 We want a technology to be developed in the field of autonomous vehicles. Provide an example to estimate the depth of the environment using monocular images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Depth Estimation\', \'api_name\': \'glpn-kitti\', \'api_call\': "GLPNForDepthEstimation.from_pretrained(\'vinvino02/glpn-kitti\')", \'api_arguments\': \'images, return_tensors\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation\\nimport torch\\nimport numpy as np\\nfrom PIL import Image\\nimport requests\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = GLPNFeatureExtractor.from_pretrained(vinvino02/glpn-kitti)\\nmodel = GLPNForDepthEstimation.from_pretrained(vinvino02/glpn-kitti)\\ninputs = feature_extractor(images=image, return_tensors=pt)\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n predicted_depth = outputs.predicted_depth\\nprediction = torch.nn.functional.interpolate(\\n predicted_depth.unsqueeze(1),\\n size=image.size[::-1],\\n mode=bicubic,\\n align_corners=False,\\n)\\noutput = prediction.squeeze().cpu().numpy()\\nformatted = (output * 255 / np.max(output)).astype(uint8)\\ndepth = Image.fromarray(formatted)\', \'performance\': {\'dataset\': \'KITTI\', \'accuracy\': \'Not provided\'}, \'description\': \'Global-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository.\'}', metadata={})]


 As a construction company, we want to have a system to estimate the depth of images taken from different construction sites.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Depth Estimation\', \'api_name\': \'glpn-kitti\', \'api_call\': "GLPNForDepthEstimation.from_pretrained(\'vinvino02/glpn-kitti\')", \'api_arguments\': \'images, return_tensors\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation\\nimport torch\\nimport numpy as np\\nfrom PIL import Image\\nimport requests\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = GLPNFeatureExtractor.from_pretrained(vinvino02/glpn-kitti)\\nmodel = GLPNForDepthEstimation.from_pretrained(vinvino02/glpn-kitti)\\ninputs = feature_extractor(images=image, return_tensors=pt)\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n predicted_depth = outputs.predicted_depth\\nprediction = torch.nn.functional.interpolate(\\n predicted_depth.unsqueeze(1),\\n size=image.size[::-1],\\n mode=bicubic,\\n align_corners=False,\\n)\\noutput = prediction.squeeze().cpu().numpy()\\nformatted = (output * 255 / np.max(output)).astype(uint8)\\ndepth = Image.fromarray(formatted)\', \'performance\': {\'dataset\': \'KITTI\', \'accuracy\': \'Not provided\'}, \'description\': \'Global-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository.\'}', metadata={})]


 Our team has built a Japanese language learning app for students. We want to make sure that the user-submitted image is an anime art created by humans and not AI-generated.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'kha-white/manga-ocr-base\', \'api_call\': "pipeline(\'ocr\', model=\'kha-white/manga-ocr-base\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'manga109s\', \'accuracy\': \'\'}, \'description\': \'Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses Vision Encoder Decoder framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality text recognition, robust against various scenarios specific to manga: both vertical and horizontal text, text with furigana, text overlaid on images, wide variety of fonts and font styles, and low quality images.\'}', metadata={})]


 We need to classify the type of an image for an inventory.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 Imagine we run an e-commerce platform and we need to tag new products with relevant categories based on their product images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 We are building an AI-based camera system to ensure safety on a construction site. Detect workers wearing hard hats in a given image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8m-hard-hat-detection\', \'api_call\': "YOLO(\'keremberke/yolov8m-hard-hat-detection\')", \'api_arguments\': {\'image\': \'URL or local path to the image\'}, \'python_environment_requirements\': [\'ultralyticsplus==0.0.24\', \'ultralytics==8.0.23\'], \'example_code\': "from ultralyticsplus import YOLO, render_result\\n\\nmodel = YOLO(\'keremberke/yolov8m-hard-hat-detection\')\\n\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\n\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\n\\nresults = model.predict(image)\\n\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'hard-hat-detection\', \'accuracy\': 0.811}, \'description\': "A YOLOv8 model for detecting hard hats in images. The model can distinguish between \'Hardhat\' and \'NO-Hardhat\' classes. It can be used to ensure safety compliance in construction sites or other industrial environments where hard hats are required."}', metadata={})]


 My client needs help with security. They have a surveillance camera set up and we need to identify if any unknown person enters their property.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8s-csgo-player-detection\', \'api_call\': "YOLO(\'keremberke/yolov8s-csgo-player-detection\')", \'api_arguments\': {\'image\': \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'}, \'python_environment_requirements\': [\'ultralyticsplus==0.0.23\', \'ultralytics==8.0.21\'], \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8s-csgo-player-detection\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'csgo-object-detection\', \'accuracy\': 0.886}, \'description\': "A YOLOv8 model for detecting Counter-Strike: Global Offensive (CS:GO) players. Supports the labels [\'ct\', \'cthead\', \'t\', \'thead\']."}', metadata={})]


 We need a system to control the access into parking lot. Analyze an image and find out the license plate numbers to detect whether it's an authorized vehicle or not.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'License Plate Detection\', \'api_name\': \'keremberke/yolov5s-license-plate\', \'api_call\': "yolov5.load(\'keremberke/yolov5s-license-plate\')", \'api_arguments\': {\'img\': \'image url or path\', \'size\': \'image resize dimensions\', \'augment\': \'optional, test time augmentation\'}, \'python_environment_requirements\': \'pip install -U yolov5\', \'example_code\': [\'import yolov5\', "model = yolov5.load(\'keremberke/yolov5s-license-plate\')", \'model.conf = 0.25\', \'model.iou = 0.45\', \'model.agnostic = False\', \'model.multi_label = False\', \'model.max_det = 1000\', "img = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'", \'results = model(img, size=640)\', \'results = model(img, augment=True)\', \'predictions = results.pred[0]\', \'boxes = predictions[:, :4]\', \'scores = predictions[:, 4]\', \'categories = predictions[:, 5]\', \'results.show()\', "results.save(save_dir=\'results/\')"], \'performance\': {\'dataset\': \'keremberke/license-plate-object-detection\', \'accuracy\': 0.985}, \'description\': \'A YOLOv5 based license plate detection model trained on a custom dataset.\'}', metadata={})]


 Analyze an image of an urban scene to identify and separate regions with different semantics, such as streets, pedestrians, buildings, and vehicles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Segmentation\', \'api_name\': \'keremberke/yolov8n-pothole-segmentation\', \'api_call\': "YOLO(\'keremberke/yolov8n-pothole-segmentation\')", \'api_arguments\': {\'image\': \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\', \'conf\': 0.25, \'iou\': 0.45, \'agnostic_nms\': False, \'max_det\': 1000}, \'python_environment_requirements\': {\'ultralyticsplus\': \'0.0.23\', \'ultralytics\': \'8.0.21\'}, \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8n-pothole-segmentation\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nprint(results[0].masks)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'pothole-segmentation\', \'accuracy\': {\'mAP@0.5(box)\': 0.995, \'mAP@0.5(mask)\': 0.995}}, \'description\': \'A YOLOv8 model for pothole segmentation in images. The model is trained on the pothole-segmentation dataset and achieves high accuracy in detecting potholes.\'}', metadata={})]


