](480/previews/pattern_1.png) | ![pattern_2-480](480/previews/pattern_2.png) | ![pattern_3-480](480/previews/pattern_3.png) | ![pattern_4-480](480/previews/pattern_4.png) | ![pattern_5-480](480/previews/pattern_5.png) | ![pattern_6-480](480/previews/pattern_6.png) | ![pattern_7-480](480/previews/pattern_7.png) | ![pattern_8-480](480/previews/pattern_8.png) | ![pattern_9-480](480/previews/pattern_9.png) | ![bikini-480](480/previews/bikini.png) | [<NSFW, click to see>](480/previews/bondage.png) | ![free-480](480/previews/free.png) | ![maid-480](480/previews/maid.png) | ![miko-480](480/previews/miko.png) | [<NSFW, click to see>](480/previews/nude.png) | [<NSFW, click to see>](480/previews/nude2.png) | ![suit-480](480/previews/suit.png) | ![yukata-480](480/previews/yukata.png) |
Sanjay1234/Classification-using-SetFit-head
Sanjay1234
2023-09-07T21:04:47Z
3
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-09-07T21:02:26Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # Sanjay1234/Classification-using-SetFit-head 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("Sanjay1234/Classification-using-SetFit-head") # 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} } ```
gauthamk28/a2c-PandaReachDense-v2
gauthamk28
2023-09-07T20:52:26Z
5
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "arxiv:2106.13687", "model-index", "region:us" ]
reinforcement-learning
2023-03-20T10:02:51Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.07 +/- 0.30 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ``` Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
DrishtiSharma/hateBERT-hate-offensive-normal-speech-lr-2e-05
DrishtiSharma
2023-09-07T20:45:58Z
63
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:GroNLP/hateBERT", "base_model:finetune:GroNLP/hateBERT", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T20:44:17Z
--- base_model: GroNLP/hateBERT tags: - generated_from_trainer metrics: - accuracy model-index: - name: hateBERT-hate-offensive-normal-speech-lr-2e-05 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. --> # hateBERT-hate-offensive-normal-speech-lr-2e-05 This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0207 - Accuracy: 0.9902 - Weighted f1: 0.9902 - Weighted recall: 0.9902 - Weighted precision: 0.9904 - Micro f1: 0.9902 - Micro recall: 0.9902 - Micro precision: 0.9902 - Macro f1: 0.9901 - Macro recall: 0.9903 - Macro precision: 0.9899 ## Model description More information needed ## Intended uses & 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 | Weighted f1 | Weighted recall | Weighted precision | Micro f1 | Micro recall | Micro precision | Macro f1 | Macro recall | Macro precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:| | 0.6155 | 1.0 | 153 | 0.0889 | 0.9805 | 0.9805 | 0.9805 | 0.9806 | 0.9805 | 0.9805 | 0.9805 | 0.9801 | 0.9811 | 0.9793 | | 0.0665 | 2.0 | 306 | 0.0368 | 0.9870 | 0.9870 | 0.9870 | 0.9870 | 0.9870 | 0.9870 | 0.9870 | 0.9864 | 0.9866 | 0.9864 | | 0.0235 | 3.0 | 459 | 0.0264 | 0.9902 | 0.9902 | 0.9902 | 0.9904 | 0.9902 | 0.9902 | 0.9902 | 0.9901 | 0.9903 | 0.9899 | | 0.0182 | 4.0 | 612 | 0.0414 | 0.9870 | 0.9870 | 0.9870 | 0.9873 | 0.9870 | 0.9870 | 0.9870 | 0.9865 | 0.9869 | 0.9864 | | 0.012 | 5.0 | 765 | 0.0207 | 0.9902 | 0.9902 | 0.9902 | 0.9904 | 0.9902 | 0.9902 | 0.9902 | 0.9901 | 0.9903 | 0.9899 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3
vaikunthgc/trial
vaikunthgc
2023-09-07T20:42:38Z
0
0
adapter-transformers
[ "adapter-transformers", "pytorch", "gpt2", "en", "region:us" ]
null
2023-09-07T20:36:33Z
--- language: - en library_name: adapter-transformers ---
DrishtiSharma/fBERT-hate-offensive-normal-speech-lr-2e-05
DrishtiSharma
2023-09-07T20:40:35Z
57
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:diptanu/fBERT", "base_model:finetune:diptanu/fBERT", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T20:38:53Z
--- base_model: diptanu/fBERT tags: - generated_from_trainer metrics: - accuracy model-index: - name: fBERT-hate-offensive-normal-speech-lr-2e-05 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. --> # fBERT-hate-offensive-normal-speech-lr-2e-05 This model is a fine-tuned version of [diptanu/fBERT](https://huggingface.co/diptanu/fBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0152 - Accuracy: 0.9935 - Weighted f1: 0.9935 - Weighted recall: 0.9935 - Weighted precision: 0.9936 - Micro f1: 0.9935 - Micro recall: 0.9935 - Micro precision: 0.9935 - Macro f1: 0.9932 - Macro recall: 0.9938 - Macro precision: 0.9927 ## Model description More information needed ## Intended uses & 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 | Weighted f1 | Weighted recall | Weighted precision | Micro f1 | Micro recall | Micro precision | Macro f1 | Macro recall | Macro precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:| | 0.4897 | 1.0 | 153 | 0.0784 | 0.9739 | 0.9741 | 0.9739 | 0.9755 | 0.9739 | 0.9739 | 0.9739 | 0.9730 | 0.9744 | 0.9729 | | 0.0723 | 2.0 | 306 | 0.0183 | 0.9967 | 0.9967 | 0.9967 | 0.9968 | 0.9967 | 0.9967 | 0.9967 | 0.9964 | 0.9965 | 0.9963 | | 0.027 | 3.0 | 459 | 0.0226 | 0.9935 | 0.9935 | 0.9935 | 0.9936 | 0.9935 | 0.9935 | 0.9935 | 0.9932 | 0.9938 | 0.9927 | | 0.0139 | 4.0 | 612 | 0.0194 | 0.9902 | 0.9903 | 0.9902 | 0.9905 | 0.9902 | 0.9902 | 0.9902 | 0.9896 | 0.9903 | 0.9891 | | 0.0119 | 5.0 | 765 | 0.0152 | 0.9935 | 0.9935 | 0.9935 | 0.9936 | 0.9935 | 0.9935 | 0.9935 | 0.9932 | 0.9938 | 0.9927 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3
estonto/fido-gpt
estonto
2023-09-07T20:35:27Z
63
2
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-28T08:24:53Z
--- language: - en --- # FIDO-GPT: Generative AI behind "Fidonet Cybernetic Immortality" Project [FIDONet](https://en.wikipedia.org/wiki/FidoNet) is a historic computer network based on nightly mail exchange between servers via telephone lines, which was popular in 1990-s. In [FIDONet Cybernetic Immortality Project](https://soshnikov.com/art/fidoci) we are looking to create exhibits that will revive now-almost-dead FIDONet by automatically writing correspondence in FIDONet style via generative large language models. This model is based on [GPT2-large](https://huggingface.co/gpt2-large) model, and was fine-tuned for 2 epochs on archives of [ExecPC BBS](https://en.wikipedia.org/wiki/ExecPC_BBS), obtained from [here](https://breakintochat.com/collections/messages/fidonet/index.html). This process took around 9 hours on NVidia A100 compute in Yandex Datasphere service. This code can be used for generation: ```python from transformers import pipeline, AutoModelForCausalLM,AutoTokenizer import torch model_name = 'estonto/fido-gpt' model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) pipe = pipeline(model=model,tokenizer=tokenizer,task="text-generation",device="cuda") result = pipe("<s>Topic: COMPUTING",do_sample=True,max_length=500)[0]['generated_text'].replace('\\n','\n') ``` Project idea and model training: [Dmitry Soshnikov](https://soshnikov.com)
DrishtiSharma/distilbert-base-uncased-hate-offensive-normal-speech-lr-2e-05
DrishtiSharma
2023-09-07T20:33:47Z
60
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
2023-09-07T20:32:47Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-hate-offensive-normal-speech-lr-2e-05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-hate-offensive-normal-speech-lr-2e-05 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0178 - Accuracy: 0.9935 - Weighted f1: 0.9935 - Weighted recall: 0.9935 - Weighted precision: 0.9936 - Micro f1: 0.9935 - Micro recall: 0.9935 - Micro precision: 0.9935 - Macro f1: 0.9932 - Macro recall: 0.9938 - Macro precision: 0.9927 ## Model description More information needed ## Intended uses & 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 | Weighted f1 | Weighted recall | Weighted precision | Micro f1 | Micro recall | Micro precision | Macro f1 | Macro recall | Macro precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:| | 0.5013 | 1.0 | 153 | 0.0914 | 0.9642 | 0.9643 | 0.9642 | 0.9649 | 0.9642 | 0.9642 | 0.9642 | 0.9629 | 0.9639 | 0.9623 | | 0.0924 | 2.0 | 306 | 0.0314 | 0.9935 | 0.9935 | 0.9935 | 0.9936 | 0.9935 | 0.9935 | 0.9935 | 0.9932 | 0.9938 | 0.9927 | | 0.0432 | 3.0 | 459 | 0.0298 | 0.9870 | 0.9870 | 0.9870 | 0.9875 | 0.9870 | 0.9870 | 0.9870 | 0.9860 | 0.9869 | 0.9857 | | 0.0217 | 4.0 | 612 | 0.0259 | 0.9902 | 0.9903 | 0.9902 | 0.9905 | 0.9902 | 0.9902 | 0.9902 | 0.9896 | 0.9903 | 0.9891 | | 0.0148 | 5.0 | 765 | 0.0178 | 0.9935 | 0.9935 | 0.9935 | 0.9936 | 0.9935 | 0.9935 | 0.9935 | 0.9932 | 0.9938 | 0.9927 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3
matgu23/zws2
matgu23
2023-09-07T20:21:26Z
9
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-08-29T23:33:54Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### zws2 Dreambooth model trained by matgu23 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:
CyberHarem/luviagelita_edelfelt_fatekaleidlinerprismaillya
CyberHarem
2023-09-07T20:18:51Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/luviagelita_edelfelt_fatekaleidlinerprismaillya", "license:mit", "region:us" ]
text-to-image
2023-09-07T20:05:42Z
--- license: mit datasets: - CyberHarem/luviagelita_edelfelt_fatekaleidlinerprismaillya pipeline_tag: text-to-image tags: - art --- # Lora of luviagelita_edelfelt_fatekaleidlinerprismaillya 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 7540, you need to download `7540/luviagelita_edelfelt_fatekaleidlinerprismaillya.pt` as the embedding and `7540/luviagelita_edelfelt_fatekaleidlinerprismaillya.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 7540**, with the score of 0.873. The trigger words are: 1. `luviagelita_edelfelt_fatekaleidlinerprismaillya` 2. `long_hair, blonde_hair, drill_hair, ribbon, hair_ribbon, bow, brown_eyes, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 8700 | 0.821 | [Download](8700/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-8700](8700/previews/pattern_1.png) | ![pattern_2-8700](8700/previews/pattern_2.png) | ![pattern_3-8700](8700/previews/pattern_3.png) | ![pattern_4-8700](8700/previews/pattern_4.png) | ![pattern_5-8700](8700/previews/pattern_5.png) | ![pattern_6-8700](8700/previews/pattern_6.png) | ![pattern_7-8700](8700/previews/pattern_7.png) | ![pattern_8-8700](8700/previews/pattern_8.png) | ![pattern_9-8700](8700/previews/pattern_9.png) | ![pattern_10-8700](8700/previews/pattern_10.png) | ![pattern_11-8700](8700/previews/pattern_11.png) | ![bikini-8700](8700/previews/bikini.png) | [<NSFW, click to see>](8700/previews/bondage.png) | ![free-8700](8700/previews/free.png) | ![maid-8700](8700/previews/maid.png) | ![miko-8700](8700/previews/miko.png) | [<NSFW, click to see>](8700/previews/nude.png) | [<NSFW, click to see>](8700/previews/nude2.png) | ![suit-8700](8700/previews/suit.png) | ![yukata-8700](8700/previews/yukata.png) | | 8120 | 0.811 | [Download](8120/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-8120](8120/previews/pattern_1.png) | ![pattern_2-8120](8120/previews/pattern_2.png) | ![pattern_3-8120](8120/previews/pattern_3.png) | ![pattern_4-8120](8120/previews/pattern_4.png) | ![pattern_5-8120](8120/previews/pattern_5.png) | ![pattern_6-8120](8120/previews/pattern_6.png) | ![pattern_7-8120](8120/previews/pattern_7.png) | ![pattern_8-8120](8120/previews/pattern_8.png) | ![pattern_9-8120](8120/previews/pattern_9.png) | ![pattern_10-8120](8120/previews/pattern_10.png) | ![pattern_11-8120](8120/previews/pattern_11.png) | ![bikini-8120](8120/previews/bikini.png) | [<NSFW, click to see>](8120/previews/bondage.png) | ![free-8120](8120/previews/free.png) | ![maid-8120](8120/previews/maid.png) | ![miko-8120](8120/previews/miko.png) | [<NSFW, click to see>](8120/previews/nude.png) | [<NSFW, click to see>](8120/previews/nude2.png) | ![suit-8120](8120/previews/suit.png) | ![yukata-8120](8120/previews/yukata.png) | | **7540** | **0.873** | [**Download**](7540/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-7540](7540/previews/pattern_1.png) | ![pattern_2-7540](7540/previews/pattern_2.png) | ![pattern_3-7540](7540/previews/pattern_3.png) | ![pattern_4-7540](7540/previews/pattern_4.png) | ![pattern_5-7540](7540/previews/pattern_5.png) | ![pattern_6-7540](7540/previews/pattern_6.png) | ![pattern_7-7540](7540/previews/pattern_7.png) | ![pattern_8-7540](7540/previews/pattern_8.png) | ![pattern_9-7540](7540/previews/pattern_9.png) | ![pattern_10-7540](7540/previews/pattern_10.png) | ![pattern_11-7540](7540/previews/pattern_11.png) | ![bikini-7540](7540/previews/bikini.png) | [<NSFW, click