Merge branch 'upstream_master' into feature/weasel

2025-04-21 17:41:59 +03:00 · 2023-03-23 10:54:10 +01:00 · 2023-03-23 10:54:10 +01:00 · 7fa617f5a6
commit 7fa617f5a6
parent b10c37f0cf 28de85737f
32 changed files with 968 additions and 219 deletions
--- a/.github/azure-steps.yml
+++ b/.github/azure-steps.yml
@ -57,51 +57,51 @@ steps:
      python -m spacy download ca_core_news_md
      python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
    displayName: 'Test download CLI'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      python -W error -m spacy info ca_core_news_sm | grep -q download_url
    displayName: 'Test download_url in info CLI'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')"
    displayName: 'Test no warnings on load (#11713)'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json .
    displayName: 'Test convert CLI'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      python -m spacy init config -p ner -l ca ner.cfg
      python -m spacy debug config ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy
    displayName: 'Test debug config CLI'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      # will have errors due to sparse data, check for summary in output
      python -m spacy debug data ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy | grep -q Summary
    displayName: 'Test debug data CLI'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      python -m spacy train ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy --training.max_steps 10 --gpu-id -1
    displayName: 'Test train CLI'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
      PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
    displayName: 'Test assemble CLI'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
      python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
    displayName: 'Test assemble CLI vectors warning'
-    condition: eq(variables['python_version'], '3.8')
+    condition: eq(variables['python_version'], '3.9')

  - script: |
      python -m pip install -U -r requirements.txt
@ -116,9 +116,3 @@ steps:
      python -m pytest --pyargs spacy
    displayName: "Run CPU tests with thinc-apple-ops"
    condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.11'))
-
-  - script: |
-      python .github/validate_universe_json.py website/meta/universe.json
-    displayName: 'Test website/meta/universe.json'
-    condition: eq(variables['python_version'], '3.8')
-
--- a/.github/workflows/autoblack.yml
+++ b/.github/workflows/autoblack.yml
@ -1,45 +0,0 @@
-# GitHub Action that uses Black to reformat all Python code and submits a PR
-# in regular intervals. Inspired by: https://github.com/cclauss/autoblack
-
-name: autoblack
-on:
-  workflow_dispatch:  # allow manual trigger
-  schedule:
-    - cron: '0 8 * * 5'  # every Friday at 8am UTC
-
-jobs:
-  autoblack:
-    if: github.repository_owner == 'explosion'
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-        with:
-            ref: ${{ github.head_ref }}
-      - uses: actions/setup-python@v4
-      - run: pip install black -c requirements.txt
-      - name: Auto-format code if needed
-        run: black spacy
-      # We can't run black --check here because that returns a non-zero excit
-      # code and makes GitHub think the action failed
-      - name: Check for modified files
-        id: git-check
-        run: echo modified=$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi) >> $GITHUB_OUTPUT
-
-      - name: Create Pull Request
-        if: steps.git-check.outputs.modified == 'true'
-        uses: peter-evans/create-pull-request@v4
-        with:
-            title: Auto-format code with black
-            labels: meta
-            commit-message: Auto-format code with black
-            committer: GitHub <noreply@github.com>
-            author: explosion-bot <explosion-bot@users.noreply.github.com>
-            body: _This PR is auto-generated._
-            branch: autoblack
-            delete-branch: true
-            draft: false
-      - name: Check outputs
-        if: steps.git-check.outputs.modified == 'true'
-        run: |
-          echo "Pull Request Number - ${{ steps.cpr.outputs.pull-request-number }}"
-          echo "Pull Request URL - ${{ steps.cpr.outputs.pull-request-url }}"
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@ -0,0 +1,172 @@
+name: tests
+
+on:
+  push:
+    branches-ignore:
+      - "spacy.io"
+      - "nightly.spacy.io"
+      - "v2.spacy.io"
+    paths-ignore:
+      - "*.md"
+      - "*.mdx"
+      - "website/**"
+      - ".github/workflows/**"
+  pull_request:
+    types: [opened, synchronize, reopened, edited]
+    paths-ignore:
+      - "*.md"
+      - "*.mdx"
+      - "website/**"
+
+jobs:
+  validate:
+    name: Validate
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repo
+        uses: actions/checkout@v3
+
+      - name: Configure Python version
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.7"
+          architecture: x64
+
+      - name: black
+        run: |
+          python -m pip install black -c requirements.txt
+          python -m black spacy --check
+      - name: flake8
+        run: |
+          python -m pip install flake8==5.0.4
+          python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
+  tests:
+    name: Test
+    needs: Validate
+    strategy:
+      fail-fast: true
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python_version: ["3.11"]
+        include:
+          - os: ubuntu-20.04
+            python_version: "3.6"
+          - os: windows-latest
+            python_version: "3.7"
+          - os: macos-latest
+            python_version: "3.8"
+          - os: ubuntu-latest
+            python_version: "3.9"
+          - os: windows-latest
+            python_version: "3.10"
+
+    runs-on: ${{ matrix.os }}
+
+    steps:
+      - name: Check out repo
+        uses: actions/checkout@v3
+
+      - name: Configure Python version
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python_version }}
+          architecture: x64
+
+      - name: Install dependencies
+        run: |
+          python -m pip install -U build pip setuptools
+          python -m pip install -U -r requirements.txt
+
+      - name: Build sdist
+        run: |
+          python -m build --sdist
+
+      - name: Run mypy
+        run: |
+          python -m mypy spacy
+        if: matrix.python_version != '3.6'
+
+      - name: Delete source directory and .egg-info
+        run: |
+          rm -rf spacy *.egg-info
+        shell: bash
+
+      - name: Uninstall all packages
+        run: |
+          python -m pip freeze
+          python -m pip freeze --exclude pywin32 > installed.txt
+          python -m pip uninstall -y -r installed.txt
+
+      - name: Install from sdist
+        run: |
+          SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
+          SPACY_NUM_BUILD_JOBS=2 python -m pip install dist/$SDIST
+        shell: bash
+
+      - name: Test import
+        run: python -W error -c "import spacy"
+
+      - name: "Test download CLI"
+        run: |
+          python -m spacy download ca_core_news_sm
+          python -m spacy download ca_core_news_md
+          python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
+        if: matrix.python_version == '3.9'
+
+      - name: "Test download_url in info CLI"
+        run: |
+          python -W error -m spacy info ca_core_news_sm | grep -q download_url
+        if: matrix.python_version == '3.9'
+
+      - name: "Test no warnings on load (#11713)"
+        run: |
+          python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')"
+        if: matrix.python_version == '3.9'
+
+      - name: "Test convert CLI"
+        run: |
+          python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json .
+        if: matrix.python_version == '3.9'
+
+      - name: "Test debug config CLI"
+        run: |
+          python -m spacy init config -p ner -l ca ner.cfg
+          python -m spacy debug config ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy
+        if: matrix.python_version == '3.9'
+
+      - name: "Test debug data CLI"
+        run: |
+          # will have errors due to sparse data, check for summary in output
+          python -m spacy debug data ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy | grep -q Summary
+        if: matrix.python_version == '3.9'
+
+      - name: "Test train CLI"
+        run: |
+          python -m spacy train ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy --training.max_steps 10 --gpu-id -1
+        if: matrix.python_version == '3.9'
+
+      - name: "Test assemble CLI"
+        run: |
+          python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
+          PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
+        if: matrix.python_version == '3.9'
+
+      - name: "Test assemble CLI vectors warning"
+        run: |
+          python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
+          python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
+        if: matrix.python_version == '3.9'
+
+      - name: "Install test requirements"
+        run: |
+          python -m pip install -U -r requirements.txt
+
+      - name: "Run CPU tests"
+        run: |
+          python -m pytest --pyargs spacy -W error
+
+      - name: "Run CPU tests with thinc-apple-ops"
+        run: |
+          python -m pip install 'spacy[apple]'
+          python -m pytest --pyargs spacy
+        if: startsWith(matrix.os, 'macos') && matrix.python_version == '3.11'
--- a/.github/workflows/universe_validation.yml
+++ b/.github/workflows/universe_validation.yml
@ -0,0 +1,32 @@
+name: universe validation
+
+on:
+  push:
+    branches-ignore:
+      - "spacy.io"
+      - "nightly.spacy.io"
+      - "v2.spacy.io"
+    paths:
+      - "website/meta/universe.json"
+  pull_request:
+    types: [opened, synchronize, reopened, edited]
+    paths:
+      - "website/meta/universe.json"
+
+jobs:
+  validate:
+    name: Validate
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repo
+        uses: actions/checkout@v3
+
+      - name: Configure Python version
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.7"
+          architecture: x64
+
+      - name: Validate website/meta/universe.json
+        run: |
+          python .github/validate_universe_json.py website/meta/universe.json
--- a/README.md
+++ b/README.md
@ -16,6 +16,9 @@ production-ready [**training system**](https://spacy.io/usage/training) and easy
 model packaging, deployment and workflow management. spaCy is commercial
 open-source software, released under the [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).