 We are developing an application to help customers visualize themselves wearing clothes available on our e-commerce website. We need identification of clothing items in the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'patrickjohncyh/fashion-clip\', \'api_call\': "CLIPModel.from_pretrained(\'patrickjohncyh/fashion-clip\')", \'api_arguments\': {\'image\': \'File\', \'class_names\': \'String (comma-separated)\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import CLIPProcessor, CLIPModel; model = CLIPModel.from_pretrained(\'patrickjohncyh/fashion-clip\'); processor = CLIPProcessor.from_pretrained(\'patrickjohncyh/fashion-clip\'); inputs = processor(text=\'blue shoes\', images=image, return_tensors=\'pt\', padding=True); logits_per_image = model(**inputs).logits_per_image; probs = logits_per_image.softmax(dim=-1).tolist()[0]", \'performance\': {\'dataset\': [{\'name\': \'FMNIST\', \'accuracy\': 0.83}, {\'name\': \'KAGL\', \'accuracy\': 0.73}, {\'name\': \'DEEP\', \'accuracy\': 0.62}]}, \'description\': \'FashionCLIP is a CLIP-based model developed to produce general product representations for fashion concepts. Leveraging the pre-trained checkpoint (ViT-B/32) released by OpenAI, it is trained on a large, high-quality novel fashion dataset to study whether domain specific fine-tuning of CLIP-like models is sufficient to produce product representations that are zero-shot transferable to entirely new datasets and tasks.\'}', metadata={})]


 We need an image segmentation solution for our smart city planning project. The solution should be able to handle different types of segmentation tasks, from semantic to instance to panoptic.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Semantic Segmentation\', \'api_name\': \'nvidia/segformer-b2-finetuned-cityscapes-1024-1024\', \'api_call\': "SegformerForSemanticSegmentation.from_pretrained(\'nvidia/segformer-b2-finetuned-cityscapes-1024-1024\')", \'api_arguments\': {\'images\': \'image\', \'return_tensors\': \'pt\'}, \'python_environment_requirements\': {\'transformers\': \'latest\', \'PIL\': \'latest\', \'requests\': \'latest\'}, \'example_code\': "from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nfeature_extractor = SegformerFeatureExtractor.from_pretrained(\'nvidia/segformer-b2-finetuned-cityscapes-1024-1024\')\\nmodel = SegformerForSemanticSegmentation.from_pretrained(\'nvidia/segformer-b2-finetuned-cityscapes-1024-1024\')\\nurl = \'http://images.cocodataset.org/val2017/000000039769.jpg\'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = feature_extractor(images=image, return_tensors=\'pt\')\\noutputs = model(**inputs)\\nlogits = outputs.logits", \'performance\': {\'dataset\': \'Cityscapes\', \'accuracy\': \'Not provided\'}, \'description\': \'SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.\'}', metadata={})]


 We have a road safety app that helps identify potholes. Can we use an image segmentation model to detect if there are potholes in the pictures of roads?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Segmentation\', \'api_name\': \'keremberke/yolov8s-pothole-segmentation\', \'api_call\': "YOLO(\'keremberke/yolov8s-pothole-segmentation\')", \'api_arguments\': {\'image\': \'URL or local path to the image\'}, \'python_environment_requirements\': {\'ultralyticsplus\': \'0.0.23\', \'ultralytics\': \'8.0.21\'}, \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8s-pothole-segmentation\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nprint(results[0].masks)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'pothole-segmentation\', \'accuracy\': {\'mAP@0.5(box)\': 0.928, \'mAP@0.5(mask)\': 0.928}}, \'description\': \'A YOLOv8 model for pothole segmentation. This model detects potholes in images and outputs bounding boxes and masks for the detected potholes.\'}', metadata={})]


 We are making a mobile app related to fitness. We need to estimate the human pose from an image of a user performing an exercise.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Human Pose Estimation\', \'api_name\': \'lllyasviel/sd-controlnet-openpose\', \'api_call\': "ControlNetModel.from_pretrained(\'lllyasviel/sd-controlnet-openpose\')", \'api_arguments\': {\'text\': \'chef in the kitchen\', \'image\': \'image\', \'num_inference_steps\': 20}, \'python_environment_requirements\': {\'diffusers\': \'pip install diffusers\', \'transformers\': \'pip install transformers\', \'accelerate\': \'pip install accelerate\', \'controlnet_aux\': \'pip install controlnet_aux\'}, \'example_code\': "from PIL import Image\\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\\nimport torch\\nfrom controlnet_aux import OpenposeDetector\\nfrom diffusers.utils import load_image\\nopenpose = OpenposeDetector.from_pretrained(\'lllyasviel/ControlNet\')\\nimage = load_image(https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png)\\nimage = openpose(image)\\ncontrolnet = ControlNetModel.from_pretrained(\\n lllyasviel/sd-controlnet-openpose, torch_dtype=torch.float16\\n)\\npipe = StableDiffusionControlNetPipeline.from_pretrained(\\n runwayml/stable-diffusion-v1-5, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\\n)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_xformers_memory_efficient_attention()\\npipe.enable_model_cpu_offload()\\nimage = pipe(chef in the kitchen, image, num_inference_steps=20).images[0]\\nimage.save(\'images/chef_pose_out.png\')", \'performance\': {\'dataset\': \'200k pose-image, caption pairs\', \'accuracy\': \'Not specified\'}, \'description\': \'ControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on Human Pose Estimation. It can be used in combination with Stable Diffusion.\'}', metadata={})]


 I wish to edit my images by detecting straight lines and controlling the diffusion models in the image's diffusion process.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image Diffusion Models\', \'api_name\': \'lllyasviel/control_v11p_sd15s2_lineart_anime\', \'api_call\': "ControlNetModel.from_pretrained(\'lllyasviel/control_v11p_sd15s2_lineart_anime\')", \'api_arguments\': {\'checkpoint\': \'lllyasviel/control_v11p_sd15s2_lineart_anime\', \'torch_dtype\': \'torch.float16\'}, \'python_environment_requirements\': [\'pip install diffusers transformers accelerate\', \'pip install controlnet_aux==0.3.0\'], \'example_code\': [\'import torch\', \'import os\', \'from huggingface_hub import HfApi\', \'from pathlib import Path\', \'from diffusers.utils import load_image\', \'from PIL import Image\', \'import numpy as np\', \'from controlnet_aux import LineartAnimeDetector\', \'from transformers import CLIPTextModel\', \'from diffusers import (\', \' ControlNetModel,\', \' StableDiffusionControlNetPipeline,\', \' UniPCMultistepScheduler,\', \')\', \'checkpoint = lllyasviel/control_v11p_sd15s2_lineart_anime\', \'image = load_image(\', \' https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/input.png\', \')\', \'image = image.resize((512, 512))\', \'prompt = A warrior girl in the jungle\', \'processor = LineartAnimeDetector.from_pretrained(lllyasviel/Annotators)\', \'control_image = processor(image)\', \'control_image.save(./images/control.png)\', \'text_encoder = CLIPTextModel.from_pretrained(runwayml/stable-diffusion-v1-5, subfolder=text_encoder, num_hidden_layers=11, torch_dtype=torch.float16)\', \'controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)\', \'pipe = StableDiffusionControlNetPipeline.from_pretrained(\', \' runwayml/stable-diffusion-v1-5, text_encoder=text_encoder, controlnet=controlnet, torch_dtype=torch.float16\', \')\', \'pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\', \'pipe.enable_model_cpu_offload()\', \'generator = torch.manual_seed(0)\', \'image = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0]\', "image.save(\'images/image_out.png\')"], \'performance\': {\'dataset\': \'Not specified\', \'accuracy\': \'Not specified\'}, \'description\': \'ControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on lineart_anime images.\'}', metadata={})]


