From 71883b6ddcd14929217a0ddf4ad627468b9ab5a8 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 20 Dec 2019 19:40:23 +0100 Subject: [PATCH] update link in readme --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 1312fcc0ace..769b0499cb3 100644 --- a/README.md +++ b/README.md @@ -55,6 +55,7 @@ Choose the right framework for every part of a model's lifetime | [Online demo](#online-demo) | Experimenting with this repo’s text generation capabilities | | [Quick tour: Usage](#quick-tour) | Tokenizers & models usage: Bert and GPT-2 | | [Quick tour: TF 2.0 and PyTorch ](#Quick-tour-TF-20-training-and-PyTorch-interoperability) | Train a TF 2.0 model in 10 lines of code, load it in PyTorch | +| [Quick tour: pipelines](#quick-tour-of-pipelines) | Using Pipelines: Wrapper around tokenizer and models to use finetuned models | | [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation | | [Quick tour: Share your models ](#Quick-tour-of-model-sharing) | Upload and share your fine-tuned models with the community | | [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers to transformers | @@ -496,6 +497,7 @@ New in version `v2.3`: `Pipeline` are high-level objects which automatically han and outputting the result in a structured object. You can create `Pipeline` objects for the following down-stream tasks: + - `feature-extraction`: Generates a tensor representation for the input sequence - `ner`: Generates named entity mapping for each word in the input sequence. - `sentiment-analysis`: Gives the polarity (positive / negative) of the whole input sequence.