huggingface generate | Generation

Keyword	CPC	PCC	Volume	Score	Length of keyword
huggingface generate	1.98	0.5	438	32	20
huggingface	1.26	1	4594	36	11
generate	1.42	0.1	165	88	8

Keyword	CPC	PCC	Volume	Score
huggingface generate	1.13	0.6	3410	67
huggingface generate stop token	0.11	0.3	137	23
huggingface generate batch	1.73	1	5478	18
huggingface generate temperature	1.16	0.5	5126	48
huggingface generate beam search	0.65	0.1	7239	58
huggingface generate config	0.68	0.3	4232	30
huggingface generate api	0.59	0.8	8918	78
huggingface generate max_length	0.77	0.7	6976	83
huggingface generate function	1.89	0.7	4512	27
huggingface generate text	1.04	0.9	724	13
huggingface generate max_new_tokens	1.74	0.4	8385	55
huggingface generate repetition penalty	0.01	1	3716	42
model generate huggingface	0.32	0.9	8692	76
huggingface transformers generate	1.36	0.2	7128	13
generate method huggingface	1.37	0.3	6328	73
generate questions from text huggingface	0.6	0.9	7730	71
huggingface transformers model generate	1.4	0.5	6285	2
huggingface generate stopping criteria	0.91	0.6	3074	51

Search Results related to huggingface generate on Search Engine

Generation - Hugging Face
huggingface.co:443

https://huggingface.co:443/docs/transformers/v4.18.0/en/main_classes/text_generation

WebA class containing all functions for auto-regressive text generation, to be used as a mixin in PreTrainedModel.. The class exposes generate(), which can be used for:. greedy decoding by calling greedy_search() if num_beams=1 and do_sample=False.; multinomial sampling by calling sample() if num_beams=1 and do_sample=True.; beam-search decoding by calling beam_search() if num_beams>1 and do ...

DA: 95 PA: 26 MOZ Rank: 22

Generation - Hugging Face
huggingface.co:443

https://huggingface.co:443/docs/transformers/main_classes/text_generation

WebEach framework has a generate method for text generation implemented in their respective GenerationMixin class: PyTorch generate() is implemented in GenerationMixin. TensorFlow generate() is implemented in TFGenerationMixin. Flax/JAX generate() is implemented in FlaxGenerationMixin.

DA: 81 PA: 44 MOZ Rank: 63

Text generation strategies - Hugging Face
huggingface.co:443

https://huggingface.co:443/docs/transformers/generation_strategies

WebSome of the models that can generate text include GPT2, XLNet, OpenAI GPT, CTRL, TransformerXL, XLM, Bart, T5, GIT, Whisper. Check out a few examples that use generate() method to produce text outputs for different tasks: Text summarization; Image captioning; Audio transcription; Note that the inputs …

DA: 31 PA: 63 MOZ Rank: 9

How to generate text: using different decoding methods for …
huggingface.co:443

https://huggingface.co:443/blog/how-to-generate

WebMar 1, 2020 · generate has evolved into a highly composable method, with flags to manipulate the resulting text in many directions that were not covered in this blog post. Here are a few helpful pages to guide you: How to parameterize generate. How to stream the output. Full list of decoding options. generate API reference. LLM score leaderboard

DA: 8 PA: 49 MOZ Rank: 17

Utilities for Generation - Hugging Face
huggingface.co:443

https://huggingface.co:443/docs/transformers/internal/generation_utils

Webfrom transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") inputs = tokenizer("Hello, my dog is cute and ", return_tensors= "pt") generation_output = model.generate(**inputs, return_dict_in_generate= True, output ...

DA: 51 PA: 3 MOZ Rank: 82

Generation with LLMs - Hugging Face
huggingface.co:443

https://huggingface.co:443/docs/transformers/main/llm_tutorial

WebAdvanced generate usage. Guide on how to control different generation methods, how to set up the generation configuration file, and how to stream the output; Guide on the prompt template for chat LLMs; Guide on to get the most of prompt design; API reference on GenerationConfig, generate(), and generate-related classes. Most of the classes ...

DA: 7 PA: 51 MOZ Rank: 39

What is Text Generation? - Hugging Face
huggingface.co:443

https://huggingface.co:443/tasks/text-generation

WebText Generation. Generating text is the task of generating new text given another text. These models can, for example, fill in incomplete text or paraphrase. Inputs. Once upon a time, Text Generation Model. Output. Once upon a time, we knew that our ancestors were on the verge of extinction.

DA: 45 PA: 72 MOZ Rank: 93

blog/how-to-generate.md at main · huggingface/blog · GitHub
github.com:443

https://github.com:443/huggingface/blog/blob/main/how-to-generate.md

Webhow-to-generate.md. History. Preview. 639 lines (461 loc) · 24.9 KB. How to generate text: using different decoding methods for language generation with Transformers. Note: Edited on July 2023 with up-to-date references and examples. Introduction.

DA: 70 PA: 69 MOZ Rank: 32

Hugging Face – The AI community building the future.
huggingface.co:443

https://huggingface.co:443/

WebApr 16, 2015 · NEW. Try Cohere Command R+ on HuggingChat. The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications. Trending on this week. Models. mistral-community/Mixtral-8x22B-v0.1. Updated 5 days ago • 28.7k • 554. CohereForAI/c4ai-command-r-plus.

DA: 47 PA: 50 MOZ Rank: 57

GitHub - huggingface/transformers: Transformers: State-of-the-art
github.com:443

https://github.com:443/huggingface/transformers

WebText generation with Mistral. Natural Language Inference with RoBERTa. Summarization with BART. Question answering with DistilBERT. Translation with T5. In Computer Vision: Image classification with ViT. Object Detection with DETR. Semantic Segmentation with SegFormer. Panoptic Segmentation with Mask2Former. Depth …

DA: 77 PA: 52 MOZ Rank: 71