 A photographer wants to create artistic interpretations of some of her pictures. Let's help her to get variated styles of her image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'vintedois-diffusion-v0-1\', \'api_call\': "pipeline(\'text-to-image\', model=\'22h/vintedois-diffusion-v0-1\')", \'api_arguments\': [\'prompt\', \'CFG Scale\', \'Scheduler\', \'Steps\', \'Seed\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "text2img(\'photo of an old man in a jungle, looking at the camera\', CFG Scale=7.5, Scheduler=\'diffusers.EulerAncestralDiscreteScheduler\', Steps=30, Seed=44)", \'performance\': {\'dataset\': \'large amount of high quality images\', \'accuracy\': \'not specified\'}, \'description\': \'Vintedois (22h) Diffusion model trained by Predogl and piEsposito with open weights, configs and prompts. This model generates beautiful images without a lot of prompt engineering. It can also generate high fidelity faces with a little amount of steps.\'}', metadata={})]


 We have a client that manages a car website for selling used cars and they need new images of cars for the website.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Transformers\', \'functionality\': \'Image Captioning\', \'api_name\': \'blip-image-captioning-large\', \'api_call\': \'BlipForConditionalGeneration.from_pretrained(Salesforce/blip-image-captioning-large)\', \'api_arguments\': {\'raw_image\': \'Image\', \'text\': \'Optional Text\'}, \'python_environment_requirements\': {\'transformers\': \'BlipProcessor, BlipForConditionalGeneration\', \'PIL\': \'Image\', \'requests\': \'requests\'}, \'example_code\': {\'import_requests\': \'import requests\', \'import_PIL\': \'from PIL import Image\', \'import_transformers\': \'from transformers import BlipProcessor, BlipForConditionalGeneration\', \'load_processor\': \'processor = BlipProcessor.from_pretrained(Salesforce/blip-image-captioning-large)\', \'load_model\': \'model = BlipForConditionalGeneration.from_pretrained(Salesforce/blip-image-captioning-large)\', \'load_image\': "img_url = \'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg\'\\nraw_image = Image.open(requests.get(img_url, stream=True).raw).convert(\'RGB\')", \'conditional_captioning\': \'text = a photography of\\ninputs = processor(raw_image, text, return_tensors=pt)\\nout = model.generate(**inputs)\\nprint(processor.decode(out[0], skip_special_tokens=True))\', \'unconditional_captioning\': \'inputs = processor(raw_image, return_tensors=pt)\\nout = model.generate(**inputs)\\nprint(processor.decode(out[0], skip_special_tokens=True))\'}, \'performance\': {\'dataset\': \'COCO\', \'accuracy\': {\'image-text retrieval\': \'+2.7% recall@1\', \'image captioning\': \'+2.8% CIDEr\', \'VQA\': \'+1.6% VQA score\'}}, \'description\': \'BLIP is a Vision-Language Pre-training (VLP) framework that achieves state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval, image captioning, and VQA. It effectively utilizes noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones.\'}', metadata={})]


 I am interested in building a wall of generated pictures for my gallery. My specifications include a size of 256x256 pixels.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Unconditional Image Generation\', \'api_name\': \'johnowhitaker/sd-class-wikiart-from-bedrooms\', \'api_call\': "DDPMPipeline.from_pretrained(\'johnowhitaker/sd-class-wikiart-from-bedrooms\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'diffusers\', \'example_code\': "from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained(\'johnowhitaker/sd-class-wikiart-from-bedrooms\')\\nimage = pipeline().images[0]\\nimage", \'performance\': {\'dataset\': \'https://huggingface.co/datasets/huggan/wikiart\', \'accuracy\': \'Not provided\'}, \'description\': \'This model is a diffusion model initialized from https://huggingface.co/google/ddpm-bedroom-256 and trained for 5000 steps on https://huggingface.co/datasets/huggan/wikiart.\'}', metadata={})]


 We are a product company selling personalized gadgets, and we want to build a recommender system that shows generated, high-resolution images of human faces on our website.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'prompthero/openjourney-v4\', \'api_call\': "pipeline(\'text-to-image\', model=\'prompthero/openjourney-v4\')", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "generate_image(\'your text here\')", \'performance\': {\'dataset\': \'Midjourney v4 images\', \'accuracy\': \'Not provided\'}, \'description\': \'Openjourney v4 is trained on +124k Midjourney v4 images by PromptHero. It is used for generating images based on text inputs.\'}', metadata={})]


 I want to build an AI-based software that can identify the activities or actions in a video clip.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'Hugging Face\', \'functionality\': \'Voice Activity Detection\', \'api_name\': \'d4data/Indian-voice-cloning\', \'api_call\': "pipeline(\'voice-activity-detection\', model=\'d4data/Indian-voice-cloning\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A model for detecting voice activity in Indian languages.\'}', metadata={})]


 The marketing team wants a tool to quickly classify new advertisement videos.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 A sports league wants to analyze their videos and extract information on game highlights.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'tuner007/pegasus_summarizer\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'tuner007/pegasus_summarizer\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'pip install sentencepiece\'], \'example_code\': "context = \\nIndia wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England on Wednesday. Pant revealed the incident made India skipper Virat Kohli upset. I think, somebody threw a ball inside, at Siraj, so he [Kohli] was upset, said Pant in a virtual press conference after the close of the first day\'s play.You can say whatever you want to chant, but don\'t throw things at the fielders and all those things. It is not good for cricket, I guess, he added.In the third session of the opening day of the third Test, a section of spectators seemed to have asked Siraj the score of the match to tease the pacer. The India pacer however came with a brilliant reply as he gestured 1-0 (India leading the Test series) towards the crowd.Earlier this month, during the second Test match, there was some bad crowd behaviour on a show as some unruly fans threw champagne corks at India batsman KL Rahul.Kohli also intervened and he was seen gesturing towards the opening batsman to know more about the incident. An over later, the TV visuals showed that many champagne corks were thrown inside the playing field, and the Indian players were visibly left frustrated.Coming back to the game, after bundling out India for 78, openers Rory Burns and Haseeb Hameed ensured that England took the honours on the opening day of the ongoing third Test.At stumps, England\'s score reads 120/0 and the hosts have extended their lead to 42 runs. For the Three Lions, Burns (52) and Hameed (60) are currently unbeaten at the crease.Talking about the pitch on opening day, Pant said, They took the heavy roller, the wicket was much more settled down, and they batted nicely also, he said. But when we batted, the wicket was slightly soft, and they bowled in good areas, but we could have applied [ourselves] much better.Both England batsmen managed to see off the final session and the hosts concluded the opening day with all ten wickets intact, extending the lead to 42.(ANI)\\n\\nget_response(context)", \'performance\': {\'dataset\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 36.604, \'ROUGE-2\': 14.64, \'ROUGE-L\': 23.884, \'ROUGE-LSUM\': 32.902, \'loss\': 2.576, \'gen_len\': 76.398}}, \'description\': \'PEGASUS fine-tuned for summarization\'}', metadata={})]