+💥 **We'd love to hear more about your experience with spaCy!**
+[Fill out our survey here.](https://form.typeform.com/to/aMel9q9f)
+
 💫 **Version 3.5 out now!**
 [Check out the release notes here.](https://github.com/explosion/spaCy/releases)

--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@ -48,6 +48,9 @@ jobs:
          pip install flake8==5.0.4
          python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
        displayName: "flake8"
+      - script: |
+          python .github/validate_universe_json.py website/meta/universe.json
+        displayName: 'Validate website/meta/universe.json'

  - job: "Test"
    dependsOn: "Validate"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -5,7 +5,7 @@ requires = [
    "cymem>=2.0.2,<2.1.0",
    "preshed>=3.0.2,<3.1.0",
    "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.1.0,<8.2.0",
+    "thinc>=8.1.8,<8.2.0",
    "numpy>=1.15.0",
 ]
 build-backend = "setuptools.build_meta"
--- a/requirements.txt
+++ b/requirements.txt
@ -3,7 +3,7 @@ spacy-legacy>=3.0.11,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.1.0,<8.2.0
+thinc>=8.1.8,<8.2.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.2.0
--- a/setup.cfg
+++ b/setup.cfg
@ -39,7 +39,7 @@ setup_requires =
    cymem>=2.0.2,<2.1.0
    preshed>=3.0.2,<3.1.0
    murmurhash>=0.28.0,<1.1.0
-    thinc>=8.1.0,<8.2.0
+    thinc>=8.1.8,<8.2.0
 install_requires =
    # Our libraries
    spacy-legacy>=3.0.11,<3.1.0
@ -47,7 +47,7 @@ install_requires =
    murmurhash>=0.28.0,<1.1.0
    cymem>=2.0.2,<2.1.0
    preshed>=3.0.2,<3.1.0
-    thinc>=8.1.0,<8.2.0
+    thinc>=8.1.8,<8.2.0
    wasabi>=0.9.1,<1.2.0
    srsly>=2.4.3,<3.0.0
    catalogue>=2.0.6,<2.1.0
--- a/spacy/cli/debug_data.py
+++ b/spacy/cli/debug_data.py
@ -7,6 +7,7 @@ import srsly
 from wasabi import Printer, MESSAGES, msg
 import typer
 import math
+import numpy