to see>](7540/previews/bondage.png) | ![free-7540](7540/previews/free.png) | ![maid-7540](7540/previews/maid.png) | ![miko-7540](7540/previews/miko.png) | [<NSFW, click to see>](7540/previews/nude.png) | [<NSFW, click to see>](7540/previews/nude2.png) | ![suit-7540](7540/previews/suit.png) | ![yukata-7540](7540/previews/yukata.png) | | 6960 | 0.805 | [Download](6960/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-6960](6960/previews/pattern_1.png) | ![pattern_2-6960](6960/previews/pattern_2.png) | ![pattern_3-6960](6960/previews/pattern_3.png) | ![pattern_4-6960](6960/previews/pattern_4.png) | ![pattern_5-6960](6960/previews/pattern_5.png) | ![pattern_6-6960](6960/previews/pattern_6.png) | ![pattern_7-6960](6960/previews/pattern_7.png) | ![pattern_8-6960](6960/previews/pattern_8.png) | ![pattern_9-6960](6960/previews/pattern_9.png) | ![pattern_10-6960](6960/previews/pattern_10.png) | ![pattern_11-6960](6960/previews/pattern_11.png) | ![bikini-6960](6960/previews/bikini.png) | [<NSFW, click to see>](6960/previews/bondage.png) | ![free-6960](6960/previews/free.png) | ![maid-6960](6960/previews/maid.png) | ![miko-6960](6960/previews/miko.png) | [<NSFW, click to see>](6960/previews/nude.png) | [<NSFW, click to see>](6960/previews/nude2.png) | ![suit-6960](6960/previews/suit.png) | ![yukata-6960](6960/previews/yukata.png) | | 6380 | 0.865 | [Download](6380/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-6380](6380/previews/pattern_1.png) | ![pattern_2-6380](6380/previews/pattern_2.png) | ![pattern_3-6380](6380/previews/pattern_3.png) | ![pattern_4-6380](6380/previews/pattern_4.png) | ![pattern_5-6380](6380/previews/pattern_5.png) | ![pattern_6-6380](6380/previews/pattern_6.png) | ![pattern_7-6380](6380/previews/pattern_7.png) | ![pattern_8-6380](6380/previews/pattern_8.png) | ![pattern_9-6380](6380/previews/pattern_9.png) | ![pattern_10-6380](6380/previews/pattern_10.png) | ![pattern_11-6380](6380/previews/pattern_11.png) | ![bikini-6380](6380/previews/bikini.png) | [<NSFW, click to see>](6380/previews/bondage.png) | ![free-6380](6380/previews/free.png) | ![maid-6380](6380/previews/maid.png) | ![miko-6380](6380/previews/miko.png) | [<NSFW, click to see>](6380/previews/nude.png) | [<NSFW, click to see>](6380/previews/nude2.png) | ![suit-6380](6380/previews/suit.png) | ![yukata-6380](6380/previews/yukata.png) | | 5800 | 0.847 | [Download](5800/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-5800](5800/previews/pattern_1.png) | ![pattern_2-5800](5800/previews/pattern_2.png) | ![pattern_3-5800](5800/previews/pattern_3.png) | ![pattern_4-5800](5800/previews/pattern_4.png) | ![pattern_5-5800](5800/previews/pattern_5.png) | ![pattern_6-5800](5800/previews/pattern_6.png) | ![pattern_7-5800](5800/previews/pattern_7.png) | ![pattern_8-5800](5800/previews/pattern_8.png) | ![pattern_9-5800](5800/previews/pattern_9.png) | ![pattern_10-5800](5800/previews/pattern_10.png) | ![pattern_11-5800](5800/previews/pattern_11.png) | ![bikini-5800](5800/previews/bikini.png) | [<NSFW, click to see>](5800/previews/bondage.png) | ![free-5800](5800/previews/free.png) | ![maid-5800](5800/previews/maid.png) | ![miko-5800](5800/previews/miko.png) | [<NSFW, click to see>](5800/previews/nude.png) | [<NSFW, click to see>](5800/previews/nude2.png) | ![suit-5800](5800/previews/suit.png) | ![yukata-5800](5800/previews/yukata.png) | | 5220 | 0.868 | [Download](5220/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-5220](5220/previews/pattern_1.png) | ![pattern_2-5220](5220/previews/pattern_2.png) | ![pattern_3-5220](5220/previews/pattern_3.png) | ![pattern_4-5220](5220/previews/pattern_4.png) | ![pattern_5-5220](5220/previews/pattern_5.png) | ![pattern_6-5220](5220/previews/pattern_6.png) | ![pattern_7-5220](5220/previews/pattern_7.png) | ![pattern_8-5220](5220/previews/pattern_8.png) | ![pattern_9-5220](5220/previews/pattern_9.png) | ![pattern_10-5220](5220/previews/pattern_10.png) | ![pattern_11-5220](5220/previews/pattern_11.png) | ![bikini-5220](5220/previews/bikini.png) | [<NSFW, click to see>](5220/previews/bondage.png) | ![free-5220](5220/previews/free.png) | ![maid-5220](5220/previews/maid.png) | ![miko-5220](5220/previews/miko.png) | [<NSFW, click to see>](5220/previews/nude.png) | [<NSFW, click to see>](5220/previews/nude2.png) | ![suit-5220](5220/previews/suit.png) | ![yukata-5220](5220/previews/yukata.png) | | 4640 | 0.834 | [Download](4640/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-4640](4640/previews/pattern_1.png) | ![pattern_2-4640](4640/previews/pattern_2.png) | ![pattern_3-4640](4640/previews/pattern_3.png) | ![pattern_4-4640](4640/previews/pattern_4.png) | ![pattern_5-4640](4640/previews/pattern_5.png) | ![pattern_6-4640](4640/previews/pattern_6.png) | ![pattern_7-4640](4640/previews/pattern_7.png) | ![pattern_8-4640](4640/previews/pattern_8.png) | ![pattern_9-4640](4640/previews/pattern_9.png) | ![pattern_10-4640](4640/previews/pattern_10.png) | ![pattern_11-4640](4640/previews/pattern_11.png) | ![bikini-4640](4640/previews/bikini.png) | [<NSFW, click to see>](4640/previews/bondage.png) | ![free-4640](4640/previews/free.png) | ![maid-4640](4640/previews/maid.png) | ![miko-4640](4640/previews/miko.png) | [<NSFW, click to see>](4640/previews/nude.png) | [<NSFW, click to see>](4640/previews/nude2.png) | ![suit-4640](4640/previews/suit.png) | ![yukata-4640](4640/previews/yukata.png) | | 4060 | 0.838 | [Download](4060/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-4060](4060/previews/pattern_1.png) | ![pattern_2-4060](4060/previews/pattern_2.png) | ![pattern_3-4060](4060/previews/pattern_3.png) | ![pattern_4-4060](4060/previews/pattern_4.png) | ![pattern_5-4060](4060/previews/pattern_5.png) | ![pattern_6-4060](4060/previews/pattern_6.png) | ![pattern_7-4060](4060/previews/pattern_7.png) | ![pattern_8-4060](4060/previews/pattern_8.png) | ![pattern_9-4060](4060/previews/pattern_9.png) | ![pattern_10-4060](4060/previews/pattern_10.png) | ![pattern_11-4060](4060/previews/pattern_11.png) | ![bikini-4060](4060/previews/bikini.png) | [<NSFW, click to see>](4060/previews/bondage.png) | ![free-4060](4060/previews/free.png) | ![maid-4060](4060/previews/maid.png) | ![miko-4060](4060/previews/miko.png) | [<NSFW, click to see>](4060/previews/nude.png) | [<NSFW, click to see>](4060/previews/nude2.png) | ![suit-4060](4060/previews/suit.png) | ![yukata-4060](4060/previews/yukata.png) | | 3480 | 0.826 | [Download](3480/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-3480](3480/previews/pattern_1.png) | ![pattern_2-3480](3480/previews/pattern_2.png) | ![pattern_3-3480](3480/previews/pattern_3.png) | ![pattern_4-3480](3480/previews/pattern_4.png) | ![pattern_5-3480](3480/previews/pattern_5.png) | ![pattern_6-3480](3480/previews/pattern_6.png) | ![pattern_7-3480](3480/previews/pattern_7.png) | ![pattern_8-3480](3480/previews/pattern_8.png) | ![pattern_9-3480](3480/previews/pattern_9.png) | ![pattern_10-3480](3480/previews/pattern_10.png) | ![pattern_11-3480](3480/previews/pattern_11.png) | ![bikini-3480](3480/previews/bikini.png) | [<NSFW, click to see>](3480/previews/bondage.png) | ![free-3480](3480/previews/free.png) | ![maid-3480](3480/previews/maid.png) | ![miko-3480](3480/previews/miko.png) | [<NSFW, click to see>](3480/previews/nude.png) | [<NSFW, click to see>](3480/previews/nude2.png) | ![suit-3480](3480/previews/suit.png) | ![yukata-3480](3480/previews/yukata.png) | | 2900 | 0.832 | [Download](2900/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-2900](2900/previews/pattern_1.png) | ![pattern_2-2900](2900/previews/pattern_2.png) | ![pattern_3-2900](2900/previews/pattern_3.png) | ![pattern_4-2900](2900/previews/pattern_4.png) | ![pattern_5-2900](2900/previews/pattern_5.png) | ![pattern_6-2900](2900/previews/pattern_6.png) | ![pattern_7-2900](2900/previews/pattern_7.png) | ![pattern_8-2900](2900/previews/pattern_8.png) | ![pattern_9-2900](2900/previews/pattern_9.png) | ![pattern_10-2900](2900/previews/pattern_10.png) | ![pattern_11-2900](2900/previews/pattern_11.png) | ![bikini-2900](2900/previews/bikini.png) | [<NSFW, click to see>](2900/previews/bondage.png) | ![free-2900](2900/previews/free.png) | ![maid-2900](2900/previews/maid.png) | ![miko-2900](2900/previews/miko.png) | [<NSFW, click to see>](2900/previews/nude.png) | [<NSFW, click to see>](2900/previews/nude2.png) | ![suit-2900](2900/previews/suit.png) | ![yukata-2900](2900/previews/yukata.png) | | 2320 | 0.846 | [Download](2320/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-2320](2320/previews/pattern_1.png) | ![pattern_2-2320](2320/previews/pattern_2.png) | ![pattern_3-2320](2320/previews/pattern_3.png) | ![pattern_4-2320](2320/previews/pattern_4.png) | ![pattern_5-2320](2320/previews/pattern_5.png) | ![pattern_6-2320](2320/previews/pattern_6.png) | ![pattern_7-2320](2320/previews/pattern_7.png) | ![pattern_8-2320](2320/previews/pattern_8.png) | ![pattern_9-2320](2320/previews/pattern_9.png) | ![pattern_10-2320](2320/previews/pattern_10.png) | ![pattern_11-2320](2320/previews/pattern_11.png) | ![bikini-2320](2320/previews/bikini.png) | [<NSFW, click to see>](2320/previews/bondage.png) | ![free-2320](2320/previews/free.png) | ![maid-2320](2320/previews/maid.png) | ![miko-2320](2320/previews/miko.png) | [<NSFW, click to see>](2320/previews/nude.png) | [<NSFW, click to see>](2320/previews/nude2.png) | ![suit-2320](2320/previews/suit.png) | ![yukata-2320](2320/previews/yukata.png) | | 1740 | 0.818 | [Download](1740/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-1740](1740/previews/pattern_1.png) | ![pattern_2-1740](1740/previews/pattern_2.png) | ![pattern_3-1740](1740/previews/pattern_3.png) | ![pattern_4-1740](1740/previews/pattern_4.png) | ![pattern_5-1740](1740/previews/pattern_5.png) | ![pattern_6-1740](1740/previews/pattern_6.png) | ![pattern_7-1740](1740/previews/pattern_7.png) | ![pattern_8-1740](1740/previews/pattern_8.png) | ![pattern_9-1740](1740/previews/pattern_9.png) | ![pattern_10-1740](1740/previews/pattern_10.png) | ![pattern_11-1740](1740/previews/pattern_11.png) | ![bikini-1740](1740/previews/bikini.png) | [<NSFW, click to see>](1740/previews/bondage.png) | ![free-1740](1740/previews/free.png) | ![maid-1740](1740/previews/maid.png) | ![miko-1740](1740/previews/miko.png) | [<NSFW, click to see>](1740/previews/nude.png) | [<NSFW, click to see>](1740/previews/nude2.png) | ![suit-1740](1740/previews/suit.png) | ![yukata-1740](1740/previews/yukata.png) | | 1160 | 0.832 | [Download](1160/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-1160](1160/previews/pattern_1.png) | ![pattern_2-1160](1160/previews/pattern_2.png) | ![pattern_3-1160](1160/previews/pattern_3.png) | ![pattern_4-1160](1160/previews/pattern_4.png) | ![pattern_5-1160](1160/previews/pattern_5.png) | ![pattern_6-1160](1160/previews/pattern_6.png) | ![pattern_7-1160](1160/previews/pattern_7.png) | ![pattern_8-1160](1160/previews/pattern_8.png) | ![pattern_9-1160](1160/previews/pattern_9.png) | ![pattern_10-1160](1160/previews/pattern_10.png) | ![pattern_11-1160](1160/previews/pattern_11.png) | ![bikini-1160](1160/previews/bikini.png) | [<NSFW, click to see>](1160/previews/bondage.png) | ![free-1160](1160/previews/free.png) | ![maid-1160](1160/previews/maid.png) | ![miko-1160](1160/previews/miko.png) | [<NSFW, click to see>](1160/previews/nude.png) | [<NSFW, click to see>](1160/previews/nude2.png) | ![suit-1160](1160/previews/suit.png) | ![yukata-1160](1160/previews/yukata.png) | | 580 | 0.714 | [Download](580/luviagelita_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-580](580/previews/pattern_1.png) | ![pattern_2-580](580/previews/pattern_2.png) | ![pattern_3-580](580/previews/pattern_3.png) | ![pattern_4-580](580/previews/pattern_4.png) | ![pattern_5-580](580/previews/pattern_5.png) | 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DrishtiSharma/bert-large-uncased-hate-offensive-normal-speech-lr-2e-05
DrishtiSharma
2023-09-07T20:17:49Z
4
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T19:00:48Z