 We are a sports broadcasting company, and we need to automatically identify the sports events taking place in the videos we receive.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 My organization wants to create a video categorization tool to classify and categorize various videos. Utilize the appropriate API to build this video classifier.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'camenduru/text2-video-zero\', \'api_call\': "pipeline(\'text-to-video\', model=\'camenduru/text2-video-zero\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is used for generating videos from text inputs. It is based on the Hugging Face framework and can be used with the transformers library. The model is trained on a variety of text and video datasets, and can be used for tasks such as video summarization, video generation from text prompts, and more.\'}', metadata={})]


 We have a surveillance camera in our backyard. We would like to analyze the captured videos to recognize the activities taking place in the backyard.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'ImRma/Brucelee\', \'api_call\': "pipeline(\'text-to-video\', model=\'ImRma/Brucelee\')", \'api_arguments\': [\'your_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Hugging Face model for converting Persian and English text into video.\'}', metadata={})]


 We need to classify an image's content and check if it contains a cat or a dog.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'julien-c/hotdog-not-hotdog\', \'api_call\': "pipeline(\'image-classification\', model=\'julien-c/hotdog-not-hotdog\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': 0.825}, \'description\': \'A model that classifies images as hotdog or not hotdog.\'}', metadata={})]


 Analyze the type of plants in the image provided and provide the name of the probable plant.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image-to-Image\', \'api_name\': \'GreeneryScenery/SheepsControlV3\', \'api_call\': "pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV3\')", \'api_arguments\': {\'image\': \'Path to image file\', \'text_guidance\': \'Optional text guidance for the model\'}, \'python_environment_requirements\': {\'transformers\': \'latest\', \'torch\': \'latest\'}, \'example_code\': [\'from transformers import pipeline\', "model = pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV3\')", "result = model({\'image\': \'path/to/image.jpg\', \'text_guidance\': \'Optional text guidance\'})"], \'performance\': {\'dataset\': \'GreeneryScenery/SheepsControlV3\', \'accuracy\': \'Not provided\'}, \'description\': \'GreeneryScenery/SheepsControlV3 is a model for image-to-image tasks. It can be used to generate images based on the input image and optional text guidance. The model has some limitations, such as the conditioning image not affecting the output image much. Improvements can be made by training for more epochs, using better prompts, and preprocessing the data.\'}', metadata={})]


 I want to organize my images based on the scene content. The categories I want are landscape, cityscape, beach, forest, and animals.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 We run an online store for selling electronic devices. We need to classify product images from our inventory and organize them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 Our customer wants to analyze the sentiment of their customers' feedback. The feedback is in Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'finiteautomata/beto-sentiment-analysis\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'finiteautomata/beto-sentiment-analysis\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'Hugging Face Transformers library\', \'example_code\': \'\', \'performance\': {\'dataset\': \'TASS 2020 corpus\', \'accuracy\': \'\'}, \'description\': \'Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish. Uses POS, NEG, NEU labels.\'}', metadata={})]


 A stock investor is looking to analyze the sentiment of a stock forum, such as StockTwits, to gain insights into the market sentiment for a specific stock.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentiment Inferencing for stock-related comments\', \'api_name\': \'zhayunduo/roberta-base-stocktwits-finetuned\', \'api_call\': "RobertaForSequenceClassification.from_pretrained(\'zhayunduo/roberta-base-stocktwits-finetuned\')", \'api_arguments\': {\'model\': \'RobertaForSequenceClassification\', \'tokenizer\': \'RobertaTokenizer\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import RobertaForSequenceClassification, RobertaTokenizer\\nfrom transformers import pipeline\\nimport pandas as pd\\nimport emoji\\ntokenizer_loaded = RobertaTokenizer.from_pretrained(\'zhayunduo/roberta-base-stocktwits-finetuned\')\\nmodel_loaded = RobertaForSequenceClassification.from_pretrained(\'zhayunduo/roberta-base-stocktwits-finetuned\')\\nnlp = pipeline(text-classification, model=model_loaded, tokenizer=tokenizer_loaded)\\nsentences = pd.Series([\'just buy\',\'just sell it\',\'entity rocket to the sky!\',\'go down\',\'even though it is going up, I still think it will not keep this trend in the near future\'])\\nsentences = list(sentences)\\nresults = nlp(sentences)\\nprint(results)", \'performance\': {\'dataset\': \'stocktwits\', \'accuracy\': 0.9343}, \'description\': "This model is fine-tuned with roberta-base model on 3,200,000 comments from stocktwits, with the user-labeled tags \'Bullish\' or \'Bearish\'."}', metadata={})]


 A forum moderator wants a tool to assess user-generated comments for toxic content. How does this model help?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'martin-ha/toxic-comment-model\', \'api_call\': "pipeline(model=\'martin-ha/toxic-comment-model\')", \'api_arguments\': {\'model_path\': \'martin-ha/toxic-comment-model\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline\\nmodel_path = martin-ha/toxic-comment-model\\ntokenizer = AutoTokenizer.from_pretrained(model_path)\\nmodel = AutoModelForSequenceClassification.from_pretrained(model_path)\\npipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)\\nprint(pipeline(\'This is a test text.\'))", \'performance\': {\'dataset\': \'held-out test set\', \'accuracy\': 0.94, \'f1-score\': 0.59}, \'description\': \'This model is a fine-tuned version of the DistilBERT model to classify toxic comments.\'}', metadata={})]


 I am developing a news analysis platform. I need to predict the named entities from the articles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'finiteautomata/beto-sentiment-analysis\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'finiteautomata/beto-sentiment-analysis\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'Hugging Face Transformers library\', \'example_code\': \'\', \'performance\': {\'dataset\': \'TASS 2020 corpus\', \'accuracy\': \'\'}, \'description\': \'Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish. Uses POS, NEG, NEU labels.\'}', metadata={})]


 We need to analyze the user's text for extracting entities and improve our virtual assistant interaction.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 Extract the named entities from a given text snippet.\n###Input: \"On June 7th, Jane Smith visited the Empire State Building in New York with an entry fee of 35 dollars.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'flair/ner-english-ontonotes\', \'api_call\': "SequenceTagger.load(\'flair/ner-english-ontonotes\')", \'api_arguments\': [\'sentence\'], \'python_environment_requirements\': [\'flair\'], \'example_code\': "from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\ntagger = SequenceTagger.load(flair/ner-english-ontonotes)\\nsentence = Sentence(On September 1st George Washington won 1 dollar.)\\ntagger.predict(sentence)\\nprint(sentence)\\nfor entity in sentence.get_spans(\'ner\'):\\n    print(entity)", \'performance\': {\'dataset\': \'Ontonotes\', \'accuracy\': \'89.27\'}, \'description\': \'This is the 18-class NER model for English that ships with Flair. It predicts 18 tags such as cardinal value, date value, event name, building name, geo-political entity, language name, law name, location name, money name, affiliation, ordinal value, organization name, percent value, person name, product name, quantity value, time value, and name of work of art. Based on Flair embeddings and LSTM-CRF.\'}', metadata={})]