 from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
 from ._util import import_code, debug_cli, _format_number
@ -521,9 +522,13 @@ def debug_data(

    if "tagger" in factory_names:
        msg.divider("Part-of-speech Tagging")
-        label_list = [label for label in gold_train_data["tags"]]
-        model_labels = _get_labels_from_model(nlp, "tagger")
+        label_list, counts = zip(*gold_train_data["tags"].items())
        msg.info(f"{len(label_list)} label(s) in train data")
+        p = numpy.array(counts)
+        p = p / p.sum()
+        norm_entropy = (-p * numpy.log2(p)).sum() / numpy.log2(len(label_list))
+        msg.info(f"{norm_entropy} is the normalised label entropy")
+        model_labels = _get_labels_from_model(nlp, "tagger")
        labels = set(label_list)
        missing_labels = model_labels - labels
        if missing_labels:
--- a/spacy/cli/find_threshold.py
+++ b/spacy/cli/find_threshold.py
@ -35,7 +35,7 @@ def find_threshold_cli(
    code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
    use_gpu: int = Opt(_DEFAULTS["use_gpu"], "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
    gold_preproc: bool = Opt(_DEFAULTS["gold_preproc"], "--gold-preproc", "-G", help="Use gold preprocessing"),
-    verbose: bool = Opt(False, "--silent", "-V", "-VV", help="Display more information for debugging purposes"),
+    verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
    # fmt: on
 ):
    """
--- a/spacy/cli/templates/quickstart_training.jinja
+++ b/spacy/cli/templates/quickstart_training.jinja
@ -3,7 +3,7 @@ the docs and the init config command. It encodes various best practices and
 can help generate the best possible configuration, given a user's requirements. #}
 {%- set use_transformer = hardware != "cpu" and transformer_data -%}
 {%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
-{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "trainable_lemmatizer"] -%}
+{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "spancat_singlelabel", "trainable_lemmatizer"] -%}
 [paths]
 train = null
 dev = null
@ -24,8 +24,11 @@ gpu_allocator = null
 lang = "{{ lang }}"
 {%- set has_textcat = ("textcat" in components or "textcat_multilabel" in components) -%}
 {%- set with_accuracy = optimize == "accuracy" -%}
-{%- set has_accurate_textcat = has_textcat and with_accuracy -%}
-{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "trainable_lemmatizer" in components or "entity_linker" in components or has_accurate_textcat) -%}
+{# The BOW textcat doesn't need a source of features, so it can omit the
+tok2vec/transformer. #}
+{%- set with_accuracy_or_transformer = (use_transformer or with_accuracy) -%}
+{%- set textcat_needs_features = has_textcat and with_accuracy_or_transformer -%}
+{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "spancat_singlelabel" in components or "trainable_lemmatizer" in components or "entity_linker" in components or textcat_needs_features) -%}
 {%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components -%}
 {%- else -%}
 {%- set full_pipeline = components -%}
@ -156,6 +159,36 @@ grad_factor = 1.0
 sizes = [1,2,3]
 {% endif -%}

+{% if "spancat_singlelabel" in components %}
+[components.spancat_singlelabel]
+factory = "spancat_singlelabel"
+negative_weight = 1.0
+allow_overlap = true
+scorer = {"@scorers":"spacy.spancat_scorer.v1"}
+spans_key = "sc"
+
+[components.spancat_singlelabel.model]
+@architectures = "spacy.SpanCategorizer.v1"
+
+[components.spancat_singlelabel.model.reducer]
+@layers = "spacy.mean_max_reducer.v1"
+hidden_size = 128
+
+[components.spancat_singlelabel.model.scorer]
+@layers = "Softmax.v2"
+
+[components.spancat_singlelabel.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+
+[components.spancat_singlelabel.model.tok2vec.pooling]
+@layers = "reduce_mean.v1"
+
+[components.spancat_singlelabel.suggester]
+@misc = "spacy.ngram_suggester.v1"
+sizes = [1,2,3]
+{% endif %}
+
 {% if "trainable_lemmatizer" in components -%}
 [components.trainable_lemmatizer]
 factory = "trainable_lemmatizer"
@ -221,10 +254,16 @@ no_output_layer = false

 {% else -%}
 [components.textcat.model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatCNN.v2"
 exclusive_classes = true
-ngram_size = 1
-no_output_layer = false
+nO = null
+
+[components.textcat.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+
+[components.textcat.model.tok2vec.pooling]
+@layers = "reduce_mean.v1"
 {%- endif %}
 {%- endif %}