--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-large-uncased-hate-offensive-normal-speech-lr-2e-05 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-uncased-hate-offensive-normal-speech-lr-2e-05 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0097 - Accuracy: 0.9935 - Weighted f1: 0.9935 - Weighted recall: 0.9935 - Weighted precision: 0.9936 - Micro f1: 0.9935 - Micro recall: 0.9935 - Micro precision: 0.9935 - Macro f1: 0.9932 - Macro recall: 0.9938 - Macro precision: 0.9927 ## Model description More information needed ## Intended uses & 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 | Weighted f1 | Weighted recall | Weighted precision | Micro f1 | Micro recall | Micro precision | Macro f1 | Macro recall | Macro precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:| | 0.5454 | 1.0 | 153 | 0.0953 | 0.9739 | 0.9743 | 0.9739 | 0.9761 | 0.9739 | 0.9739 | 0.9739 | 0.9730 | 0.9752 | 0.9725 | | 0.0682 | 2.0 | 306 | 0.0285 | 0.9902 | 0.9903 | 0.9902 | 0.9905 | 0.9902 | 0.9902 | 0.9902 | 0.9896 | 0.9903 | 0.9891 | | 0.025 | 3.0 | 459 | 0.0381 | 0.9902 | 0.9903 | 0.9902 | 0.9905 | 0.9902 | 0.9902 | 0.9902 | 0.9896 | 0.9903 | 0.9891 | | 0.0212 | 4.0 | 612 | 0.0246 | 0.9902 | 0.9903 | 0.9902 | 0.9905 | 0.9902 | 0.9902 | 0.9902 | 0.9896 | 0.9903 | 0.9891 | | 0.0114 | 5.0 | 765 | 0.0097 | 0.9935 | 0.9935 | 0.9935 | 0.9936 | 0.9935 | 0.9935 | 0.9935 | 0.9932 | 0.9938 | 0.9927 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3
theodor1289/thesis_halfsize_text10_vision10
theodor1289
2023-09-07T20:00:50Z
38
0
transformers
[ "transformers", "pytorch", "flava", "pretraining", "endpoints_compatible", "region:us" ]
null
2023-09-07T19:46:52Z
flava_half-wit/date(2023-09-04)_time(16:56:17)/seed(5501650)-magic({'enable': True})-text_perc(10)-vision_perc(10/flava-epoch=00-step=13867.ckpt
Terps/mt5-small-finetuned-amazon-en-es
Terps
2023-09-07T19:46:31Z
8
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2023-09-07T18:42:36Z
--- license: apache-2.0 base_model: google/mt5-small tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-es 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. --> # mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0279 - Rouge1: 16.4284 - Rouge2: 7.8601 - Rougel: 16.0029 - Rougelsum: 16.0246 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 4.4194 | 1.0 | 1209 | 3.3097 | 14.9867 | 6.4886 | 14.4174 | 14.4646 | | 3.8132 | 2.0 | 2418 | 3.1602 | 16.1474 | 7.9815 | 15.5342 | 15.6445 | | 3.5412 | 3.0 | 3627 | 3.0789 | 17.4468 | 8.8014 | 16.9142 | 17.002 | | 3.3861 | 4.0 | 4836 | 3.0775 | 15.903 | 7.4423 | 15.4008 | 15.3871 | | 3.2952 | 5.0 | 6045 | 3.0480 | 15.8646 | 7.3936 | 15.3989 | 15.4395 | | 3.2155 | 6.0 | 7254 | 3.0354 | 16.5887 | 8.0624 | 16.2377 | 16.2562 | | 3.1896 | 7.0 | 8463 | 3.0273 | 17.1092 | 8.5391 | 16.6507 | 16.7272 | | 3.1594 | 8.0 | 9672 | 3.0279 | 16.4284 | 7.8601 | 16.0029 | 16.0246 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Onutoa/1_7e-3_1_0.5
Onutoa
2023-09-07T19:40:43Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T16:41:39Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: 1_7e-3_1_0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 1_7e-3_1_0.5 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4732 - Accuracy: 0.7462 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.007 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.9787 | 1.0 | 590 | 0.7825 | 0.6217 | | 1.0111 | 2.0 | 1180 | 0.7676 | 0.6021 | | 0.9238 | 3.0 | 1770 | 0.6005 | 0.6217 | | 0.8313 | 4.0 | 2360 | 0.6038 | 0.4321 | | 0.7671 | 5.0 | 2950 | 0.9066 | 0.6217 | | 0.7472 | 6.0 | 3540 | 0.6074 | 0.4560 | | 0.7577 | 7.0 | 4130 | 0.6978 | 0.3807 | | 0.6835 | 8.0 | 4720 | 0.6612 | 0.6217 | | 0.6855 | 9.0 | 5310 | 0.7161 | 0.6217 | | 0.6572 | 10.0 | 5900 | 0.5321 | 0.6370 | | 0.6389 | 11.0 | 6490 | 0.5122 | 0.6621 | | 0.5993 | 12.0 | 7080 | 0.5795 | 0.6612 | | 0.587 | 13.0 | 7670 | 0.5287 | 0.6245 | | 0.5662 | 14.0 | 8260 | 0.4982 | 0.6664 | | 0.5474 | 15.0 | 8850 | 0.5174 | 0.6453 | | 0.5533 | 16.0 | 9440 | 0.5125 | 0.6890 | | 0.5201 | 17.0 | 10030 | 0.4753 | 0.6716 | | 0.5055 | 18.0 | 10620 | 0.4841 | 0.6755 | | 0.4886 | 19.0 | 11210 | 0.4682 | 0.7028 | | 0.4806 | 20.0 | 11800 | 0.4591 | 0.6905 | | 0.456 | 21.0 | 12390 | 0.4729 | 0.6896 | | 0.4627 | 22.0 | 12980 | 0.4434 | 0.7003 | | 0.4301 | 23.0 | 13570 | 0.4426 | 0.7092 | | 0.4203 | 24.0 | 14160 | 0.4324 | 0.7092 | | 0.4175 | 25.0 | 14750 | 0.4642 | 0.7275 | | 0.3993 | 26.0 | 15340 | 0.5582 | 0.6459 | | 0.3972 | 27.0 | 15930 | 0.4367 | 0.7076 | | 0.3812 | 28.0 | 16520 | 0.4484 | 0.7278 | | 0.3726 | 29.0 | 17110 | 0.4581 | 0.7202 | | 0.3781 | 30.0 | 17700 | 0.4322 | 0.7275 | | 0.3578 | 31.0 | 18290 | 0.4970 | 0.7217 | | 0.3458 | 32.0 | 18880 | 0.6182 | 0.7095 | | 0.3434 | 33.0 | 19470 | 0.4644 | 0.7095 | | 0.3338 | 34.0 | 20060 | 0.4355 | 0.7199 | | 0.3344 | 35.0 | 20650 | 0.4495 | 0.7223 | | 0.3308 | 36.0 | 21240 | 0.4515 | 0.7330 | | 0.3208 | 37.0 | 21830 | 0.4562 | 0.7373 | | 0.3012 | 38.0 | 22420 | 0.4464 | 0.7211 | | 0.3055 | 39.0 | 23010 | 0.4410 | 0.7382 | | 0.306 | 40.0 | 23600 | 0.5016 | 0.7343 | | 0.2894 | 41.0 | 24190 | 0.4726 | 0.7364 | | 0.2834 | 42.0 | 24780 | 0.4714 | 0.7379 | | 0.2789 | 43.0 | 25370 | 0.4379 | 0.7199 | | 0.2759 | 44.0 | 25960 | 0.4570 | 0.7287 | | 0.2667 | 45.0 | 26550 | 0.4500 | 0.7294 | | 0.2564 | 46.0 | 27140 | 0.4628 | 0.7413 | | 0.2541 | 47.0 | 27730 | 0.4643 | 0.7379 | | 0.2498 | 48.0 | 28320 | 0.4406 | 0.7336 | | 0.2571 | 49.0 | 28910 | 0.4427 | 0.7373 | | 0.2423 | 50.0 | 29500 | 0.4658 | 0.7315 | | 0.2374 | 51.0 | 30090 | 0.4744 | 0.7214 | | 0.2415 | 52.0 | 30680 | 0.5416 | 0.7373 | | 0.2309 | 53.0 | 31270 | 0.4830 | 0.7226 | | 0.2282 | 54.0 | 31860 | 0.4758 | 0.7343 | | 0.2307 | 55.0 | 32450 | 0.4698 | 0.7266 | | 0.2213 | 56.0 | 33040 | 0.4458 | 0.7446 | | 0.2193 | 57.0 | 33630 | 0.4778 | 0.7382 | | 0.214 | 58.0 | 34220 | 0.4828 | 0.7456 | | 0.207 | 59.0 | 34810 | 0.4818 | 0.7294 | | 0.21 | 60.0 | 35400 | 0.4614 | 0.7508 | | 0.2118 | 61.0 | 35990 | 0.4507 | 0.7480 | | 0.2031 | 62.0 | 36580 | 0.4718 | 0.7416 | | 0.1987 | 63.0 | 37170 | 0.4752 | 0.7324 | | 0.2018 | 64.0 | 37760 | 0.4431 | 0.7388 | | 0.1889 | 65.0 | 38350 | 0.4769 | 0.7385 | | 0.1941 | 66.0 | 38940 | 0.4623 | 0.7443 | | 0.1898 | 67.0 | 39530 | 0.4818 | 0.7355 | | 0.1872 | 68.0 | 40120 | 0.4678 | 0.7446 | | 0.1813 | 69.0 | 40710 | 0.4843 | 0.7529 | | 0.1893 | 70.0 | 41300 | 0.4702 | 0.7459 | | 0.1885 | 71.0 | 41890 | 0.4931 | 0.7193 | | 0.1811 | 72.0 | 42480 | 0.4854 | 0.7477 | | 0.1755 | 73.0 | 43070 | 0.4848 | 0.7373 | | 0.1768 | 74.0 | 43660 | 0.4867 | 0.7520 | | 0.1728 | 75.0 | 44250 | 0.5011 | 0.7477 | | 0.1791 | 76.0 | 44840 | 0.4876 | 0.7416 | | 0.1733 | 77.0 | 45430 | 0.4920 | 0.7486 | | 0.1745 | 78.0 | 46020 | 0.4711 | 0.7492 | | 0.1741 | 79.0 | 46610 | 0.4661 | 0.7401 | | 0.1706 | 80.0 | 47200 | 0.4670 | 0.7422 | | 0.165 | 81.0 | 47790 | 0.4736 | 0.7459 | | 0.1612 | 82.0 | 48380 | 0.4660 | 0.7459 | | 0.1722 | 83.0 | 48970 | 0.4772 | 0.7410 | | 0.1638 | 84.0 | 49560 | 0.4767 | 0.7434 | | 0.1613 | 85.0 | 50150 | 0.4641 | 0.7391 | | 0.1649 | 86.0 | 50740 | 0.4783 | 0.7450 | | 0.1609 | 87.0 | 51330 | 0.4734 | 0.7453 | | 0.1588 | 88.0 | 51920 | 0.4919 | 0.7508 | | 0.1601 | 89.0 | 52510 | 0.4698 | 0.7453 | | 0.1573 | 90.0 | 53100 | 0.4765 | 0.7508 | | 0.1584 | 91.0 | 53690 | 0.4754 | 0.7492 | | 0.1587 | 92.0 | 54280 | 0.4704 | 0.7413 | | 0.1521 | 93.0 | 54870 | 0.4865 | 0.7505 | | 0.1546 | 94.0 | 55460 | 0.4777 | 0.7505 | | 0.1539 | 95.0 | 56050 | 0.4791 | 0.7526 | | 0.1545 | 96.0 | 56640 | 0.4721 | 0.7456 | | 0.1533 | 97.0 | 57230 | 0.4725 | 0.7407 | | 0.1476 | 98.0 | 57820 | 0.4709 | 0.7462 | | 0.1489 | 99.0 | 58410 | 0.4731 | 0.7459 | | 0.1501 | 100.0 | 59000 | 0.4732 | 0.7462 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
billfass/my_bert_model
billfass
2023-09-07T19:31:20Z
122
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-07T15:19:43Z
# Custom BERT Model for Text Classification ## Model Description This is a custom BERT model fine-tuned for text classification. The model was trained using a subset of a publicly available dataset and is capable of classifying text into 3 classes. ## Training Details - **Architecture**: BERT Base Multilingual Cased - **Training data**: Custom dataset - **Preprocessing**: Tokenized using BERT's tokenizer, with a max sequence length of 80. - **Fine-tuning**: The model was trained for 1 epoch with a learning rate of 2e-5, using AdamW optimizer and Cross-Entropy Loss. - **Evaluation Metrics**: Accuracy on a held-out validation set. ## How to Use ### Dependencies - Transformers 4.x - Torch 1.x ### Code Snippet For classification: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch tokenizer = AutoTokenizer.from_pretrained("billfass/my_bert_model") model = AutoModelForSequenceClassification.from_pretrained("billfass/my_bert_model") text = "Your example text here." inputs = tokenizer(text, padding=True, truncation=True, max_length=80, return_tensors="pt") labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(**inputs, labels=labels) loss = outputs.loss logits = outputs.logits # To get probabilities: probs = torch.softmax(logits, dim=-1) ``` ## Limitations and Bias - Trained on a specific dataset, so may not generalize well to other kinds of text. - Uses multilingual cased BERT, so it's not optimized for any specific language. ## Authors - **Fassinou Bile** - **billfass2010@gmail.com** ## Acknowledgments Special thanks to Hugging Face for providing the Transformers library that made this project possible. ---
PHL99/dqn-SpaceInvaders-v4-NoFrameskip
PHL99
2023-09-07T19:19:26Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-07T19:18:55Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 535.50 +/- 107.04 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PHL99 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PHL99 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga PHL99 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
TencentARC/t2i-adapter-depth-midas-sdxl-1.0
TencentARC
2023-09-07T19:11:24Z
5,283
31
diffusers
[ "diffusers", "safetensors", "art", "t2i-adapter", "image-to-image", "stable-diffusion-xl-diffusers", "stable-diffusion-xl", "arxiv:2302.08453", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:apache-2.0", "region:us" ]
image-to-image
2023-09-03T14:46:44Z
--- license: apache-2.0 base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - art - t2i-adapter - image-to-image - stable-diffusion-xl-diffusers - stable-diffusion-xl --- # T2I-Adapter-SDXL - Depth-MiDaS T2I Adapter is a network providing additional conditioning to stable diffusion. Each t2i checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint. This checkpoint provides conditioning on depth for the StableDiffusionXL checkpoint. This was a collaboration between **Tencent ARC** and [**Hugging Face**](https://huggingface.co/). ## Model Details - **Developed by:** T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** Apache 2.0 - **Resources for more information:** [GitHub Repository](https://github.com/TencentARC/T2I-Adapter), [Paper](https://arxiv.org/abs/2302.08453). - **Model complexity:** | | SD-V1.4/1.5 | SD-XL | T2I-Adapter | T2I-Adapter-SDXL | | --- | --- |--- |--- |--- | | Parameters | 860M | 2.6B |77 M | 77/79 M | | - **Cite as:** @misc{ title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models}, author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie}, year={2023}, eprint={2302.08453}, archivePrefix={arXiv}, primaryClass={cs.CV} } ### Checkpoints | Model Name | Control Image Overview| Control Image Example | Generated Image Example | |---|---|---|---| |[TencentARC/t2i-adapter-canny-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"/></a>| |[TencentARC/t2i-adapter-sketch-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-sketch-sdxl-1.0)<br/> *Trained with [PidiNet](https://github.com/zhuoinoulu/pidinet) edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_sketch.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_sketch.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_sketch.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_sketch.png"/></a>| |[TencentARC/t2i-adapter-lineart-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-lineart-sdxl-1.0)<br/> *Trained with lineart edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_lin.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_lin.png"/></a>| |[TencentARC/t2i-adapter-depth-midas-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-depth-midas-sdxl-1.0)<br/> *Trained with Midas depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_mid.