Tau

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Average Model Cost: $0.0000

Number of Runs: 9,027

Models by this creator

splinter-base

splinter-base

tau

splinter-base is a question-answering model that is based on the open-source Hugging Face transformers library. It can answer questions given a context and a question.

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$-/run

7.5K

Huggingface

bart-base-sled

bart-base-sled

BART-SLED (SLiding-Encoder and Decoder, base-sized model) SLED models use pretrained, short-range encoder-decoder models, and apply them over long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder Model description This SLED model is based on the BART model, which is described in its model card. BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). When used as a BART-SLED model, it can be applied on long text tasks. Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. How to use To use the model, you first need to install py-sled in your environment (or clone the code from the official repository) For more installation instructions, see here. Once installed, SLED is fully compatible with HuggingFace's AutoClasses (AutoTokenizer, AutoConfig, AutoModel and AutoModelForCausalLM) and can be loaded using the from_pretrained methods Here is how to use this model in PyTorch: You can also replace SledModel by SledModelForConditionalGeneration for Seq2Seq generation In case you wish to apply SLED on a task containing a prefix (e.g. question) which should be given as a context to every chunk, you can pass the prefix_length tensor input as well (A LongTensor in the length of the batch size). BibTeX entry and citation info Please cite both the SLED paper and the BART paper by Lewis et al

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$-/run

157

Huggingface

bart-base-sled-govreport

bart-base-sled-govreport

BART-SLED (SLiding-Encoder and Decoder, base-sized model) SLED models use pretrained, short-range encoder-decoder models, and apply them over long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder Model description This SLED model is based on the BART model, which is described in its model card. BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). When used as a BART-SLED model, it can be applied on long text tasks. This model was finetuned on the GovReport Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. How to use To use the model, you first need to install py-sled in your environment (or clone the code from the official repository) For more installation instructions, see here. Once installed, SLED is fully compatible with HuggingFace's AutoClasses (AutoTokenizer, AutoConfig, AutoModel and AutoModelForCausalLM) and can be loaded using the from_pretrained methods Here is how to use this model in PyTorch: You can also replace SledModel by SledModelForConditionalGeneration for Seq2Seq generation In case you wish to apply SLED on a task containing a prefix (e.g. question) which should be given as a context to every chunk, you can pass the prefix_length tensor input as well (A LongTensor in the length of the batch size). BibTeX entry and citation info Please cite both the SLED paper and the BART paper by Lewis et al as well as GovReport by Huang et al

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$-/run

28

Huggingface

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