@ -252,10 +291,16 @@ no_output_layer = false

 {% else -%}
 [components.textcat_multilabel.model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatCNN.v2"
 exclusive_classes = false
-ngram_size = 1
-no_output_layer = false
+nO = null
+
+[components.textcat_multilabel.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+
+[components.textcat_multilabel.model.tok2vec.pooling]
+@layers = "reduce_mean.v1"
 {%- endif %}
 {%- endif %}

@ -286,6 +331,7 @@ maxout_pieces = 3
 {% if "morphologizer" in components %}
 [components.morphologizer]
 factory = "morphologizer"
+label_smoothing = 0.05

 [components.morphologizer.model]
@architectures = "spacy.Tagger.v2"
@ -299,6 +345,7 @@ width = ${components.tok2vec.model.encode.width}
 {% if "tagger" in components %}
 [components.tagger]
 factory = "tagger"
+label_smoothing = 0.05

 [components.tagger.model]
@architectures = "spacy.Tagger.v2"
@ -374,6 +421,33 @@ width = ${components.tok2vec.model.encode.width}
 sizes = [1,2,3]
 {% endif %}

+{% if "spancat_singlelabel" in components %}
+[components.spancat_singlelabel]
+factory = "spancat_singlelabel"
+negative_weight = 1.0
+allow_overlap = true
+scorer = {"@scorers":"spacy.spancat_scorer.v1"}
+spans_key = "sc"
+
+[components.spancat_singlelabel.model]
+@architectures = "spacy.SpanCategorizer.v1"
+
+[components.spancat_singlelabel.model.reducer]
+@layers = "spacy.mean_max_reducer.v1"
+hidden_size = 128
+
+[components.spancat_singlelabel.model.scorer]
+@layers = "Softmax.v2"
+
+[components.spancat_singlelabel.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+
+[components.spancat_singlelabel.suggester]
+@misc = "spacy.ngram_suggester.v1"
+sizes = [1,2,3]
+{% endif %}
+
 {% if "trainable_lemmatizer" in components -%}
 [components.trainable_lemmatizer]
 factory = "trainable_lemmatizer"
--- a/spacy/errors.py
+++ b/spacy/errors.py
@ -966,6 +966,7 @@ class Errors(metaclass=ErrorsWithCodes):
             "with `displacy.serve(doc, port=port)`")
    E1050 = ("Port {port} is already in use. Please specify an available port with `displacy.serve(doc, port=port)` "
             "or use `auto_select_port=True` to pick an available port automatically.")
+    E1051 = ("'allow_overlap' can only be False when max_positive is 1, but found 'max_positive': {max_positive}.")


 # Deprecated model shortcuts, only used in errors and warnings
--- a/spacy/pipeline/entity_linker.py
+++ b/spacy/pipeline/entity_linker.py
@ -474,18 +474,24 @@ class EntityLinker(TrainablePipe):

                # Looping through each entity in batch (TODO: rewrite)
                for j, ent in enumerate(ent_batch):
-                    sent_index = sentences.index(ent.sent)
-                    assert sent_index >= 0
+                    assert hasattr(ent, "sents")
+                    sents = list(ent.sents)
+                    sent_indices = (
+                        sentences.index(sents[0]),
+                        sentences.index(sents[-1]),
+                    )
+                    assert sent_indices[1] >= sent_indices[0] >= 0

                    if self.incl_context:
                        # get n_neighbour sentences, clipped to the length of the document
-                        start_sentence = max(0, sent_index - self.n_sents)
+                        start_sentence = max(0, sent_indices[0] - self.n_sents)
                        end_sentence = min(
-                            len(sentences) - 1, sent_index + self.n_sents
+                            len(sentences) - 1, sent_indices[1] + self.n_sents
                        )
                        start_token = sentences[start_sentence].start
                        end_token = sentences[end_sentence].end
                        sent_doc = doc[start_token:end_token].as_doc()
+
                        # currently, the context is the same for each entity in a sentence (should be refined)
                        sentence_encoding = self.model.predict([sent_doc])[0]
                        sentence_encoding_t = sentence_encoding.T
--- a/spacy/pipeline/morphologizer.pyx
+++ b/spacy/pipeline/morphologizer.pyx
@ -52,7 +52,8 @@ DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
    "morphologizer",
    assigns=["token.morph", "token.pos"],
-    default_config={"model": DEFAULT_MORPH_MODEL, "overwrite": True, "extend": False, "scorer": {"@scorers": "spacy.morphologizer_scorer.v1"}},
+    default_config={"model": DEFAULT_MORPH_MODEL, "overwrite": True, "extend": False,
+                    "scorer": {"@scorers": "spacy.morphologizer_scorer.v1"}, "label_smoothing": 0.0},
    default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
 )
 def make_morphologizer(
@ -61,9 +62,10 @@ def make_morphologizer(
    name: str,
    overwrite: bool,
    extend: bool,
+    label_smoothing: float,
    scorer: Optional[Callable],
 ):
-    return Morphologizer(nlp.vocab, model, name, overwrite=overwrite, extend=extend, scorer=scorer)
+    return Morphologizer(nlp.vocab, model, name, overwrite=overwrite, extend=extend, label_smoothing=label_smoothing, scorer=scorer)