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_mid.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_mid.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_mid.png"/></a>| |[TencentARC/t2i-adapter-depth-zoe-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-depth-zoe-sdxl-1.0)<br/> *Trained with Zoe depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_zeo.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_zeo.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_zeo.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_zeo.png"/></a>| |[TencentARC/t2i-adapter-openpose-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-openpose-sdxl-1.0)<br/> *Trained with OpenPose bone image* | A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/openpose.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/openpose.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/res_pose.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/res_pose.png"/></a>| ## Example To get started, first install the required dependencies: ```bash pip install -U git+https://github.com/huggingface/diffusers.git pip install -U controlnet_aux==0.0.7 # for conditioning models and detectors pip install transformers accelerate safetensors ``` 1. Images are first downloaded into the appropriate *control image* format. 2. The *control image* and *prompt* are passed to the [`StableDiffusionXLAdapterPipeline`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py#L125). Let's have a look at a simple example using the [Canny Adapter](https://huggingface.co/TencentARC/t2i-adapter-lineart-sdxl-1.0). - Dependency ```py from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL from diffusers.utils import load_image, make_image_grid from controlnet_aux.midas import MidasDetector import torch # load adapter adapter = T2IAdapter.from_pretrained( "TencentARC/t2i-adapter-depth-midas-sdxl-1.0", torch_dtype=torch.float16, varient="fp16" ).to("cuda") # load euler_a scheduler model_id = 'stabilityai/stable-diffusion-xl-base-1.0' euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = StableDiffusionXLAdapterPipeline.from_pretrained( model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16", ).to("cuda") pipe.enable_xformers_memory_efficient_attention() midas_depth = MidasDetector.from_pretrained( "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large" ).to("cuda") ``` - Condition Image ```py url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_mid.jpg" image = load_image(url) image = midas_depth( image, detect_resolution=512, image_resolution=1024 ) ``` <a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_mid.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_mid.png"/></a> - Generation ```py prompt = "A photo of a room, 4k photo, highly detailed" negative_prompt = "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" gen_images = pipe( prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=30, adapter_conditioning_scale=1, guidance_scale=7.5, ).images[0] gen_images.save('out_mid.png') ``` <a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_mid.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_mid.png"/></a> ### Training Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/t2i_adapter/README_sdxl.md). The model is trained on 3M high-resolution image-text pairs from LAION-Aesthetics V2 with - Training steps: 35000 - Batch size: Data parallel with a single gpu batch size of `16` for a total batch size of `256`. - Learning rate: Constant learning rate of `1e-5`. - Mixed precision: fp16
TencentARC/t2i-adapter-canny-sdxl-1.0
TencentARC
2023-09-07T19:10:05Z
6,149
48
diffusers
[ "diffusers", "safetensors", "art", "t2i-adapter", "image-to-image", "stable-diffusion-xl-diffusers", "stable-diffusion-xl", "arxiv:2302.08453", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:apache-2.0", "region:us" ]
image-to-image
2023-09-03T14:19:29Z
--- license: apache-2.0 base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - art - t2i-adapter - image-to-image - stable-diffusion-xl-diffusers - stable-diffusion-xl --- # T2I-Adapter-SDXL - Canny T2I Adapter is a network providing additional conditioning to stable diffusion. Each t2i checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint. This checkpoint provides conditioning on canny for the StableDiffusionXL checkpoint. This was a collaboration between **Tencent ARC** and [**Hugging Face**](https://huggingface.co/). ## Model Details - **Developed by:** T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** Apache 2.0 - **Resources for more information:** [GitHub Repository](https://github.com/TencentARC/T2I-Adapter), [Paper](https://arxiv.org/abs/2302.08453). - **Model complexity:** | | SD-V1.4/1.5 | SD-XL | T2I-Adapter | T2I-Adapter-SDXL | | --- | --- |--- |--- |--- | | Parameters | 860M | 2.6B |77 M | 77/79 M | | - **Cite as:** @misc{ title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models}, author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie}, year={2023}, eprint={2302.08453}, archivePrefix={arXiv}, primaryClass={cs.CV} } ### Checkpoints | Model Name | Control Image Overview| Control Image Example | Generated Image Example | |---|---|---|---| |[TencentARC/t2i-adapter-canny-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"/></a>| |[TencentARC/t2i-adapter-sketch-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-sketch-sdxl-1.0)<br/> *Trained with [PidiNet](https://github.com/zhuoinoulu/pidinet) edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_sketch.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_sketch.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_sketch.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_sketch.png"/></a>| |[TencentARC/t2i-adapter-lineart-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-lineart-sdxl-1.0)<br/> *Trained with lineart edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_lin.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_lin.png"/></a>| |[TencentARC/t2i-adapter-depth-midas-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-depth-midas-sdxl-1.0)<br/> *Trained with Midas depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_mid.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_mid.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_mid.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_mid.png"/></a>| |[TencentARC/t2i-adapter-depth-zoe-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-depth-zoe-sdxl-1.0)<br/> *Trained with Zoe depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_zeo.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_depth_zeo.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_zeo.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_depth_zeo.png"/></a>| |[TencentARC/t2i-adapter-openpose-sdxl-1.0](https://huggingface.co/TencentARC/t2i-adapter-openpose-sdxl-1.0)<br/> *Trained with OpenPose bone image* | A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/openpose.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/openpose.png"/></a>|<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/res_pose.png"><img width="64" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/res_pose.png"/></a>| ## Example To get started, first install the required dependencies: ```bash pip install -U git+https://github.com/huggingface/diffusers.git pip install -U controlnet_aux==0.0.7 # for conditioning models and detectors pip install transformers accelerate safetensors ``` 1. Images are first downloaded into the appropriate *control image* format. 2. The *control image* and *prompt* are passed to the [`StableDiffusionXLAdapterPipeline`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py#L125). Let's have a look at a simple example using the [Canny Adapter](https://huggingface.co/Adapter/t2iadapter_canny_sdxlv1). - Dependency ```py from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL from diffusers.utils import load_image, make_image_grid from controlnet_aux.canny import CannyDetector import torch # load adapter adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda") # load euler_a scheduler model_id = 'stabilityai/stable-diffusion-xl-base-1.0' euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = StableDiffusionXLAdapterPipeline.from_pretrained( model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16", ).to("cuda") pipe.enable_xformers_memory_efficient_attention() canny_detector = CannyDetector() ``` - Condition Image ```py url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg" image = load_image(url) # Detect the canny map in low resolution to avoid high-frequency details image = canny_detector(image, detect_resolution=384, image_resolution=1024)#.resize((1024, 1024)) ``` <a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"/></a> - Generation ```py prompt = "Mystical fairy in real, magic, 4k picture, high quality" negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured" gen_images = pipe( prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=30, guidance_scale=7.5, adapter_conditioning_scale=0.8, adapter_conditioning_factor=1 ).images[0] gen_images.save('out_canny.png') ``` <a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_canny.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_canny.png"/></a> ### Training Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/t2i_adapter/README_sdxl.md). The model is trained on 3M high-resolution image-text pairs from LAION-Aesthetics V2 with - Training steps: 20000 - Batch size: Data parallel with a single gpu batch size of `16` for a total batch size of `256`. - Learning rate: Constant learning rate of `1e-5`. - Mixed precision: fp16
slhoefel/distilbert-base-uncased-DON-mask-lemma
slhoefel
2023-09-07T19:09:30Z
78
0
transformers
[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-07T18:42:57Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: slhoefel/distilbert-base-uncased-DON-mask-lemma results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # slhoefel/distilbert-base-uncased-DON-mask-lemma This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.6919 - Validation Loss: 3.3336 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -967, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.6919 | 3.3336 | 0 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.0 - Datasets 2.12.0 - Tokenizers 0.13.2
Koltunov-Matthew/my_model
Koltunov-Matthew
2023-09-07T19:08:26Z
23
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "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" ]
text2text-generation
2023-09-07T16:05:37Z
--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0550 - Rouge1: 0.4076 - Rouge2: 0.2169 - Rougel: 0.3655 - Rougelsum: 0.3654 - Gen Len: 14.4845 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 2.3027 | 1.0 | 6750 | 2.1348 | 0.3964 | 0.2084 | 0.3554 | 0.3552 | 14.5291 | | 2.2589 | 2.0 | 13500 | 2.0818 | 0.4021 | 0.2127 | 0.3603 | 0.3602 | 14.6178 | | 2.227 | 3.0 | 20250 | 2.0605 | 0.4067 | 0.2167 | 0.365 | 0.3649 | 14.4537 | | 2.2137 | 4.0 | 27000 | 2.0550 | 0.4076 | 0.2169 | 0.3655 | 0.3654 | 14.4845 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.0 - Datasets 2.11.0 - Tokenizers 0.13.3
facebook/mask2former-swin-large-cityscapes-panoptic
facebook
2023-09-07T18:57:04Z
1,317
0
transformers
[ "transformers", "pytorch", "safetensors", "mask2former", "vision", "image-segmentation", "dataset:coco", "arxiv:2112.01527", "arxiv:2107.06278", "license:other", "endpoints_compatible", "region:us" ]
image-segmentation
2023-01-03T11:42:47Z
--- license: other tags: - vision - image-segmentation datasets: - coco widget: - src: http://images.cocodataset.org/val2017/000000039769.jpg example_title: Cats - src: http://images.cocodataset.org/val2017/000000039770.jpg example_title: Castle --- # Mask2Former Mask2Former model trained on Cityscapes panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on Cityscapes panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-cityscapes-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-cityscapes-panoptic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing result = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) predicted_panoptic_map = result["segmentation"] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).