 As a surveillance analyst, my main task this week is to find out all the names and locations mentioned in the online chat rooms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 I need a program to identify the entities like persons, locations, organizations, and other names in a given German text.\n
 Use this API documentation for reference: [Document(page_content="{'domain': 'Natural Language Processing Text Classification', 'framework': 'Hugging Face Transformers', 'functionality': 'German Sentiment Classification', 'api_name': 'oliverguhr/german-sentiment-bert', 'api_call': 'SentimentModel()', 'api_arguments': ['texts'], 'python_environment_requirements': 'pip install germansentiment', 'example_code': ['from germansentiment import SentimentModel', 'model = SentimentModel()', 'texts = [', ' Mit keinem guten Ergebniss,Das ist gar nicht mal so gut,', ' Total awesome!,nicht so schlecht wie erwartet,', ' Der Test verlief positiv.,Sie fhrt ein grnes Auto.]', 'result = model.predict_sentiment(texts)', 'print(result)'], 'performance': {'dataset': ['holidaycheck', 'scare', 'filmstarts', 'germeval', 'PotTS', 'emotions', 'sb10k', 'Leipzig Wikipedia Corpus 2016', 'all'], 'accuracy': [0.9568, 0.9418, 0.9021, 0.7536, 0.678, 0.9649, 0.7376, 0.9967, 0.9639]}, 'description': 'This model was trained for sentiment classification of German language texts. The model uses the Googles Bert architecture and was trained on 1.834 million German-language samples. The training data contains texts from various domains like Twitter, Facebook and movie, app and hotel reviews.'}", metadata={})]


 We are a multi-national company that would like to better understand global events and extract relevant named entities across 9 languages (de, en, es, fr, it, nl, pl, pt, ru). We would like to utilize a pre-trained NER model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'Davlan/bert-base-multilingual-cased-ner-hrl\', \'api_call\': "AutoModelForTokenClassification.from_pretrained(\'Davlan/bert-base-multilingual-cased-ner-hrl\')", \'api_arguments\': {\'model\': \'AutoModelForTokenClassification.from_pretrained(Davlan/bert-base-multilingual-cased-ner-hrl)\', \'tokenizer\': \'AutoTokenizer.from_pretrained(Davlan/bert-base-multilingual-cased-ner-hrl)\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\'}, \'example_code\': \'example = Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute.; ner_results = nlp(example); print(ner_results)\', \'performance\': {\'dataset\': {\'Arabic\': \'ANERcorp\', \'German\': \'conll 2003\', \'English\': \'conll 2003\', \'Spanish\': \'conll 2002\', \'French\': \'Europeana Newspapers\', \'Italian\': \'Italian I-CAB\', \'Latvian\': \'Latvian NER\', \'Dutch\': \'conll 2002\', \'Portuguese\': \'Paramopama + Second Harem\', \'Chinese\': \'MSRA\'}, \'accuracy\': \'Not provided\'}, \'description\': \'bert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).\'}', metadata={})]


 We have a multimedia app in the Korean language. To deal with customer queries automatically, we want to incorporate question answering capability.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'monologg/koelectra-small-v2-distilled-korquad-384\', \'api_call\': "pipeline(\'question-answering\', model=\'monologg/koelectra-small-v2-distilled-korquad-384\')", \'api_arguments\': {\'model\': \'monologg/koelectra-small-v2-distilled-korquad-384\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "nlp(question=\'your_question\', context=\'your_context\')", \'performance\': {\'dataset\': \'KorQuAD\', \'accuracy\': \'Not provided\'}, \'description\': \'A Korean Question Answering model based on Electra and trained on the KorQuAD dataset.\'}', metadata={})]


 Guide me to create a quiz project where I will summarize an article into a paragraph and from the summary I will develop a question with some multiple options. I need to check the correct answer for that question.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'tuner007/pegasus_summarizer\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'tuner007/pegasus_summarizer\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'pip install sentencepiece\'], \'example_code\': "context = \\nIndia wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England on Wednesday. Pant revealed the incident made India skipper Virat Kohli upset. I think, somebody threw a ball inside, at Siraj, so he [Kohli] was upset, said Pant in a virtual press conference after the close of the first day\'s play.You can say whatever you want to chant, but don\'t throw things at the fielders and all those things. It is not good for cricket, I guess, he added.In the third session of the opening day of the third Test, a section of spectators seemed to have asked Siraj the score of the match to tease the pacer. The India pacer however came with a brilliant reply as he gestured 1-0 (India leading the Test series) towards the crowd.Earlier this month, during the second Test match, there was some bad crowd behaviour on a show as some unruly fans threw champagne corks at India batsman KL Rahul.Kohli also intervened and he was seen gesturing towards the opening batsman to know more about the incident. An over later, the TV visuals showed that many champagne corks were thrown inside the playing field, and the Indian players were visibly left frustrated.Coming back to the game, after bundling out India for 78, openers Rory Burns and Haseeb Hameed ensured that England took the honours on the opening day of the ongoing third Test.At stumps, England\'s score reads 120/0 and the hosts have extended their lead to 42 runs. For the Three Lions, Burns (52) and Hameed (60) are currently unbeaten at the crease.Talking about the pitch on opening day, Pant said, They took the heavy roller, the wicket was much more settled down, and they batted nicely also, he said. But when we batted, the wicket was slightly soft, and they bowled in good areas, but we could have applied [ourselves] much better.Both England batsmen managed to see off the final session and the hosts concluded the opening day with all ten wickets intact, extending the lead to 42.(ANI)\\n\\nget_response(context)", \'performance\': {\'dataset\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 36.604, \'ROUGE-2\': 14.64, \'ROUGE-L\': 23.884, \'ROUGE-LSUM\': 32.902, \'loss\': 2.576, \'gen_len\': 76.398}}, \'description\': \'PEGASUS fine-tuned for summarization\'}', metadata={})]


 I have a website text about technology and I want to know if it represents a positive sentiment or a negative one.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'it5-base-news-summarization\', \'api_call\': "pipeline(\'summarization\', model=\'it5/it5-base-news-summarization\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'newsum(Dal 31 maggio è infine partita la piattaforma ITsART, a più di un anno da quando – durante il primo lockdown – il ministro della Cultura Dario Franceschini ne aveva parlato come di «una sorta di Netflix della cultura», pensata per «offrire a tutto il mondo la cultura italiana a pagamento». È presto per dare giudizi definitivi sulla piattaforma, e di certo sarà difficile farlo anche più avanti senza numeri precisi. Al momento, l’unica cosa che si può fare è guardare com’è fatto il sito, contare quanti contenuti ci sono (circa 700 “titoli”, tra film, documentari, spettacoli teatrali e musicali e altri eventi) e provare a dare un giudizio sul loro valore e sulla loro varietà. Intanto, una cosa notata da più parti è che diversi contenuti di ITsART sono a pagamento sulla piattaforma sebbene altrove, per esempio su RaiPlay, siano invece disponibili gratuitamente.)\', \'performance\': {\'dataset\': \'NewsSum-IT\', \'accuracy\': {\'Rouge1\': 0.339, \'Rouge2\': 0.16, \'RougeL\': 0.263}}, \'description\': \'IT5 Base model fine-tuned on news summarization on the Fanpage and Il Post corpora for Italian Language Understanding and Generation.\'}', metadata={})]


 Translate a French website description containing information about the company, services, and copyright notice into English.\n###Input: \"Bienvenue sur notre site ! Nous sommes une entreprise sp\u00e9cialis\u00e9e dans la gestion des projets informatiques et la cr\u00e9ation de logiciels sur mesure. Nos services incluent la conception, le d\u00e9veloppement, la maintenance et le support 24/7. Tous droits r\u00e9serv\u00e9s.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-fr\', \'api_call\': "translate(\'input_text\', model=\'Helsinki-NLP/opus-mt-en-fr\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.en.fr\': 33.8, \'newsdiscusstest2015-enfr.en.fr\': 40.0, \'newssyscomb2009.en.fr\': 29.8, \'news-test2008.en.fr\': 27.5, \'newstest2009.en.fr\': 29.4, \'newstest2010.en.fr\': 32.7, \'newstest2011.en.fr\': 34.3, \'newstest2012.en.fr\': 31.8, \'newstest2013.en.fr\': 33.2, \'Tatoeba.en.fr\': 50.5}}}, \'description\': \'Helsinki-NLP/opus-mt-en-fr is a translation model that translates English text to French using the Hugging Face Transformers library. It is based on the OPUS dataset and uses a transformer-align architecture with normalization and SentencePiece pre-processing.\'}', metadata={})]