 def morphologizer_score(examples, **kwargs):
@ -94,6 +96,7 @@ class Morphologizer(Tagger):
        *,
        overwrite: bool = BACKWARD_OVERWRITE,
        extend: bool = BACKWARD_EXTEND,
+        label_smoothing: float = 0.0,
        scorer: Optional[Callable] = morphologizer_score,
    ):
        """Initialize a morphologizer.
@ -121,6 +124,7 @@ class Morphologizer(Tagger):
            "labels_pos": {},
            "overwrite": overwrite,
            "extend": extend,
+            "label_smoothing": label_smoothing,
        }
        self.cfg = dict(sorted(cfg.items()))
        self.scorer = scorer
@ -270,7 +274,8 @@ class Morphologizer(Tagger):
        DOCS: https://spacy.io/api/morphologizer#get_loss
        """
        validate_examples(examples, "Morphologizer.get_loss")
-        loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
+        loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False,
+                                                    label_smoothing=self.cfg["label_smoothing"])
        truths = []
        for eg in examples:
            eg_truths = []
--- a/spacy/pipeline/spancat.py
+++ b/spacy/pipeline/spancat.py
@ -1,4 +1,5 @@
-from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any
+from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast, Union
+from dataclasses import dataclass
 from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
 from thinc.api import Optimizer
 from thinc.types import Ragged, Ints2d, Floats2d
@ -43,7 +44,36 @@ maxout_pieces = 3
 depth = 4
 """

+spancat_singlelabel_default_config = """
+[model]
+@architectures = "spacy.SpanCategorizer.v1"
+scorer = {"@layers": "Softmax.v2"}
+
+[model.reducer]
+@layers = spacy.mean_max_reducer.v1
+hidden_size = 128
+
+[model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+[model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v1"
+width = 96
+rows = [5000, 1000, 2500, 1000]
+attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
+include_static_vectors = false
+
+[model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = ${model.tok2vec.embed.width}
+window_size = 1
+maxout_pieces = 3
+depth = 4
+"""
+
 DEFAULT_SPANCAT_MODEL = Config().from_str(spancat_default_config)["model"]
+DEFAULT_SPANCAT_SINGLELABEL_MODEL = Config().from_str(
+    spancat_singlelabel_default_config
+)["model"]


@runtime_checkable
@ -119,10 +149,14 @@ def make_spancat(
    threshold: float,
    max_positive: Optional[int],
 ) -> "SpanCategorizer":
-    """Create a SpanCategorizer component. The span categorizer consists of two
+    """Create a SpanCategorizer component and configure it for multi-label
+    classification to be able to assign multiple labels for each span.
+    The span categorizer consists of two
    parts: a suggester function that proposes candidate spans, and a labeller
    model that predicts one or more labels for each span.

+    name (str): The component instance name, used to add entries to the
+        losses during training.
    suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
        Spans are returned as a ragged array with two integer columns, for the
        start and end positions.
@ -144,12 +178,80 @@ def make_spancat(
    """
    return SpanCategorizer(
        nlp.vocab,
-        suggester=suggester,
        model=model,
-        spans_key=spans_key,
-        threshold=threshold,
-        max_positive=max_positive,
+        suggester=suggester,
        name=name,
+        spans_key=spans_key,
+        negative_weight=None,
+        allow_overlap=True,
+        max_positive=max_positive,
+        threshold=threshold,
+        scorer=scorer,
+        add_negative_label=False,
+    )
+
+
+@Language.factory(
+    "spancat_singlelabel",
+    assigns=["doc.spans"],
+    default_config={
+        "spans_key": "sc",
+        "model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
+        "negative_weight": 1.0,
+        "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
+        "scorer": {"@scorers": "spacy.spancat_scorer.v1"},
+        "allow_overlap": True,
+    },
+    default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
+)
+def make_spancat_singlelabel(
+    nlp: Language,
+    name: str,
+    suggester: Suggester,
+    model: Model[Tuple[List[Doc], Ragged], Floats2d],
+    spans_key: str,
+    negative_weight: float,
+    allow_overlap: bool,
+    scorer: Optional[Callable],
+) -> "SpanCategorizer":
+    """Create a SpanCategorizer component and configure it for multi-class
+    classification. With this configuration each span can get at most one
+    label. The span categorizer consists of two
+    parts: a suggester function that proposes candidate spans, and a labeller
+    model that predicts one or more labels for each span.
+
+    name (str): The component instance name, used to add entries to the
+        losses during training.
+    suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
+        Spans are returned as a ragged array with two integer columns, for the
+        start and end positions.
+    model (Model[Tuple[List[Doc], Ragged], Floats2d]): A model instance that
+        is given a list of documents and (start, end) indices representing
+        candidate span offsets. The model predicts a probability for each category
+        for each span.
+    spans_key (str): Key of the doc.spans dict to save the spans under. During
+        initialization and training, the component will look for spans on the
+        reference document under the same key.
+    scorer (Optional[Callable]): The scoring method. Defaults to
+        Scorer.score_spans for the Doc.spans[spans_key] with overlapping
+        spans allowed.
+    negative_weight (float): Multiplier for the loss terms.
+        Can be used to downweight the negative samples if there are too many.
+    allow_overlap (bool): If True the data is assumed to contain overlapping spans.
+        Otherwise it produces non-overlapping spans greedily prioritizing
+        higher assigned label scores.
+    """
+    return SpanCategorizer(
+        nlp.vocab,
+        model=model,
+        suggester=suggester,
+        name=name,
+        spans_key=spans_key,
+        negative_weight=negative_weight,
+        allow_overlap=allow_overlap,
+        max_positive=1,
+        add_negative_label=True,
+        threshold=None,
        scorer=scorer,
    )