facebook/maskformer-swin-small-ade
facebook
2023-09-07T18:56:38Z
309
2
transformers
[ "transformers", "pytorch", "safetensors", "maskformer", "vision", "image-segmentation", "dataset:scene_parse_150", "arxiv:2107.06278", "license:other", "endpoints_compatible", "region:us" ]
image-segmentation
2022-03-02T23:29:05Z
--- license: other tags: - vision - image-segmentation datasets: - scene_parse_150 widget: - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg example_title: House - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg example_title: Castle --- # MaskFormer MaskFormer model trained on ADE20k semantic segmentation (small-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169). Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description MaskFormer addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/maskformer_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation from PIL import Image import requests url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-small-ade") inputs = feature_extractor(images=image, return_tensors="pt") model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-ade") outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to feature_extractor for postprocessing # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs) predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).
fastbond/llama-2-7b-guanaco-dolly-test-500step
fastbond
2023-09-07T18:54:58Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-07T18:54:50Z
--- 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
prashanth07/ard_docs_check
prashanth07
2023-09-07T18:47:54Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-07T18:40:29Z
--- 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
osieosie/bloom-mnli-4bit-560m-bnb-seed42
osieosie
2023-09-07T18:42:27Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-06T17:22:32Z
--- 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: fp4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
badhorse666/q-taxi-v3
badhorse666
2023-09-07T18:40:24Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-09-07T18:38:56Z
--- 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="badhorse666/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"]) ```
rigon-tk/ppo-Huggy
rigon-tk
2023-09-07T18:36:55Z
5
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-09-07T18:36:47Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: rigon-tk/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
CristianEstupinan/ppo-LunarLander-v2
CristianEstupinan
2023-09-07T18:33:26Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-06T11:05:47Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 280.21 +/- 25.90 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 ... ```
bongo2112/sd-15-db-mulokoziepk
bongo2112
2023-09-07T18:16:02Z
2
0
diffusers
[ "diffusers", "text-to-image", "autotrain", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "region:us" ]
text-to-image
2023-09-02T16:30:48Z
--- base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of mulokoziepk man tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
mahimairaja/tweet-summarization-llama-2-finetuned
mahimairaja
2023-09-07T18:09:56Z
20
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "dataset:Salesforce/dialogstudio", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-07T10:43:33Z
--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - Salesforce/dialogstudio model-index: - name: tweet-summarization-llama-2-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tweet-summarization-llama-2-finetuned This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the Salesforce/dialogstudio dataset. It achieves the following results on the evaluation set: - Loss: 1.8672 ## Model description More information needed ## Intended uses & 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8996 | 1.0 | 55 | 1.9491 | | 1.8415 | 2.0 | 110 | 1.8857 | | 1.7693 | 3.0 | 165 | 1.8749 | | 1.7136 | 4.0 | 220 | 1.8678 | | 1.7533 | 5.0 | 275 | 1.8663 | | 1.6182 | 6.0 | 330 | 1.8665 | | 1.69 | 7.0 | 385 | 1.8672 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
anupamtripathi/model_sd2_new_data
anupamtripathi
2023-09-07T17:55:56Z
1
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-07T04:10:04Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a photo of Beavertail Pastry food products tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - anupamtripathi/model_sd2_new_data These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on a photo of Beavertail Pastry food products using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
Sonny4Sonnix/movie_sentiment_trainer
Sonny4Sonnix
2023-09-07T17:49:58Z
106
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:Sonny4Sonnix/twitter-roberta-base-sentimental-analysis-of-covid-tweets", "base_model:finetune:Sonny4Sonnix/twitter-roberta-base-sentimental-analysis-of-covid-tweets", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T08:45:17Z
--- base_model: Sonny4Sonnix/twitter-roberta-base-sentimental-analysis-of-covid-tweets tags: - generated_from_trainer model-index: - name: movie_sentiment_trainer 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. --> # movie_sentiment_trainer This model is a fine-tuned version of [Sonny4Sonnix/twitter-roberta-base-sentimental-analysis-of-covid-tweets](https://huggingface.co/Sonny4Sonnix/twitter-roberta-base-sentimental-analysis-of-covid-tweets) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6934 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7168 | 0.2 | 500 | 0.6982 | | 0.7017 | 0.4 | 1000 | 0.6971 | | 0.6995 | 0.6 | 1500 | 0.7128 | | 0.7027 | 0.8 | 2000 | 0.7011 | | 0.7046 | 1.0 | 2500 | 0.6937 | | 0.698 | 1.2 | 3000 | 0.6938 | | 0.6988 | 1.4 | 3500 | 0.6932 | | 0.6972 | 1.6 | 4000 | 0.6935 | | 0.698 | 1.8 | 4500 | 0.6940 | | 0.6975 | 2.0 | 5000 | 0.6973 | | 0.6977 | 2.2 | 5500 | 0.6932 | | 0.6955 | 2.4 | 6000 | 0.6933 | | 0.6952 | 2.6 | 6500 | 0.6932 | | 0.6946 | 2.8 | 7000 | 0.6941 | | 0.6944 | 3.0 | 7500 | 0.6934 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
chunyuu/results
chunyuu
2023-09-07T17:49:34Z
107
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-07T17:46:20Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) 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: 3e-06 - 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: 10 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Onutoa/1_9e-3_5_0.1
Onutoa
2023-09-07T17:44:57Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T14:46:32Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: 1_9e-3_5_0.1 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. --> # 1_9e-3_5_0.1 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9096 - Accuracy: 0.7495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.009 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.6689 | 1.0 | 590 | 1.8930 | 0.3792 | | 1.4177 | 2.0 | 1180 | 1.1713 | 0.6217 | | 1.4671 | 3.0 | 1770 | 0.9910 | 0.4239 | | 1.2704 | 4.0 | 2360 | 1.0000 | 0.4969 | | 1.1101 | 5.0 | 2950 | 0.8316 | 0.6459 | | 1.0767 | 6.0 | 3540 | 0.9325 | 0.6428 | | 1.0047 | 7.0 | 4130 | 1.4778 | 0.4725 | | 0.9251 | 8.0 | 4720 | 0.7582 | 0.6801 | | 0.8846 | 9.0 | 5310 | 0.8984 | 0.6737 | | 0.8439 | 10.0 | 5900 | 0.8034 | 0.7018 | | 0.8068 | 11.0 | 6490 | 0.8305 | 0.6624 | | 0.7643 | 12.0 | 7080 | 1.0910 | 0.5859 | | 0.7306 | 13.0 | 7670 | 0.7682 | 0.6908 | | 0.6488 | 14.0 | 8260 | 0.7171 | 0.7226 | | 0.6521 | 15.0 | 8850 | 0.6864 | 0.7202 | | 0.6048 | 16.0 | 9440 | 0.7442 | 0.7260 | | 0.5536 | 17.0 | 10030 | 1.0092 | 0.6532 | | 0.5654 | 18.0 | 10620 | 0.7884 | 0.7052 | | 0.5349 | 19.0 | 11210 | 0.7640 | 0.7073 | | 0.4958 | 20.0 | 11800 | 0.7724 | 0.7343 | | 0.4706 | 21.0 | 12390 | 0.7728 | 0.7183 | | 0.459 | 22.0 | 12980 | 0.7394 | 0.7254 | | 0.4362 | 23.0 | 13570 | 0.7550 | 0.7196 | | 0.4176 | 24.0 | 14160 | 0.7744 | 0.7248 | | 0.4012 | 25.0 | 14750 | 0.8998 | 0.7364 | | 0.388 | 26.0 | 15340 | 0.9046 | 0.7104 | | 0.3852 | 27.0 | 15930 | 0.7894 | 0.7278 | | 0.3737 | 28.0 | 16520 | 0.8274 | 0.7391 | | 0.3456 | 29.0 | 17110 | 0.7725 | 0.7471 | | 0.34 | 30.0 | 17700 | 0.9009 | 0.7260 | | 0.3247 | 31.0 | 18290 | 0.7733 | 0.7398 | | 0.3197 | 32.0 | 18880 | 0.8370 | 0.7385 | | 0.3109 | 33.0 | 19470 | 0.8705 | 0.7269 | | 0.3047 | 34.0 | 20060 | 0.8475 | 0.7373 | | 0.2815 | 35.0 | 20650 | 0.9676 | 0.7407 | | 0.2782 | 36.0 | 21240 | 0.8183 | 0.7450 | | 0.2808 | 37.0 | 21830 | 0.8551 | 0.7394 | | 0.2639 | 38.0 | 22420 | 0.9552 | 0.7440 | | 0.2599 | 39.0 | 23010 | 0.8785 | 0.7422 | | 0.2563 | 40.0 | 23600 | 1.0538 | 0.7364 | | 0.2471 | 41.0 | 24190 | 0.9479 | 0.7502 | | 0.2524 | 42.0 | 24780 | 0.9348 | 0.7398 | | 0.2419 | 43.0 | 25370 | 0.9101 | 0.7401 | | 0.2338 | 44.0 | 25960 | 0.8726 | 0.7394 | | 0.2218 | 45.0 | 26550 | 0.8953 | 0.7416 | | 0.2115 | 46.0 | 27140 | 0.8966 | 0.7291 | | 0.2234 | 47.0 | 27730 | 0.9359 | 0.7416 | | 0.2047 | 48.0 | 28320 | 0.9434 | 0.7284 | | 0.2218 | 49.0 | 28910 | 0.9202 | 0.7465 | | 0.2075 | 50.0 | 29500 | 0.8866 | 0.7394 | | 0.1982 | 51.0 | 30090 | 0.9081 | 0.7358 | | 0.2064 | 52.0 | 30680 | 0.9691 | 0.7321 | | 0.1955 | 53.0 | 31270 | 0.9527 | 0.7275 | | 0.2006 | 54.0 | 31860 | 0.8744 | 0.7456 | | 0.2021 | 55.0 | 32450 | 0.9529 | 0.7419 | | 0.1932 | 56.0 | 33040 | 0.9040 | 0.7391 | | 0.1823 | 57.0 | 33630 | 0.9188 | 0.7382 | | 0.1726 | 58.0 | 34220 | 0.8715 | 0.7385 | | 0.1867 | 59.0 | 34810 | 0.9165 | 0.7410 | | 0.1831 | 60.0 | 35400 | 0.9393 | 0.7431 | | 0.1741 | 61.0 | 35990 | 0.9843 | 0.7502 | | 0.1687 | 62.0 | 36580 | 0.9161 | 0.7419 | | 0.1712 | 63.0 | 37170 | 0.9630 | 0.7431 | | 0.1742 | 64.0 | 37760 | 0.9306 | 0.7443 | | 0.1721 | 65.0 | 38350 | 0.9384 | 0.7446 | | 0.1614 | 66.0 | 38940 | 0.9237 | 0.7401 | | 0.1631 | 67.0 | 39530 | 0.9315 | 0.7404 | | 0.1626 | 68.0 | 40120 | 0.8884 | 0.7434 | | 0.1547 | 69.0 | 40710 | 0.9163 | 0.7483 | | 0.1609 | 70.0 | 41300 | 0.9340 | 0.7422 | | 0.1592 | 71.0 | 41890 | 0.9292 | 0.7352 | | 0.1588 | 72.0 | 42480 | 0.8887 | 0.7495 | | 0.1504 | 73.0 | 43070 | 0.9228 | 0.7480 | | 0.1422 | 74.0 | 43660 | 0.9570 | 0.7361 | | 0.1535 | 75.0 | 44250 | 0.9705 | 0.7446 | | 0.1486 | 76.0 | 44840 | 0.9364 | 0.7477 | | 0.146 | 77.0 | 45430 | 0.9385 | 0.7517 | | 0.1519 | 78.0 | 46020 | 0.8991 | 0.7495 | | 0.148 | 79.0 | 46610 | 0.9516 | 0.7483 | | 0.1388 | 80.0 | 47200 | 0.9189 | 0.7462 | | 0.1392 | 81.0 | 47790 | 0.8985 | 0.7474 | | 0.1426 | 82.0 | 48380 | 0.9112 | 0.7459 | | 0.1388 | 83.0 | 48970 | 0.9468 | 0.7456 | | 0.1396 | 84.0 | 49560 | 0.9185 | 0.7474 | | 0.1316 | 85.0 | 50150 | 0.9230 | 0.7434 | | 0.1332 | 86.0 | 50740 | 0.9365 | 0.7388 | | 0.1245 | 87.0 | 51330 | 0.9405 | 0.7502 | | 0.1283 | 88.0 | 51920 | 0.9384 | 0.7453 | | 0.1309 | 89.0 | 52510 | 0.9250 | 0.7483 | | 0.127 | 90.0 | 53100 | 0.9176 | 0.7434 | | 0.124 | 91.0 | 53690 | 0.9207 | 0.7446 | | 0.1294 | 92.0 | 54280 | 0.8949 | 0.7489 | | 0.1322 | 93.0 | 54870 | 0.9154 | 0.7495 | | 0.1242 | 94.0 | 55460 | 0.9033 | 0.7508 | | 0.1251 | 95.0 | 56050 | 0.9201 | 0.7502 | | 0.1174 | 96.0 | 56640 | 0.9043 | 0.7480 | | 0.1284 | 97.0 | 57230 | 0.9111 | 0.7489 | | 0.1188 | 98.0 | 57820 | 0.9175 | 0.7489 | | 0.1201 | 99.0 | 58410 | 0.9150 | 0.7498 | | 0.1229 | 100.0 | 59000 | 0.9096 | 0.7495 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