 We have a list of documents written in multiple Romance languages, including texts in French, Spanish, and Italian. We want to make these texts accessible to our English-speaking audience by translating them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Translation\', \'api_name\': \'opus-mt-en-ROMANCE\', \'api_call\': "pipeline(\'translation_en_to_ROMANCE\', model=\'Helsinki-NLP/opus-mt-en-ROMANCE\')", \'api_arguments\': \'source languages, target languages\', \'python_environment_requirements\': \'transformers, torch\', \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_en_to_ROMANCE\', model=\'Helsinki-NLP/opus-mt-en-ROMANCE\')\\ntranslated_text = translation(\'Hello, how are you?\', tgt_lang=\'es\')", \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': 50.1, \'chr-F\': 0.693}}, \'description\': \'A translation model trained on the OPUS dataset that supports translation between English and various Romance languages. It uses a transformer architecture and requires a sentence initial language token in the form of >>id<< (id = valid target language ID).\'}', metadata={})]


 We have an international audience for our website and require our website content to be translated into multiple languages for better understanding.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'PyTorch Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'facebook/nllb-200-distilled-600M\', \'api_call\': "pipeline(\'translation_xx_to_yy\', model=\'facebook/nllb-200-distilled-600M\')", \'api_arguments\': [\'model\', \'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import pipeline; translator = pipeline(\'translation_xx_to_yy\', model=\'facebook/nllb-200-distilled-600M\'); translator(\'Hello World\')", \'performance\': {\'dataset\': \'Flores-200\', \'accuracy\': \'BLEU, spBLEU, chrF++\'}, \'description\': \'NLLB-200 is a machine translation model primarily intended for research in machine translation, especially for low-resource languages. It allows for single sentence translation among 200 languages. The model was trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation.\'}', metadata={})]


 I am working for the review section of a book company. I want to convert a book summary into a positive book review.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'pszemraj/long-t5-tglobal-base-16384-book-summary\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'pszemraj/long-t5-tglobal-base-16384-book-summary\')", \'api_arguments\': [\'long_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\nimport torch\\nsummarizer = pipeline(\\n summarization,\\n pszemraj/long-t5-tglobal-base-16384-book-summary,\\n device=0 if torch.cuda.is_available() else -1,\\n)\\nlong_text = Here is a lot of text I don\'t want to read. Replace me\\nresult = summarizer(long_text)\\nprint(result[0][summary_text])", \'performance\': {\'dataset\': \'kmfoda/booksum\', \'accuracy\': {\'ROUGE-1\': 36.408, \'ROUGE-2\': 6.065, \'ROUGE-L\': 16.721, \'ROUGE-LSUM\': 33.34}}, \'description\': \'A fine-tuned version of google/long-t5-tglobal-base on the kmfoda/booksum dataset, which can be used to summarize long text and generate SparkNotes-esque summaries of arbitrary topics. The model generalizes reasonably well to academic and narrative text.\'}', metadata={})]


 We are a company that wants to expand globally. We need to translate our website content from English to Italian.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-it-en\', \'api_call\': "pipeline(\'translation_it_to_en\', model=\'Helsinki-NLP/opus-mt-it-en\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_it_to_en\', model=\'Helsinki-NLP/opus-mt-it-en\')(\'Ciao mondo!\')", \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newssyscomb2009.it.en\': 35.3, \'newstest2009.it.en\': 34.0, \'Tatoeba.it.en\': 70.9}, \'chr-F\': {\'newssyscomb2009.it.en\': 0.6, \'newstest2009.it.en\': 0.594, \'Tatoeba.it.en\': 0.808}}}, \'description\': \'A transformer model for Italian to English translation trained on the OPUS dataset. It can be used for translating Italian text to English.\'}', metadata={})]


 Our company is working on a news website. We want to present summaries of news articles written in French to the users. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'it5-base-news-summarization\', \'api_call\': "pipeline(\'summarization\', model=\'it5/it5-base-news-summarization\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'newsum(Dal 31 maggio è infine partita la piattaforma ITsART, a più di un anno da quando – durante il primo lockdown – il ministro della Cultura Dario Franceschini ne aveva parlato come di «una sorta di Netflix della cultura», pensata per «offrire a tutto il mondo la cultura italiana a pagamento». È presto per dare giudizi definitivi sulla piattaforma, e di certo sarà difficile farlo anche più avanti senza numeri precisi. Al momento, l’unica cosa che si può fare è guardare com’è fatto il sito, contare quanti contenuti ci sono (circa 700 “titoli”, tra film, documentari, spettacoli teatrali e musicali e altri eventi) e provare a dare un giudizio sul loro valore e sulla loro varietà. Intanto, una cosa notata da più parti è che diversi contenuti di ITsART sono a pagamento sulla piattaforma sebbene altrove, per esempio su RaiPlay, siano invece disponibili gratuitamente.)\', \'performance\': {\'dataset\': \'NewsSum-IT\', \'accuracy\': {\'Rouge1\': 0.339, \'Rouge2\': 0.16, \'RougeL\': 0.263}}, \'description\': \'IT5 Base model fine-tuned on news summarization on the Fanpage and Il Post corpora for Italian Language Understanding and Generation.\'}', metadata={})]


 We are an international business and need to translate our documents from French to Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-fr\', \'api_call\': "translate(\'input_text\', model=\'Helsinki-NLP/opus-mt-en-fr\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.en.fr\': 33.8, \'newsdiscusstest2015-enfr.en.fr\': 40.0, \'newssyscomb2009.en.fr\': 29.8, \'news-test2008.en.fr\': 27.5, \'newstest2009.en.fr\': 29.4, \'newstest2010.en.fr\': 32.7, \'newstest2011.en.fr\': 34.3, \'newstest2012.en.fr\': 31.8, \'newstest2013.en.fr\': 33.2, \'Tatoeba.en.fr\': 50.5}}}, \'description\': \'Helsinki-NLP/opus-mt-en-fr is a translation model that translates English text to French using the Hugging Face Transformers library. It is based on the OPUS dataset and uses a transformer-align architecture with normalization and SentencePiece pre-processing.\'}', metadata={})]


 The company needs to summarize articles for its news application. Provide guidelines to use PEGASUS for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'google/pegasus-xsum\', \'api_call\': "pipeline(\'summarization\', model=\'google/pegasus-xsum\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': [{\'name\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 46.862, \'ROUGE-2\': 24.453, \'ROUGE-L\': 39.055, \'ROUGE-LSUM\': 39.099}}, {\'name\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 22.206, \'ROUGE-2\': 7.67, \'ROUGE-L\': 15.405, \'ROUGE-LSUM\': 19.218}}, {\'name\': \'samsum\', \'accuracy\': {\'ROUGE-1\': 21.81, \'ROUGE-2\': 4.253, \'ROUGE-L\': 17.447, \'ROUGE-LSUM\': 18.891}}]}, \'description\': \'PEGASUS is a pre-trained model for abstractive summarization, developed by Google. It is based on the Transformer architecture and trained on both C4 and HugeNews datasets. The model is designed to extract gap sentences and generate summaries by stochastically sampling important sentences.\'}', metadata={})]


