Model: truong1301/bi-encode-HG-DOCS
Author: truong1301
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:6300, loss:CachedMultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2101.06983, base_model:sentence-transformers/all-MiniLM-L6-v2, base_model:finetune:sentence-transformers/all-MiniLM-L6-v2, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-MiniLM-L6-v2
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:6300, loss:CachedMultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget: source_sentence: How can AnimateDiff, a motion adapter for pretrained diffusion models, be used to generate videos from images?
sentences: Performs a single real input radix-2 transformation on the provided data Kind: instance method ofP2FFT The input data array The output data array The output offset The input offset The step, AnimateDiffis an adapter model that inserts a motion module into a pretrained diffusion model to animate an image. The adapter is trained on video clips to learn motion which is used to condition the generation process to create a video. It is faster and easier to only train the adapter and it can be loaded into most diffusion models, effectively turning them into “video models”. Start by loading aMotionAdapter. Then load a finetuned Stable Diffusion model with theAnimateDiffPipeline. Create a prompt and generate the video., Utility class to handle streaming of tokens generated by whisper speech-to-text models. Callback functions are invoked when each of the following events occur: Kind: static class ofgeneration/streamers, source_sentence: How to configure DeepSpeed, including ZeRO-2 and bf16 precision, for optimal performance with Intel Gaudi HPUs?
sentences: The DeepSpeed configuration to use is passed through a JSON file and enables you to choose the optimizations to apply. Here is an example for applying ZeRO-2 optimizations andbf16precision: The special value"auto"enables to automatically get the correct or most efficient value. You can also specify the values yourself but, if you do so, you should be careful not to have conflicting values with your training arguments. It is strongly advised to readthis sectionin the Transformers documentation to completely understand how this works. Other examples of configurations for HPUs are proposedhereby Intel. TheTransformers documentationexplains how to write a configuration from scratch very well. A more complete description of all configuration possibilities is availablehere., Creates a new instance of TokenizerModel. The configuration object for the TokenizerModel., Most Spaces should run out of the box after a GPU upgrade, but sometimes you’ll need to install CUDA versions of the machine learning frameworks you use. Please, follow this guide to ensure your Space takes advantage of the improved hardware., source_sentence: Can DeBERTa's question-answering model be fine-tuned for improved information retrieval?
sentences: RegNetXis a convolutional network design space with simple, regular models with parameters: depthddd, initial widthw0>0w_{0} > 0w0​>0, and slopewa>0w_{a} > 0wa​>0, and generates a different block widthuju_{j}uj​for each blockj<dj < dj<d. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):uj=w0+wa⋅ju_{j} = w_{0} + w_{a}\cdot{j}uj​=w0​+wa​⋅j ForRegNetXwe have additional restrictions: we setb=1b = 1b=1(the bottleneck ratio),12≤d≤2812 \leq d \leq 2812≤d≤28, andwm≥2w_{m} \geq 2wm​≥2(the width multiplier)., DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to computespan start logitsandspan end logits). Kind: static class ofmodels, The minimum length of the sequence to be generated. Corresponds to the length of the input prompt +min_new_tokens. Its effect is overridden bymin_new_tokens, if also set. Kind: instance property ofGenerationConfigDefault:0, source_sentence: How can I efficiently upload models from supported libraries like Transformers to the Hugging Face Hub for improved information retrieval?