CristianEstupinan/ppo-Huggy
CristianEstupinan
2023-09-07T17:44:17Z
7
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-09-07T17:44:13Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: CristianEstupinan/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
chakra17/pokemon-lora
chakra17
2023-09-07T17:38:09Z
3
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
2023-09-07T17:19:03Z
--- 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 - chakra17/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
mariogiordano/Bert-emotion-analysis
mariogiordano
2023-09-07T17:38:06Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T16:38:31Z
--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: Bert-emotion-analysis 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-emotion-analysis This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1244 - Accuracy: 0.6220 - F1: 0.6112 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 83 | 1.3491 | 0.5572 | 0.5410 | | No log | 2.0 | 166 | 1.1244 | 0.6220 | 0.6112 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
vinayaksodar/ppo-Huggy
vinayaksodar
2023-09-07T17:06:05Z
2
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-09-07T17:05:53Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vinayaksodar/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0
turing-motors
2023-09-07T16:59:14Z
6
8
transformers
[ "transformers", "pytorch", "video_blip", "text2text-generation", "heron", "vision", "image-captioning", "VQA", "image-to-text", "ja", "arxiv:2301.12597", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]
image-to-text
2023-09-06T09:31:44Z
--- language: - ja tags: - heron - vision - image-captioning - VQA pipeline_tag: image-to-text license: - cc-by-nc-4.0 inference: false --- # Heron BLIP Japanese StableLM Base 7B ![heron](./heron_image.png) ## DEMO You can play the demo of this model [here](https://huggingface.co/spaces/turing-motors/heron_chat_blip). ## Model Details Heron BLIP Japanese StableLM Base 7B is a vision-language model that can converse about input images.<br> This model was trained using [the heron library](https://github.com/turingmotors/heron). Please refer to the code for details. ## Usage Follow [the installation guide](https://github.com/turingmotors/heron/tree/dev-0.0.1#1-clone-this-repository). ```python import torch from heron.models.video_blip import VideoBlipForConditionalGeneration, VideoBlipProcessor from transformers import LlamaTokenizer device_id = 0 device = f"cuda:{device_id}" max_length = 512 MODEL_NAME = "turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0" model = VideoBlipForConditionalGeneration.from_pretrained( MODEL_NAME, torch_dtype=torch.float16, ignore_mismatched_sizes=True ) model = model.half() model.eval() model.to(device) # prepare a processor processor = VideoBlipProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") tokenizer = LlamaTokenizer.from_pretrained("novelai/nerdstash-tokenizer-v1", additional_special_tokens=['▁▁']) processor.tokenizer = tokenizer import requests from PIL import Image # prepare inputs url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw) text = f"##human: この画像の面白い点は何ですか?\n##gpt: " # do preprocessing inputs = processor( text=text, images=image, return_tensors="pt", truncation=True, ) inputs = {k: v.to(device) for k, v in inputs.items()} inputs["pixel_values"] = inputs["pixel_values"].to(device, torch.float16) # set eos token eos_token_id_list = [ processor.tokenizer.pad_token_id, processor.tokenizer.eos_token_id, int(tokenizer.convert_tokens_to_ids("##")) ] # do inference with torch.no_grad(): out = model.generate(**inputs, max_length=256, do_sample=False, temperature=0., eos_token_id=eos_token_id_list, no_repeat_ngram_size=2) # print result print(processor.tokenizer.batch_decode(out)) ``` ## Model Details * **Developed by**: [Turing Inc.](https://www.turing-motors.com/) * **Adaptor type**: [BLIP2](https://arxiv.org/abs/2301.12597) * **Lamguage Model**: [Japanese StableLM Base Alpha](https://huggingface.co/stabilityai/japanese-stablelm-base-alpha-7b) * **Language(s)**: Japanese ### Training This model was initially trained with the Adaptor using STAIR Captions. In the second phase, it was fine-tuned with [LLaVA-Instruct-150K-JA](https://huggingface.co/datasets/turing-motors/LLaVA-Instruct-150K-JA) and Japanese Visual Genome using LoRA. ### Training Dataset - [LLaVA-Instruct-150K-JA](https://huggingface.co/datasets/turing-motors/LLaVA-Instruct-150K-JA) - [Japanese STAIR Captions](http://captions.stair.center/) - [Japanese Visual Genome VQA dataset](https://github.com/yahoojapan/ja-vg-vqa) ## Use and Limitations ### Intended Use This model is intended for use in chat-like applications and for research purposes. ### Limitations The model may produce inaccurate or false information, and its accuracy is not guaranteed. It is still in the research and development stage. ## How to cite ```bibtex @misc{BlipJapaneseStableLM, url = {[https://huggingface.co/turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0](https://huggingface.co/turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0)}, title = {Heron BLIP Japanese StableLM Base 7B}, author = {Kotaro Tanahashi, Yuichi Inoue, and Yu Yamaguchi} } ``` ## Citations ```bibtex @misc{JapaneseInstructBLIPAlpha, url = {[https://huggingface.co/stabilityai/japanese-instructblip-alpha](https://huggingface.co/stabilityai/japanese-instructblip-alpha)}, title = {Japanese InstructBLIP Alpha}, author = {Shing, Makoto and Akiba, Takuya} } ``` --- license: cc-by-nc-4.0 ---
CyberHarem/miyu_edelfelt_fatekaleidlinerprismaillya
CyberHarem
2023-09-07T16:45:08Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/miyu_edelfelt_fatekaleidlinerprismaillya", "license:mit", "region:us" ]
text-to-image
2023-09-07T16:24:45Z
--- license: mit datasets: - CyberHarem/miyu_edelfelt_fatekaleidlinerprismaillya pipeline_tag: text-to-image tags: - art --- # Lora of miyu_edelfelt_fatekaleidlinerprismaillya 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 6800, you need to download `6800/miyu_edelfelt_fatekaleidlinerprismaillya.pt` as the embedding and `6800/miyu_edelfelt_fatekaleidlinerprismaillya.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 6800**, with the score of 0.709. The trigger words are: 1. `miyu_edelfelt_fatekaleidlinerprismaillya` 2. `black_hair, brown_eyes, hair_ornament, hairclip, bangs, long_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:------------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:-------------------------------------------|:---------------------------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:------------------------------------------------|:-------------------------------------------------|:---------------------------------------|:-------------------------------------------| | 10200 | 0.705 | [Download](10200/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-10200](10200/previews/pattern_1.png) | ![pattern_2-10200](10200/previews/pattern_2.png) | ![pattern_3-10200](10200/previews/pattern_3.png) | ![pattern_4-10200](10200/previews/pattern_4.png) | ![pattern_5-10200](10200/previews/pattern_5.png) | ![pattern_6-10200](10200/previews/pattern_6.png) | ![pattern_7-10200](10200/previews/pattern_7.png) | ![pattern_8-10200](10200/previews/pattern_8.png) | ![pattern_9-10200](10200/previews/pattern_9.png) | ![pattern_10-10200](10200/previews/pattern_10.png) | ![pattern_11-10200](10200/previews/pattern_11.png) | ![pattern_12-10200](10200/previews/pattern_12.png) | ![pattern_13-10200](10200/previews/pattern_13.png) | ![pattern_14-10200](10200/previews/pattern_14.png) | [<NSFW, click to see>](10200/previews/pattern_15.png) | ![pattern_16-10200](10200/previews/pattern_16.png) | ![pattern_17-10200](10200/previews/pattern_17.png) | ![bikini-10200](10200/previews/bikini.png) | [<NSFW, click to see>](10200/previews/bondage.png) | ![free-10200](10200/previews/free.png) | ![maid-10200](10200/previews/maid.png) | ![miko-10200](10200/previews/miko.png) | [<NSFW, click to see>](10200/previews/nude.png) | [<NSFW, click to see>](10200/previews/nude2.png) | ![suit-10200](10200/previews/suit.png) | ![yukata-10200](10200/previews/yukata.png) | | 9520 | 0.687 | [Download](9520/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-9520](9520/previews/pattern_1.png) | ![pattern_2-9520](9520/previews/pattern_2.png) | ![pattern_3-9520](9520/previews/pattern_3.png) | ![pattern_4-9520](9520/previews/pattern_4.png) | ![pattern_5-9520](9520/previews/pattern_5.png) | ![pattern_6-9520](9520/previews/pattern_6.png) | ![pattern_7-9520](9520/previews/pattern_7.png) | ![pattern_8-9520](9520/previews/pattern_8.png) | ![pattern_9-9520](9520/previews/pattern_9.png) | ![pattern_10-9520](9520/previews/pattern_10.png) | ![pattern_11-9520](9520/previews/pattern_11.png) | ![pattern_12-9520](9520/previews/pattern_12.png) | ![pattern_13-9520](9520/previews/pattern_13.png) | ![pattern_14-9520](9520/previews/pattern_14.png) | [<NSFW, click to see>](9520/previews/pattern_15.png) | ![pattern_16-9520](9520/previews/pattern_16.png) | ![pattern_17-9520](9520/previews/pattern_17.png) | ![bikini-9520](9520/previews/bikini.png) | [<NSFW, click to see>](9520/previews/bondage.png) | ![free-9520](9520/previews/free.png) | ![maid-9520](9520/previews/maid.png) | ![miko-9520](9520/previews/miko.png) | [<NSFW, click to see>](9520/previews/nude.png) | [<NSFW, click to see>](9520/previews/nude2.png) | ![suit-9520](9520/previews/suit.png) | ![yukata-9520](9520/previews/yukata.png) | | 8840 | 0.647 | [Download](8840/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-8840](8840/previews/pattern_1.png) | ![pattern_2-8840](8840/previews/pattern_2.png) | ![pattern_3-8840](8840/previews/pattern_3.png) | ![pattern_4-8840](8840/previews/pattern_4.png) | ![pattern_5-8840](8840/previews/pattern_5.png) | ![pattern_6-8840](8840/previews/pattern_6.png) | ![pattern_7-8840](8840/previews/pattern_7.png) | ![pattern_8-8840](8840/previews/pattern_8.png) | ![pattern_9-8840](8840/previews/pattern_9.png) | ![pattern_10-8840](8840/previews/pattern_10.png) | ![pattern_11-8840](8840/previews/pattern_11.png) | ![pattern_12-8840](8840/previews/pattern_12.png) | ![pattern_13-8840](8840/previews/pattern_13.png) | ![pattern_14-8840](8840/previews/pattern_14.png) | [<NSFW, click to see>](8840/previews/pattern_15.png) | ![pattern_16-8840](8840/previews/pattern_16.png) | ![pattern_17-8840](8840/previews/pattern_17.png) | ![bikini-8840](8840/previews/bikini.png) | [<NSFW, click to see>](8840/previews/bondage.png) | ![free-8840](8840/previews/free.png) | ![maid-8840](8840/previews/maid.png) | ![miko-8840](8840/previews/miko.png) | [<NSFW, click to see>](8840/previews/nude.png) | [<NSFW, click to see>](8840/previews/nude2.png) | ![suit-8840](8840/previews/suit.png) | ![yukata-8840](8840/previews/yukata.png) | | 8160 | 0.694 | [Download](8160/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-8160](8160/previews/pattern_1.png) | ![pattern_2-8160](8160/previews/pattern_2.png) | ![pattern_3-8160](8160/previews/pattern_3.png) | ![pattern_4-8160](8160/previews/pattern_4.png) | ![pattern_5-8160](8160/previews/pattern_5.png) | ![pattern_6-8160](8160/previews/pattern_6.png) | ![pattern_7-8160](8160/previews/pattern_7.png) | ![pattern_8-8160](8160/previews/pattern_8.png) | ![pattern_9-8160](8160/previews/pattern_9.png) | ![pattern_10-8160](8160/previews/pattern_10.png) | ![pattern_11-8160](8160/previews/pattern_11.png) | ![pattern_12-8160](8160/previews/pattern_12.png) | ![pattern_13-8160](8160/previews/pattern_13.png) | ![pattern_14-8160](8160/previews/pattern_14.png) | [<NSFW, click to see>](8160/previews/pattern_15.png) | ![pattern_16-8160](8160/previews/pattern_16.png) | ![pattern_17-8160](8160/previews/pattern_17.png) | ![bikini-8160](8160/previews/bikini.png) | [<NSFW, click to see>](8160/previews/bondage.png) | ![free-8160](8160/previews/free.png) | ![maid-8160](8160/previews/maid.png) | ![miko-8160](8160/previews/miko.png) | [<NSFW, click to see>](8160/previews/nude.png) | [<NSFW, click to see>](8160/previews/nude2.png) | ![suit-8160](8160/previews/suit.png) | ![yukata-8160](8160/previews/yukata.png) | | 7480 | 0.708 | [Download](7480/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-7480](7480/previews/pattern_1.png) | ![pattern_2-7480](7480/previews/pattern_2.png) | ![pattern_3-7480](7480/previews/pattern_3.png) | ![pattern_4-7480](7480/previews/pattern_4.png) | ![pattern_5-7480](7480/previews/pattern_5.png) | ![pattern_6-7480](7480/previews/pattern_6.png) | ![pattern_7-7480](7480/previews/pattern_7.png) | ![pattern_8-7480](7480/previews/pattern_8.png) | ![pattern_9-7480](7480/previews/pattern_9.png) | ![pattern_10-7480](7480/previews/pattern_10.png) | ![pattern_11-7480](7480/previews/pattern_11.png) | ![pattern_12-7480](7480/previews/pattern_12.png) | ![pattern_13-7480](7480/previews/pattern_13.png) | ![pattern_14-7480](7480/previews/pattern_14.png) | [<NSFW, click to see>](7480/previews/pattern_15.png) | ![pattern_16-7480](7480/previews/pattern_16.png) | ![pattern_17-7480](7480/previews/pattern_17.png) | ![bikini-7480](7480/previews/bikini.png) | [<NSFW, click to see>](7480/previews/bondage.png) | ![free-7480](7480/previews/free.png) | ![maid-7480](7480/previews/maid.png) | ![miko-7480](7480/previews/miko.png) | [<NSFW, click to see>](7480/previews/nude.png) | [<NSFW, click to see>](7480/previews/nude2.png) | ![suit-7480](7480/previews/suit.png) | ![yukata-7480](7480/previews/yukata.png) | | **6800** | **0.709** | [**Download**](6800/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-6800](6800/previews/pattern_1.png) | ![pattern_2-6800](6800/previews/pattern_2.png) | ![pattern_3-6800](6800/previews/pattern_3.png) | ![pattern_4-6800](6800/previews/pattern_4.png) | ![pattern_5-6800](6800/previews/pattern_5.png) | ![pattern_6-6800](6800/previews/pattern_6.png) | ![pattern_7-6800](6800/previews/pattern_7.png) | ![pattern_8-6800](6800/previews/pattern_8.png) | ![pattern_9-6800](6800/previews/pattern_9.png) | ![pattern_10-6800](6800/previews/pattern_10.png) | ![pattern_11-6800](6800/previews/pattern_11.png) | ![pattern_12-6800](6800/previews/pattern_12.png) | ![pattern_13-6800](6800/previews/pattern_13.png) | ![pattern_14-6800](6800/previews/pattern_14.png) | [<NSFW, click to see>](6800/previews/pattern_15.png) | ![pattern_16-6800](6800/previews/pattern_16.png) | ![pattern_17-6800](6800/previews/pattern_17.png) | ![bikini-6800](6800/previews/bikini.png) | [<NSFW, click to see>](6800/previews/bondage.png) | ![free-6800](6800/previews/free.png) | ![maid-6800](6800/previews/maid.png) | ![miko-6800](6800/previews/miko.png) | [<NSFW, click to see>](6800/previews/nude.png) | [<NSFW, click to see>](6800/previews/nude2.png) | ![suit-6800](6800/previews/suit.png) | ![yukata-6800](6800/previews/yukata.png) | | 6120 | 0.685 | [Download](6120/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-6120](6120/previews/pattern_1.png) | ![pattern_2-6120](6120/previews/pattern_2.png) | ![pattern_3-6120](6120/previews/pattern_3.png) | ![pattern_4-6120](6120/previews/pattern_4.png) | ![pattern_5-6120](6120/previews/pattern_5.png) | ![pattern_6-6120](6120/previews/pattern_6.png) | ![pattern_7-6120](6120/previews/pattern_7.png) | ![pattern_8-6120](6120/previews/pattern_8.png) | ![pattern_9-6120](6120/previews/pattern_9.png) | ![pattern_10-6120](6120/previews/pattern_10.png) | ![pattern_11-6120](6120/previews/pattern_11.png) | ![pattern_12-6120](6120/previews/pattern_12.png) | ![pattern_13-6120](6120/previews/pattern_13.png) | ![pattern_14-6120](6120/previews/pattern_14.png) | [<NSFW, click to see>](6120/previews/pattern_15.png) | ![pattern_16-6120](6120/previews/pattern_16.png) | ![pattern_17-6120](6120/previews/pattern_17.png) | ![bikini-6120](6120/previews/bikini.png) | [<NSFW, click to see>](6120/previews/bondage.png) | ![free-6120](6120/previews/free.png) | ![maid-6120](6120/previews/maid.png) | ![miko-6120](6120/previews/miko.png) | [<NSFW, click to see>](6120/previews/nude.png) | [<NSFW, click to see>](6120/previews/nude2.png) | ![suit-6120](6120/previews/suit.png) | ![yukata-6120](6120/previews/yukata.png) | | 5440 | 0.680 | [Download](5440/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-5440](5440/previews/pattern_1.png) | ![pattern_2-5440](5440/previews/pattern_2.png) | ![pattern_3-5440](5440/previews/pattern_3.png) | ![pattern_4-5440](5440/previews/pattern_4.png) | ![pattern_5-5440](5440/previews/pattern_5.png) | ![pattern_6-5440](5440/previews/pattern_6.png) | ![pattern_7-5440](5440/previews/pattern_7.png) | ![pattern_8-5440](5440/previews/pattern_8.png) | ![pattern_9-5440](5440/previews/pattern_9.png) | ![pattern_10-5440](5440/previews/pattern_10.png) | ![pattern_11-5440](5440/previews/pattern_11.png) | ![pattern_12-5440](5440/previews/pattern_12.png) | ![pattern_13-5440](5440/previews/pattern_13.png) | ![pattern_14-5440](5440/previews/pattern_14.png) | [<NSFW, click to see>](5440/previews/pattern_15.png) | ![pattern_16-5440](5440/previews/pattern_16.png) | ![pattern_17-5440](5440/previews/pattern_17.png) | ![bikini-5440](5440/previews/bikini.png) | [<NSFW, click to see>](5440/previews/bondage.png) | ![free-5440](5440/previews/free.png) | ![maid-5440](5440/previews/maid.png) | ![miko-5440](5440/previews/miko.png) | [<NSFW, click to see>](5440/previews/nude.png) | [<NSFW, click to see>](5440/previews/nude2.png) | ![suit-5440](5440/previews/suit.png) | ![yukata-5440](5440/previews/yukata.png) | | 4760 | 0.649 | [Download](4760/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![pattern_8-4760](4760/previews/pattern_8.png) | ![pattern_9-4760](4760/previews/pattern_9.png) | ![pattern_10-4760](4760/previews/pattern_10.png) | ![pattern_11-4760](4760/previews/pattern_11.png) | ![pattern_12-4760](4760/previews/pattern_12.png) | ![pattern_13-4760](4760/previews/pattern_13.png) | ![pattern_14-4760](4760/previews/pattern_14.png) | [<NSFW, click to see>](4760/previews/pattern_15.png) | ![pattern_16-4760](4760/previews/pattern_16.png) | ![pattern_17-4760](4760/previews/pattern_17.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) | | 4080 | 0.646 | [Download](4080/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![pattern_8-4080](4080/previews/pattern_8.png) | ![pattern_9-4080](4080/previews/pattern_9.png) | ![pattern_10-4080](4080/previews/pattern_10.png) | ![pattern_11-4080](4080/previews/pattern_11.png) | ![pattern_12-4080](4080/previews/pattern_12.png) | ![pattern_13-4080](4080/previews/pattern_13.png) | ![pattern_14-4080](4080/previews/pattern_14.png) | [<NSFW, click to see>](4080/previews/pattern_15.png) | ![pattern_16-4080](4080/previews/pattern_16.png) | ![pattern_17-4080](4080/previews/pattern_17.