 You are working as a news curator for a media company and need to make a short summary of a long news article.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'it5-base-news-summarization\', \'api_call\': "pipeline(\'summarization\', model=\'it5/it5-base-news-summarization\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'newsum(Dal 31 maggio è infine partita la piattaforma ITsART, a più di un anno da quando – durante il primo lockdown – il ministro della Cultura Dario Franceschini ne aveva parlato come di «una sorta di Netflix della cultura», pensata per «offrire a tutto il mondo la cultura italiana a pagamento». È presto per dare giudizi definitivi sulla piattaforma, e di certo sarà difficile farlo anche più avanti senza numeri precisi. Al momento, l’unica cosa che si può fare è guardare com’è fatto il sito, contare quanti contenuti ci sono (circa 700 “titoli”, tra film, documentari, spettacoli teatrali e musicali e altri eventi) e provare a dare un giudizio sul loro valore e sulla loro varietà. Intanto, una cosa notata da più parti è che diversi contenuti di ITsART sono a pagamento sulla piattaforma sebbene altrove, per esempio su RaiPlay, siano invece disponibili gratuitamente.)\', \'performance\': {\'dataset\': \'NewsSum-IT\', \'accuracy\': {\'Rouge1\': 0.339, \'Rouge2\': 0.16, \'RougeL\': 0.263}}, \'description\': \'IT5 Base model fine-tuned on news summarization on the Fanpage and Il Post corpora for Italian Language Understanding and Generation.\'}', metadata={})]


 Assemble a conversational bot for my online business to answer questions regarding our products.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 We are developing a game and need a text-based AI to control the main character's behavior. Help us use this chatbot based on the Joshua character.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 We are creating an AI assistant for banking clients. The customers should be able to talk to the bot to do various tasks. We need to have a meaningful dialogue with them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 We want to facilitate a conversation with our Russian customers. Build a model that can respond to customer inquiries in Russian.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 We are launching a new blog and need a paragraph with tips on how to take care of houseplants.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


 The development team is trying to create a function in Python to print \"Hello, World!\" but they're not sure how to proceed. Generate this function for them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


 We need to generate a motivational quote related to sports.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'tuner007/pegasus_summarizer\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'tuner007/pegasus_summarizer\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'pip install sentencepiece\'], \'example_code\': "context = \\nIndia wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England on Wednesday. Pant revealed the incident made India skipper Virat Kohli upset. I think, somebody threw a ball inside, at Siraj, so he [Kohli] was upset, said Pant in a virtual press conference after the close of the first day\'s play.You can say whatever you want to chant, but don\'t throw things at the fielders and all those things. It is not good for cricket, I guess, he added.In the third session of the opening day of the third Test, a section of spectators seemed to have asked Siraj the score of the match to tease the pacer. The India pacer however came with a brilliant reply as he gestured 1-0 (India leading the Test series) towards the crowd.Earlier this month, during the second Test match, there was some bad crowd behaviour on a show as some unruly fans threw champagne corks at India batsman KL Rahul.Kohli also intervened and he was seen gesturing towards the opening batsman to know more about the incident. An over later, the TV visuals showed that many champagne corks were thrown inside the playing field, and the Indian players were visibly left frustrated.Coming back to the game, after bundling out India for 78, openers Rory Burns and Haseeb Hameed ensured that England took the honours on the opening day of the ongoing third Test.At stumps, England\'s score reads 120/0 and the hosts have extended their lead to 42 runs. For the Three Lions, Burns (52) and Hameed (60) are currently unbeaten at the crease.Talking about the pitch on opening day, Pant said, They took the heavy roller, the wicket was much more settled down, and they batted nicely also, he said. But when we batted, the wicket was slightly soft, and they bowled in good areas, but we could have applied [ourselves] much better.Both England batsmen managed to see off the final session and the hosts concluded the opening day with all ten wickets intact, extending the lead to 42.(ANI)\\n\\nget_response(context)", \'performance\': {\'dataset\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 36.604, \'ROUGE-2\': 14.64, \'ROUGE-L\': 23.884, \'ROUGE-LSUM\': 32.902, \'loss\': 2.576, \'gen_len\': 76.398}}, \'description\': \'PEGASUS fine-tuned for summarization\'}', metadata={})]


 Give me a tool that I can use to generate stories based on a starting phrase.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


 Our PR team requires a machine learning approach to creative sentence generation for marketing content creation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


 We are developing an AI-powered code review system. Our model should provide a short summary of the provided code snippet.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'pszemraj/long-t5-tglobal-base-16384-book-summary\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'pszemraj/long-t5-tglobal-base-16384-book-summary\')", \'api_arguments\': [\'long_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\nimport torch\\nsummarizer = pipeline(\\n summarization,\\n pszemraj/long-t5-tglobal-base-16384-book-summary,\\n device=0 if torch.cuda.is_available() else -1,\\n)\\nlong_text = Here is a lot of text I don\'t want to read. Replace me\\nresult = summarizer(long_text)\\nprint(result[0][summary_text])", \'performance\': {\'dataset\': \'kmfoda/booksum\', \'accuracy\': {\'ROUGE-1\': 36.408, \'ROUGE-2\': 6.065, \'ROUGE-L\': 16.721, \'ROUGE-LSUM\': 33.34}}, \'description\': \'A fine-tuned version of google/long-t5-tglobal-base on the kmfoda/booksum dataset, which can be used to summarize long text and generate SparkNotes-esque summaries of arbitrary topics. The model generalizes reasonably well to academic and narrative text.\'}', metadata={})]


 I want a language model that can fill in short blanks for example sentences, quizzes, or trivia questions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Masked Language Modeling\', \'api_name\': \'distilbert-base-multilingual-cased\', \'api_call\': "pipeline(\'fill-mask\', model=\'distilbert-base-multilingual-cased\')", \'api_arguments\': [\'pipeline\', \'fill-mask\', \'model\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\nunmasker = pipeline(\'fill-mask\', model=\'distilbert-base-multilingual-cased\')\\nunmasker(Hello I\'m a [MASK] model.)", \'performance\': {\'dataset\': [{\'name\': \'XNLI\', \'accuracy\': {\'English\': 78.2, \'Spanish\': 69.1, \'Chinese\': 64.0, \'German\': 66.3, \'Arabic\': 59.1, \'Urdu\': 54.7}}]}, \'description\': \'This model is a distilled version of the BERT base multilingual model. It is trained on the concatenation of Wikipedia in 104 different languages. The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters. On average, this model, referred to as DistilmBERT, is twice as fast as mBERT-base.\'}', metadata={})]


 As a language teacher, I sometimes need help completing Dutch sentences with the most suitable word. Can you fill in the gaps?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'GroNLP/bert-base-dutch-cased\', \'api_call\': "AutoModel.from_pretrained(\'GroNLP/bert-base-dutch-cased\')", \'api_arguments\': [\'pretrained_model_name_or_path\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModel, TFAutoModel\\ntokenizer = AutoTokenizer.from_pretrained(GroNLP/bert-base-dutch-cased)\\nmodel = AutoModel.from_pretrained(GroNLP/bert-base-dutch-cased)\', \'performance\': {\'dataset\': [{\'name\': \'CoNLL-2002\', \'accuracy\': \'90.24\'}, {\'name\': \'SoNaR-1\', \'accuracy\': \'84.93\'}, {\'name\': \'spaCy UD LassySmall\', \'accuracy\': \'86.10\'}]}, \'description\': \'BERTje is a Dutch pre-trained BERT model developed at the University of Groningen.\'}', metadata={})]