@ -172,6 +274,27 @@ def make_spancat_scorer():
    return spancat_score


+@dataclass
+class _Intervals:
+    """
+    Helper class to avoid storing overlapping spans.
+    """
+
+    def __init__(self):
+        self.ranges = set()
+
+    def add(self, i, j):
+        for e in range(i, j):
+            self.ranges.add(e)
+
+    def __contains__(self, rang):
+        i, j = rang
+        for e in range(i, j):
+            if e in self.ranges:
+                return True
+        return False
+
+
 class SpanCategorizer(TrainablePipe):
    """Pipeline component to label spans of text.

@ -185,25 +308,43 @@ class SpanCategorizer(TrainablePipe):
        suggester: Suggester,
        name: str = "spancat",
        *,
+        add_negative_label: bool = False,
        spans_key: str = "spans",
-        threshold: float = 0.5,
+        negative_weight: Optional[float] = 1.0,
+        allow_overlap: Optional[bool] = True,
        max_positive: Optional[int] = None,
+        threshold: Optional[float] = 0.5,
        scorer: Optional[Callable] = spancat_score,
    ) -> None:
-        """Initialize the span categorizer.
+        """Initialize the multi-label or multi-class span categorizer.
+
        vocab (Vocab): The shared vocabulary.
        model (thinc.api.Model): The Thinc Model powering the pipeline component.
+            For multi-class classification (single label per span) we recommend
+            using a Softmax classifier as a the final layer, while for multi-label
+            classification (multiple possible labels per span) we recommend Logistic.
+        suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
+            Spans are returned as a ragged array with two integer columns, for the
+            start and end positions.
        name (str): The component instance name, used to add entries to the
            losses during training.
        spans_key (str): Key of the Doc.spans dict to save the spans under.
            During initialization and training, the component will look for
            spans on the reference document under the same key. Defaults to
            `"spans"`.
-        threshold (float): Minimum probability to consider a prediction
-            positive. Spans with a positive prediction will be saved on the Doc.
-            Defaults to 0.5.
+        add_negative_label (bool): Learn to predict a special 'negative_label'
+            when a Span is not annotated.
+        threshold (Optional[float]): Minimum probability to consider a prediction
+            positive. Defaults to 0.5. Spans with a positive prediction will be saved
+            on the Doc.
        max_positive (Optional[int]): Maximum number of labels to consider
            positive per span. Defaults to None, indicating no limit.
+        negative_weight (float): Multiplier for the loss terms.
+            Can be used to downweight the negative samples if there are too many
+            when add_negative_label is True. Otherwise its unused.
+        allow_overlap (bool): If True the data is assumed to contain overlapping spans.
+            Otherwise it produces non-overlapping spans greedily prioritizing
+            higher assigned label scores. Only used when max_positive is 1.
        scorer (Optional[Callable]): The scoring method. Defaults to
            Scorer.score_spans for the Doc.spans[spans_key] with overlapping
            spans allowed.
@ -215,12 +356,17 @@ class SpanCategorizer(TrainablePipe):
            "spans_key": spans_key,
            "threshold": threshold,
            "max_positive": max_positive,
+            "negative_weight": negative_weight,
+            "allow_overlap": allow_overlap,
        }
        self.vocab = vocab
        self.suggester = suggester
        self.model = model
        self.name = name
        self.scorer = scorer
+        self.add_negative_label = add_negative_label
+        if not allow_overlap and max_positive is not None and max_positive > 1:
+            raise ValueError(Errors.E1051.format(max_positive=max_positive))

    @property
    def key(self) -> str:
@ -230,6 +376,21 @@ class SpanCategorizer(TrainablePipe):
        """
        return str(self.cfg["spans_key"])

+    def _allow_extra_label(self) -> None:
+        """Raise an error if the component can not add any more labels."""
+        nO = None
+        if self.model.has_dim("nO"):
+            nO = self.model.get_dim("nO")
+        elif self.model.has_ref("output_layer") and self.model.get_ref(
+            "output_layer"
+        ).has_dim("nO"):
+            nO = self.model.get_ref("output_layer").get_dim("nO")
+        if nO is not None and nO == self._n_labels:
+            if not self.is_resizable:
+                raise ValueError(
+                    Errors.E922.format(name=self.name, nO=self.model.get_dim("nO"))
+                )
+
    def add_label(self, label: str) -> int:
        """Add a new label to the pipe.

@ -263,6 +424,27 @@ class SpanCategorizer(TrainablePipe):
        """
        return list(self.labels)

+    @property
+    def _label_map(self) -> Dict[str, int]:
+        """RETURNS (Dict[str, int]): The label map."""
+        return {label: i for i, label in enumerate(self.labels)}
+
+    @property
+    def _n_labels(self) -> int:
+        """RETURNS (int): Number of labels."""
+        if self.add_negative_label:
+            return len(self.labels) + 1
+        else:
+            return len(self.labels)
+
+    @property
+    def _negative_label_i(self) -> Union[int, None]:
+        """RETURNS (Union[int, None]): Index of the negative label."""
+        if self.add_negative_label:
+            return len(self.label_data)
+        else:
+            return None
+
    def predict(self, docs: Iterable[Doc]):
        """Apply the pipeline's model to a batch of docs, without modifying them.