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) | | 3400 | 0.620 | [Download](3400/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![pattern_8-3400](3400/previews/pattern_8.png) | ![pattern_9-3400](3400/previews/pattern_9.png) | ![pattern_10-3400](3400/previews/pattern_10.png) | ![pattern_11-3400](3400/previews/pattern_11.png) | ![pattern_12-3400](3400/previews/pattern_12.png) | ![pattern_13-3400](3400/previews/pattern_13.png) | ![pattern_14-3400](3400/previews/pattern_14.png) | [<NSFW, click to see>](3400/previews/pattern_15.png) | ![pattern_16-3400](3400/previews/pattern_16.png) | ![pattern_17-3400](3400/previews/pattern_17.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) | | 2720 | 0.622 | [Download](2720/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![pattern_8-2720](2720/previews/pattern_8.png) | ![pattern_9-2720](2720/previews/pattern_9.png) | ![pattern_10-2720](2720/previews/pattern_10.png) | ![pattern_11-2720](2720/previews/pattern_11.png) | ![pattern_12-2720](2720/previews/pattern_12.png) | ![pattern_13-2720](2720/previews/pattern_13.png) | ![pattern_14-2720](2720/previews/pattern_14.png) | [<NSFW, click to see>](2720/previews/pattern_15.png) | ![pattern_16-2720](2720/previews/pattern_16.png) | ![pattern_17-2720](2720/previews/pattern_17.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) | | 2040 | 0.447 | [Download](2040/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![pattern_8-2040](2040/previews/pattern_8.png) | ![pattern_9-2040](2040/previews/pattern_9.png) | ![pattern_10-2040](2040/previews/pattern_10.png) | ![pattern_11-2040](2040/previews/pattern_11.png) | ![pattern_12-2040](2040/previews/pattern_12.png) | ![pattern_13-2040](2040/previews/pattern_13.png) | ![pattern_14-2040](2040/previews/pattern_14.png) | [<NSFW, click to see>](2040/previews/pattern_15.png) | ![pattern_16-2040](2040/previews/pattern_16.png) | ![pattern_17-2040](2040/previews/pattern_17.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) | | 1360 | 0.386 | [Download](1360/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![pattern_8-1360](1360/previews/pattern_8.png) | ![pattern_9-1360](1360/previews/pattern_9.png) | ![pattern_10-1360](1360/previews/pattern_10.png) | ![pattern_11-1360](1360/previews/pattern_11.png) | ![pattern_12-1360](1360/previews/pattern_12.png) | ![pattern_13-1360](1360/previews/pattern_13.png) | ![pattern_14-1360](1360/previews/pattern_14.png) | [<NSFW, click to see>](1360/previews/pattern_15.png) | ![pattern_16-1360](1360/previews/pattern_16.png) | ![pattern_17-1360](1360/previews/pattern_17.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) | | 680 | 0.254 | [Download](680/miyu_edelfelt_fatekaleidlinerprismaillya.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![pattern_8-680](680/previews/pattern_8.png) | ![pattern_9-680](680/previews/pattern_9.png) | ![pattern_10-680](680/previews/pattern_10.png) | ![pattern_11-680](680/previews/pattern_11.png) | ![pattern_12-680](680/previews/pattern_12.png) | ![pattern_13-680](680/previews/pattern_13.png) | ![pattern_14-680](680/previews/pattern_14.png) | [<NSFW, click to see>](680/previews/pattern_15.png) | ![pattern_16-680](680/previews/pattern_16.png) | ![pattern_17-680](680/previews/pattern_17.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) |
nagupv/stablebeluga7b_ckpoint1500
nagupv
2023-09-07T16:43:53Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-07T16:43:35Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
Onutoa/1_7e-3_10_0.1
Onutoa
2023-09-07T16:41:06Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-07T13:40:46Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: 1_7e-3_10_0.1 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. --> # 1_7e-3_10_0.1 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9819 - Accuracy: 0.7303 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.007 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.4686 | 1.0 | 590 | 2.1510 | 0.3798 | | 1.4409 | 2.0 | 1180 | 1.6620 | 0.6214 | | 1.3336 | 3.0 | 1770 | 2.9692 | 0.3789 | | 1.3331 | 4.0 | 2360 | 0.9502 | 0.6306 | | 1.1121 | 5.0 | 2950 | 1.0075 | 0.6294 | | 1.1211 | 6.0 | 3540 | 0.8872 | 0.6612 | | 1.0596 | 7.0 | 4130 | 2.2995 | 0.4128 | | 0.9931 | 8.0 | 4720 | 0.9438 | 0.6810 | | 0.9235 | 9.0 | 5310 | 0.8872 | 0.6581 | | 0.9613 | 10.0 | 5900 | 1.2425 | 0.5847 | | 0.9177 | 11.0 | 6490 | 0.8943 | 0.6862 | | 0.7985 | 12.0 | 7080 | 0.8038 | 0.6884 | | 0.7943 | 13.0 | 7670 | 0.8016 | 0.6924 | | 0.7742 | 14.0 | 8260 | 0.7611 | 0.7162 | | 0.7373 | 15.0 | 8850 | 0.8728 | 0.7128 | | 0.7054 | 16.0 | 9440 | 0.7415 | 0.7116 | | 0.6589 | 17.0 | 10030 | 0.7437 | 0.7070 | | 0.6449 | 18.0 | 10620 | 1.1703 | 0.6303 | | 0.5872 | 19.0 | 11210 | 0.7583 | 0.7217 | | 0.6065 | 20.0 | 11800 | 0.8280 | 0.7196 | | 0.5721 | 21.0 | 12390 | 0.8555 | 0.7012 | | 0.5955 | 22.0 | 12980 | 0.8109 | 0.7147 | | 0.5202 | 23.0 | 13570 | 0.7935 | 0.7245 | | 0.5017 | 24.0 | 14160 | 0.8676 | 0.6976 | | 0.4923 | 25.0 | 14750 | 0.9052 | 0.7346 | | 0.4774 | 26.0 | 15340 | 1.5937 | 0.5976 | | 0.4714 | 27.0 | 15930 | 0.8523 | 0.7220 | | 0.4439 | 28.0 | 16520 | 0.8909 | 0.7278 | | 0.4227 | 29.0 | 17110 | 0.9224 | 0.7321 | | 0.4029 | 30.0 | 17700 | 0.8559 | 0.7245 | | 0.4015 | 31.0 | 18290 | 0.9032 | 0.7309 | | 0.3923 | 32.0 | 18880 | 0.9003 | 0.7327 | | 0.3897 | 33.0 | 19470 | 0.9786 | 0.6966 | | 0.354 | 34.0 | 20060 | 0.8606 | 0.7251 | | 0.3508 | 35.0 | 20650 | 0.8788 | 0.7278 | | 0.3293 | 36.0 | 21240 | 1.1236 | 0.7214 | | 0.3336 | 37.0 | 21830 | 0.9196 | 0.7266 | | 0.3407 | 38.0 | 22420 | 0.9319 | 0.7220 | | 0.3338 | 39.0 | 23010 | 0.8982 | 0.7321 | | 0.3065 | 40.0 | 23600 | 0.9969 | 0.7333 | | 0.2972 | 41.0 | 24190 | 1.0879 | 0.7309 | | 0.2904 | 42.0 | 24780 | 0.9547 | 0.7327 | | 0.2883 | 43.0 | 25370 | 0.9553 | 0.7187 | | 0.2889 | 44.0 | 25960 | 0.9805 | 0.7251 | | 0.269 | 45.0 | 26550 | 0.9516 | 0.7321 | | 0.2573 | 46.0 | 27140 | 0.9094 | 0.7242 | | 0.2679 | 47.0 | 27730 | 0.9398 | 0.7217 | | 0.2595 | 48.0 | 28320 | 1.0380 | 0.7064 | | 0.2819 | 49.0 | 28910 | 0.9346 | 0.7324 | | 0.247 | 50.0 | 29500 | 0.9272 | 0.7239 | | 0.2482 | 51.0 | 30090 | 0.9673 | 0.7254 | | 0.242 | 52.0 | 30680 | 1.0115 | 0.7217 | | 0.2343 | 53.0 | 31270 | 0.9958 | 0.7226 | | 0.2381 | 54.0 | 31860 | 0.9392 | 0.7263 | | 0.2279 | 55.0 | 32450 | 0.9564 | 0.7284 | | 0.2256 | 56.0 | 33040 | 1.0298 | 0.7239 | | 0.2267 | 57.0 | 33630 | 1.0001 | 0.7263 | | 0.2161 | 58.0 | 34220 | 0.9867 | 0.7248 | | 0.214 | 59.0 | 34810 | 0.9574 | 0.7226 | | 0.2148 | 60.0 | 35400 | 1.0306 | 0.7229 | | 0.2128 | 61.0 | 35990 | 1.0751 | 0.7346 | | 0.2081 | 62.0 | 36580 | 0.9656 | 0.7263 | | 0.203 | 63.0 | 37170 | 1.0100 | 0.7263 | | 0.204 | 64.0 | 37760 | 0.9536 | 0.7297 | | 0.1988 | 65.0 | 38350 | 0.9686 | 0.7269 | | 0.1976 | 66.0 | 38940 | 0.9927 | 0.7297 | | 0.1943 | 67.0 | 39530 | 0.9987 | 0.7309 | | 0.1941 | 68.0 | 40120 | 0.9876 | 0.7309 | | 0.1862 | 69.0 | 40710 | 0.9646 | 0.7321 | | 0.1986 | 70.0 | 41300 | 1.0332 | 0.7324 | | 0.1872 | 71.0 | 41890 | 0.9861 | 0.7324 | | 0.1898 | 72.0 | 42480 | 0.9831 | 0.7346 | | 0.1793 | 73.0 | 43070 | 0.9901 | 0.7303 | | 0.1843 | 74.0 | 43660 | 1.0411 | 0.7294 | | 0.1757 | 75.0 | 44250 | 1.0355 | 0.7312 | | 0.1814 | 76.0 | 44840 | 1.0320 | 0.7239 | | 0.1764 | 77.0 | 45430 | 0.9895 | 0.7333 | | 0.1779 | 78.0 | 46020 | 0.9944 | 0.7367 | | 0.1752 | 79.0 | 46610 | 0.9581 | 0.7263 | | 0.1734 | 80.0 | 47200 | 0.9525 | 0.7297 | | 0.1718 | 81.0 | 47790 | 0.9693 | 0.7275 | | 0.1722 | 82.0 | 48380 | 0.9876 | 0.7297 | | 0.1719 | 83.0 | 48970 | 0.9838 | 0.7306 | | 0.161 | 84.0 | 49560 | 0.9996 | 0.7281 | | 0.1711 | 85.0 | 50150 | 0.9880 | 0.7291 | | 0.1634 | 86.0 | 50740 | 1.0062 | 0.7306 | | 0.1587 | 87.0 | 51330 | 1.0071 | 0.7318 | | 0.156 | 88.0 | 51920 | 1.0271 | 0.7297 | | 0.1574 | 89.0 | 52510 | 1.0062 | 0.7321 | | 0.151 | 90.0 | 53100 | 0.9889 | 0.7263 | | 0.1553 | 91.0 | 53690 | 0.9676 | 0.7324 | | 0.1584 | 92.0 | 54280 | 0.9721 | 0.7321 | | 0.1491 | 93.0 | 54870 | 0.9824 | 0.7349 | | 0.1523 | 94.0 | 55460 | 0.9880 | 0.7306 | | 0.1509 | 95.0 | 56050 | 0.9993 | 0.7327 | | 0.1496 | 96.0 | 56640 | 0.9892 | 0.7318 | | 0.1518 | 97.0 | 57230 | 0.9925 | 0.7339 | | 0.149 | 98.0 | 57820 | 0.9845 | 0.7333 | | 0.1449 | 99.0 | 58410 | 0.9832 | 0.7312 | | 0.15 | 100.0 | 59000 | 0.9819 | 0.7303 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
mariogiordano/finetuning-emotion-model
mariogiordano
2023-09-07T16:40:03Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-02T16:36:55Z
--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-emotion-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-emotion-model This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1597 - Accuracy: 0.6197 - F1: 0.6118 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 100 | 1.5910 | 0.4681 | 0.4490 | | No log | 2.0 | 200 | 1.1597 | 0.6197 | 0.6118 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
zlsl/ruGPT-3.5-13B-erotic-kink-chat-lora
zlsl
2023-09-07T16:25:58Z
26
8
adapter-transformers
[ "adapter-transformers", "safetensors", "rugpt", "chat", "lora", "erotic", "porn", "text-generation", "ru", "license:cc-by-nc-nd-4.0", "region:us" ]
text-generation
2023-08-24T12:12:46Z
--- license: cc-by-nc-nd-4.0 language: - ru library_name: adapter-transformers pipeline_tag: text-generation tags: - rugpt - chat - lora - erotic - porn --- LoRA (rank 16, alpha 16) улучшает диалоги на кхм, пикантные темы для ruGPT-3.5-13B. Обучается на 4-bit GPTQ модели ruGPT-3.5-13B, как будет работать на полной и 8-битной модели не проверял, на 4-х битах результат очень хороший. LoRA будет регулярно обновляться. Датасет - input-output с контекстом, на данный момент ~1Гб В стоп-лист добавляйте "\n", "<\/s>"