sentences: 🤗 Diffusers is compatible with Habana Gaudi through 🤗Optimum. Follow theinstallationguide to install the SynapseAI and Gaudi drivers, and then install Optimum Habana: To generate images with Stable Diffusion 1 and 2 on Gaudi, you need to instantiate two instances: When you initialize the pipeline, you have to specifyuse_habana=Trueto deploy it on HPUs and to get the fastest possible generation, you should enableHPU graphswithuse_hpu_graphs=True. Finally, specify aGaudiConfigwhich can be downloaded from theHabanaorganization on the Hub. Now you can call the pipeline to generate images by batches from one or several prompts: For more information, check out 🤗 Optimum Habana’sdocumentationand theexampleprovided in the official GitHub repository., While training and evaluating we record the following reward metrics:, First check if your model is from a library that has built-in support to push to/load from the Hub, like Transformers, Diffusers, Timm, Asteroid, etc.:https://huggingface.co/docs/hub/models-libraries. Below we’ll show how easy this is for a library like Transformers: Some libraries, like Transformers, support loadingcode from the Hub. This is a way to make your model work with Transformers using thetrust_remote_code=Trueflag. You may want to consider this option instead of a full-fledged library integration., source_sentence: How can I use Shiny for Python to build and deploy a Hugging Face Space application?
sentences: Shiny for Pythonis a pure Python implementation of Shiny. This gives you access to all of the great features of Shiny like reactivity, complex layouts, and modules without needing to use R. Shiny for Python is ideal for Hugging Face applications because it integrates smoothly with other Hugging Face tools. To get started deploying a Space, click this button to select your hardware and specify if you want a public or private Space. The Space template will populate a few files to get your app started. app.py This file defines your app’s logic. To learn more about how to modify this file, seethe Shiny for Python documentation. As your app gets more complex, it’s a good idea to break your application logic up intomodules. Dockerfile The Dockerfile for a Shiny for Python app is very minimal because the library doesn’t have many system dependencies, but you may need to modify this file if your application has additional system dependencies. The one essential feature of this file is that it exposes and runs the app on the port specified in the space README file (which is 7860 by default). requirements.txt The Space will automatically install dependencies listed in the requirements.txt file. Note that you must include shiny in this file., (**kwargs) A context manager that will add each keyword argument passed toos.environand remove them when exiting. Will convert the values inkwargsto strings and upper-case all the keys. () A context manager that will temporarily clear environment variables. When this context exits, the previous environment variables will be back. (mixed_precision= 'no'save_location: str = '/github/home/.cache/huggingface/accelerate/default_config.yaml'use_xpu: bool = False) Parameters Creates and saves a basic cluster config to be used on a local machine with potentially multiple GPUs. Will also set CPU if it is a CPU-only machine. When setting up 🤗 Accelerate for the first time, rather than runningaccelerate config[~utils.write_basic_config] can be used as an alternative for quick configuration. (local_process_index: intverbose: typing.Optional[bool] = None) Parameters Assigns the current process to a specific NUMA node. Ideally most efficient when having at least 2 cpus per node. This result is cached between calls. If you want to override it, please useaccelerate.utils.environment.override_numa_afifnity. (local_process_index: intverbose: typing.Optional[bool] = None) Parameters Overrides whatever NUMA affinity is set for the current process. This is very taxing and requires recalculating the affinity to set, ideally you should useutils.environment.set_numa_affinityinstead. (func_or_cls) Decorator to clean up accelerate environment variables set by the decorated class or function. In some circumstances, calling certain classes or functions can result in accelerate env vars being set and not being cleaned up afterwards. As an example, when calling: TrainingArguments(fp16=True, …) The following env var will be set: ACCELERATE_MIXED_PRECISION=fp16 This can affect subsequent code, since the env var takes precedence over TrainingArguments(fp16=False). This is especially relevant for unit testing, where we want to avoid the individual tests to have side effects on one another. Decorate the unit test function or whole class with this decorator to ensure that after each test, the env vars are cleaned up. This works for both unittest.TestCase and normal classes (pytest); it also works when decorating the parent class., Performs a real-valued forward FFT on the given input buffer and stores the result in the given output buffer. The input buffer must contain real values only, while the output buffer will contain complex values. The input and output buffers must be different. Kind: instance method ofP2FFTThrows: The output buffer. The input buffer containing real values.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
Likes: 0 | All-time downloads: 8
Created: 2025-04-03 09:01:15