@ -304,14 +486,24 @@ class SpanCategorizer(TrainablePipe):

        DOCS: https://spacy.io/api/spancategorizer#set_annotations
        """
-        labels = self.labels
        indices, scores = indices_scores
        offset = 0
        for i, doc in enumerate(docs):
            indices_i = indices[i].dataXd
-            doc.spans[self.key] = self._make_span_group(
-                doc, indices_i, scores[offset : offset + indices.lengths[i]], labels  # type: ignore[arg-type]
-            )
+            allow_overlap = cast(bool, self.cfg["allow_overlap"])
+            if self.cfg["max_positive"] == 1:
+                doc.spans[self.key] = self._make_span_group_singlelabel(
+                    doc,
+                    indices_i,
+                    scores[offset : offset + indices.lengths[i]],
+                    allow_overlap,
+                )
+            else:
+                doc.spans[self.key] = self._make_span_group_multilabel(
+                    doc,
+                    indices_i,
+                    scores[offset : offset + indices.lengths[i]],
+                )
            offset += indices.lengths[i]

    def update(
@ -371,9 +563,11 @@ class SpanCategorizer(TrainablePipe):
        spans = Ragged(
            self.model.ops.to_numpy(spans.data), self.model.ops.to_numpy(spans.lengths)
        )
-        label_map = {label: i for i, label in enumerate(self.labels)}
        target = numpy.zeros(scores.shape, dtype=scores.dtype)
+        if self.add_negative_label:
+            negative_spans = numpy.ones((scores.shape[0]))
        offset = 0
+        label_map = self._label_map
        for i, eg in enumerate(examples):
            # Map (start, end) offset of spans to the row in the d_scores array,
            # so that we can adjust the gradient for predictions that were
@ -390,10 +584,16 @@ class SpanCategorizer(TrainablePipe):
                    row = spans_index[key]
                    k = label_map[gold_span.label_]
                    target[row, k] = 1.0
+                    if self.add_negative_label:
+                        # delete negative label target.
+                        negative_spans[row] = 0.0
            # The target is a flat array for all docs. Track the position
            # we're at within the flat array.
            offset += spans.lengths[i]
        target = self.model.ops.asarray(target, dtype="f")  # type: ignore
+        if self.add_negative_label:
+            negative_samples = numpy.nonzero(negative_spans)[0]
+            target[negative_samples, self._negative_label_i] = 1.0  # type: ignore
        # The target will have the values 0 (for untrue predictions) or 1
        # (for true predictions).
        # The scores should be in the range [0, 1].
@ -402,6 +602,10 @@ class SpanCategorizer(TrainablePipe):
        # If the prediction is 0.9 and it's false, the gradient will be
        # 0.9 (0.9 - 0.0)
        d_scores = scores - target
+        if self.add_negative_label:
+            neg_weight = cast(float, self.cfg["negative_weight"])
+            if neg_weight != 1.0:
+                d_scores[negative_samples] *= neg_weight
        loss = float((d_scores**2).sum())
        return loss, d_scores

@ -438,7 +642,7 @@ class SpanCategorizer(TrainablePipe):
        if subbatch:
            docs = [eg.x for eg in subbatch]
            spans = build_ngram_suggester(sizes=[1])(docs)
-            Y = self.model.ops.alloc2f(spans.dataXd.shape[0], len(self.labels))
+            Y = self.model.ops.alloc2f(spans.dataXd.shape[0], self._n_labels)
            self.model.initialize(X=(docs, spans), Y=Y)
        else:
            self.model.initialize()
@ -452,31 +656,96 @@ class SpanCategorizer(TrainablePipe):
            eg.reference.spans.get(self.key, []), allow_overlap=True
        )

-    def _make_span_group(
-        self, doc: Doc, indices: Ints2d, scores: Floats2d, labels: List[str]
+    def _make_span_group_multilabel(
+        self,
+        doc: Doc,
+        indices: Ints2d,
+        scores: Floats2d,
    ) -> SpanGroup:
+        """Find the top-k labels for each span (k=max_positive)."""
        spans = SpanGroup(doc, name=self.key)
-        max_positive = self.cfg["max_positive"]
+        if scores.size == 0:
+            return spans
+        scores = self.model.ops.to_numpy(scores)
+        indices = self.model.ops.to_numpy(indices)
        threshold = self.cfg["threshold"]
+        max_positive = self.cfg["max_positive"]

        keeps = scores >= threshold
-        ranked = (scores * -1).argsort()  # type: ignore
        if max_positive is not None:
            assert isinstance(max_positive, int)
+            if self.add_negative_label:
+                negative_scores = numpy.copy(scores[:, self._negative_label_i])
+                scores[:, self._negative_label_i] = -numpy.inf
+                ranked = (scores * -1).argsort()  # type: ignore
+                scores[:, self._negative_label_i] = negative_scores
+            else:
+                ranked = (scores * -1).argsort()  # type: ignore
            span_filter = ranked[:, max_positive:]
            for i, row in enumerate(span_filter):
                keeps[i, row] = False
-        spans.attrs["scores"] = scores[keeps].flatten()
-
-        indices = self.model.ops.to_numpy(indices)
-        keeps = self.model.ops.to_numpy(keeps)

