      <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="40" height="14" transform="translate(455.5 80.5)">tagger</text>
      <path fill="#ffe6cc" stroke="#d79b00" stroke-width="2" stroke-miterlimit="10" d="M519 58h103.3L639 88l-16.8 30H519l16.8-30z"/>
      <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="40" height="14" transform="translate(558.5 80.5)">parser</text>
      <path fill="#ffe6cc" stroke="#d79b00" stroke-width="2" stroke-miterlimit="10" d="M622 58h103.3L742 88l-16.8 30H622l16.8-30z"/>
      <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="20" height="14" transform="translate(671.5 80.5)">ner</text>
  </svg>
 
</s> remove     <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="40" height="14" transform="translate(558.5 80.5)">parser</text>
 </s> add     <text class="svg__pipeline__text-small" dy="0.75em" dx="8" width="40" height="14" transform="translate(558.5 80.5)">ner</text> </s> remove     <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="40" height="14" transform="translate(455.5 80.5)">tagger</text>
 </s> add     <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="40" height="14" transform="translate(455.5 80.5)">parser</text> </s> remove     <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="62" height="14" transform="translate(342.5 80.5)">tensorizer</text>
 </s> add     <text class="svg__pipeline__text-small" dy="0.75em" dx="8" width="62" height="14" transform="translate(342.5 80.5)">tagger</text> </s> remove     |  As the first component of the
    |  #[+a("/usage/processing-pipelines") processing pipeline], the
    |  tensorizer encodes a document's internal meaning representations as an
 </s> add     |  As the  #[+a("/usage/processing-pipelines") processing pipeline] is
    |  applied spaCy encodes a document's internal meaning representations as an </s> remove     nlp = English().from_disk('/model', disable=['tensorizer', 'ner'])
 </s> add     nlp = English().from_disk('/model', disable=['ner'])
    <text class="svg__pipeline__text-small" dy="0.75em" dx="8" width="20" height="14" transform="translate(671.5 80.5)">...</text>
      <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="40" height="14" transform="translate(455.5 80.5)">tagger</text>
      <path fill="#ffe6cc" stroke="#d79b00" stroke-width="2" stroke-miterlimit="10" d="M519 58h103.3L639 88l-16.8 30H519l16.8-30z"/>
      <text class="svg__pipeline__text-small" dy="0.75em" dx="-0.25em" width="40" height="14" transform="translate(558.5 80.5)">parser</text>
      <path fill="#ffe6cc" stroke="#d79b00" stroke-width="2" stroke-miterlimit="10" d="M622 58h103.3L742 88l-16.8 30H622l16.8-30z"/>
    <text class="svg__pipeline__text-small" dy="0.75em" dx="8" width="20" height="14" transform="translate(671.5 80.5)">...</text>
  </svg>
keep keep keep keep replace keep
 Don't document the tensorizer for now 
website/assets/img/pipeline.svg