 Translate some lyrics of a Portuguese song into English\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Speech Recognition\', \'api_name\': \'jonatasgrosman/wav2vec2-large-xlsr-53-portuguese\', \'api_call\': "SpeechRecognitionModel(\'jonatasgrosman/wav2vec2-large-xlsr-53-portuguese\')", \'api_arguments\': [\'audio_paths\'], \'python_environment_requirements\': [\'huggingsound\', \'torch\', \'librosa\', \'datasets\', \'transformers\'], \'example_code\': \'from huggingsound import SpeechRecognitionModel\\nmodel = SpeechRecognitionModel(jonatasgrosman/wav2vec2-large-xlsr-53-portuguese)\\naudio_paths = [/path/to/file.mp3, /path/to/another_file.wav]\\ntranscriptions = model.transcribe(audio_paths)\', \'performance\': {\'dataset\': \'mozilla-foundation/common_voice_6_0\', \'accuracy\': {\'Test WER\': 11.31, \'Test CER\': 3.74, \'Test WER (+LM)\': 9.01, \'Test CER (+LM)\': 3.21}}, \'description\': \'Fine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz.\'}', metadata={})]


 We are a news portal website and recently acquired a breaking news article. Determine the similarity between the main text of this article and other articles in our database.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'it5-base-news-summarization\', \'api_call\': "pipeline(\'summarization\', model=\'it5/it5-base-news-summarization\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'newsum(Dal 31 maggio è infine partita la piattaforma ITsART, a più di un anno da quando – durante il primo lockdown – il ministro della Cultura Dario Franceschini ne aveva parlato come di «una sorta di Netflix della cultura», pensata per «offrire a tutto il mondo la cultura italiana a pagamento». È presto per dare giudizi definitivi sulla piattaforma, e di certo sarà difficile farlo anche più avanti senza numeri precisi. Al momento, l’unica cosa che si può fare è guardare com’è fatto il sito, contare quanti contenuti ci sono (circa 700 “titoli”, tra film, documentari, spettacoli teatrali e musicali e altri eventi) e provare a dare un giudizio sul loro valore e sulla loro varietà. Intanto, una cosa notata da più parti è che diversi contenuti di ITsART sono a pagamento sulla piattaforma sebbene altrove, per esempio su RaiPlay, siano invece disponibili gratuitamente.)\', \'performance\': {\'dataset\': \'NewsSum-IT\', \'accuracy\': {\'Rouge1\': 0.339, \'Rouge2\': 0.16, \'RougeL\': 0.263}}, \'description\': \'IT5 Base model fine-tuned on news summarization on the Fanpage and Il Post corpora for Italian Language Understanding and Generation.\'}', metadata={})]


 As a business assistant of an international company, find the most relevant sentence among a list of sentences that answers a specific question.\n###Input: {\"question\": \"What is the main purpose of photosynthesis?\", \"sentences\": [\"Photosynthesis is the process used by plants to convert light energy into chemical energy to fuel their growth.\", \"The Eiffel Tower is a famous landmark in Paris.\", \"Photosynthesis also produces oxygen as a byproduct, which is necessary for life on Earth.\"]}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Sentence Correction\', \'api_name\': \'flexudy/t5-base-multi-sentence-doctor\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'flexudy/t5-base-multi-sentence-doctor\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\nmodel = AutoModelWithLMHead.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\ninput_text = repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>\\ninput_ids = tokenizer.encode(input_text, return_tensors=pt)\\noutputs = model.generate(input_ids, max_length=32, num_beams=1)\\nsentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\\nassert sentence == I am a medical doctor.\', \'performance\': {\'dataset\': \'tatoeba\', \'accuracy\': \'Not specified\'}, \'description\': \'Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.\'}', metadata={})]


 We need to cluster customer reviews based on their content similarity to understand the common issues customers are facing.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'michellejieli/NSFW_text_classifier\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'michellejieli/NSFW_text_classification\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'classifier(I see you’ve set aside this special time to humiliate yourself in public.)\', \'performance\': {\'dataset\': \'Reddit posts\', \'accuracy\': \'Not specified\'}, \'description\': \'DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of DistilBERT. It was fine-tuned on 14317 Reddit posts pulled from the Reddit API.\'}', metadata={})]


 Create a program to calculate sentence similarity scores between a list of sentences.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentence Transformers\', \'api_name\': \'sentence-transformers/distiluse-base-multilingual-cased-v2\', \'api_call\': "SentenceTransformer(\'sentence-transformers/distiluse-base-multilingual-cased-v2\')", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': \'pip install -U sentence-transformers\', \'example_code\': "from sentence_transformers import SentenceTransformer\\nsentences = [This is an example sentence, Each sentence is converted]\\nmodel = SentenceTransformer(\'sentence-transformers/distiluse-base-multilingual-cased-v2\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


 We need to find a similar Chinese sentence to my source sentence for a suggestion.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'text2vec-large-chinese\', \'api_call\': "AutoModel.from_pretrained(\'GanymedeNil/text2vec-large-chinese\')", \'api_arguments\': \'source_sentence, sentences_to_compare\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import AutoModel, AutoTokenizer\\n\\ntokenizer = AutoTokenizer.from_pretrained(\'GanymedeNil/text2vec-large-chinese\')\\nmodel = AutoModel.from_pretrained(\'GanymedeNil/text2vec-large-chinese\')", \'performance\': {\'dataset\': \'https://huggingface.co/shibing624/text2vec-base-chinese\', \'accuracy\': \'Not provided\'}, \'description\': \'A Chinese sentence similarity model based on the derivative model of https://huggingface.co/shibing624/text2vec-base-chinese, replacing MacBERT with LERT, and keeping other training conditions unchanged.\'}', metadata={})]


 I am building a voice assistant for my mobile app. Give me an example of how I can implement this Text-to-Speech model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'mio/Artoria\', \'api_call\': "pipeline(\'text-to-speech\', model=\'mio/Artoria\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline; tts = pipeline(\'text-to-speech\', model=\'mio/Artoria\'); tts(\'s\')", \'performance\': {\'dataset\': \'fate\', \'accuracy\': \'Not provided\'}, \'description\': \'This model was trained by mio using fate recipe in espnet. It is a text-to-speech model that can convert text input into speech output.\'}', metadata={})]


 Our team wants to transcribe audio files into text including punctuation marks for usability studies.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Fairseq\', \'functionality\': \'speech-to-speech-translation\', \'api_name\': \'facebook/textless_sm_ro_en\', \'api_call\': "pipeline(\'audio-to-audio\', model=\'facebook/textless_sm_ro_en\')", \'api_arguments\': \'audio file or recording\', \'python_environment_requirements\': \'fairseq, huggingface_hub\', \'example_code\': \'https://huggingface.co/facebook/textless_sm_cs_en\', \'performance\': {\'dataset\': \'unknown\', \'accuracy\': \'unknown\'}, \'description\': \'A speech-to-speech translation model for Romanian to English developed by Facebook AI\'}', metadata={})]