+        attrs_scores = []
        for i in range(indices.shape[0]):
            start = indices[i, 0]
            end = indices[i, 1]
-
            for j, keep in enumerate(keeps[i]):
                if keep:
-                    spans.append(Span(doc, start, end, label=labels[j]))
+                    if j != self._negative_label_i:
+                        spans.append(Span(doc, start, end, label=self.labels[j]))
+                        attrs_scores.append(scores[i, j])
+        spans.attrs["scores"] = numpy.array(attrs_scores)
+        return spans
+
+    def _make_span_group_singlelabel(
+        self,
+        doc: Doc,
+        indices: Ints2d,
+        scores: Floats2d,
+        allow_overlap: bool = True,
+    ) -> SpanGroup:
+        """Find the argmax label for each span."""
+        # Handle cases when there are zero suggestions
+        if scores.size == 0:
+            return SpanGroup(doc, name=self.key)
+        scores = self.model.ops.to_numpy(scores)
+        indices = self.model.ops.to_numpy(indices)
+        predicted = scores.argmax(axis=1)
+        argmax_scores = numpy.take_along_axis(
+            scores, numpy.expand_dims(predicted, 1), axis=1
+        )
+        keeps = numpy.ones(predicted.shape, dtype=bool)
+        # Remove samples where the negative label is the argmax.
+        if self.add_negative_label:
+            keeps = numpy.logical_and(keeps, predicted != self._negative_label_i)
+        # Filter samples according to threshold.
+        threshold = self.cfg["threshold"]
+        if threshold is not None:
+            keeps = numpy.logical_and(keeps, (argmax_scores >= threshold).squeeze())
+        # Sort spans according to argmax probability
+        if not allow_overlap:
+            # Get the probabilities
+            sort_idx = (argmax_scores.squeeze() * -1).argsort()
+            predicted = predicted[sort_idx]
+            indices = indices[sort_idx]
+            keeps = keeps[sort_idx]
+        seen = _Intervals()
+        spans = SpanGroup(doc, name=self.key)
+        attrs_scores = []
+        for i in range(indices.shape[0]):
+            if not keeps[i]:
+                continue
+
+            label = predicted[i]
+            start = indices[i, 0]
+            end = indices[i, 1]
+
+            if not allow_overlap:
+                if (start, end) in seen:
+                    continue
+                else:
+                    seen.add(start, end)
+            attrs_scores.append(argmax_scores[i])
+            spans.append(Span(doc, start, end, label=self.labels[label]))

        return spans
--- a/spacy/pipeline/tagger.pyx
+++ b/spacy/pipeline/tagger.pyx
@ -45,7 +45,7 @@ DEFAULT_TAGGER_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
    "tagger",
    assigns=["token.tag"],
-    default_config={"model": DEFAULT_TAGGER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.tagger_scorer.v1"}, "neg_prefix": "!"},
+    default_config={"model": DEFAULT_TAGGER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.tagger_scorer.v1"}, "neg_prefix": "!", "label_smoothing": 0.0},
    default_score_weights={"tag_acc": 1.0},
 )
 def make_tagger(
@ -55,6 +55,7 @@ def make_tagger(
    overwrite: bool,
    scorer: Optional[Callable],
    neg_prefix: str,
+    label_smoothing: float,
 ):
    """Construct a part-of-speech tagger component.

@ -63,7 +64,7 @@ def make_tagger(
        in size, and be normalized as probabilities (all scores between 0 and 1,
        with the rows summing to 1).
    """
-    return Tagger(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer, neg_prefix=neg_prefix)
+    return Tagger(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer, neg_prefix=neg_prefix, label_smoothing=label_smoothing)


 def tagger_score(examples, **kwargs):
@ -89,6 +90,7 @@ class Tagger(TrainablePipe):
        overwrite=BACKWARD_OVERWRITE,
        scorer=tagger_score,
        neg_prefix="!",
+        label_smoothing=0.0,
    ):
        """Initialize a part-of-speech tagger.

@ -105,7 +107,7 @@ class Tagger(TrainablePipe):
        self.model = model
        self.name = name
        self._rehearsal_model = None
-        cfg = {"labels": [], "overwrite": overwrite, "neg_prefix": neg_prefix}
+        cfg = {"labels": [], "overwrite": overwrite, "neg_prefix": neg_prefix, "label_smoothing": label_smoothing}
        self.cfg = dict(sorted(cfg.items()))
        self.scorer = scorer

@ -256,7 +258,7 @@ class Tagger(TrainablePipe):
        DOCS: https://spacy.io/api/tagger#get_loss
        """
        validate_examples(examples, "Tagger.get_loss")
-        loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False, neg_prefix=self.cfg["neg_prefix"])
+        loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False, neg_prefix=self.cfg["neg_prefix"], label_smoothing=self.cfg["label_smoothing"])
        # Convert empty tag "" to missing value None so that both misaligned
        # tokens and tokens with missing annotation have the default missing
        # value None.
--- a/spacy/tests/doc/test_span.py
+++ b/spacy/tests/doc/test_span.py
@ -700,3 +700,19 @@ def test_span_group_copy(doc):
    assert len(doc.spans["test"]) == 3
    # check that the copy spans were not modified and this is an isolated doc
    assert len(doc_copy.spans["test"]) == 2
+
+
+def test_for_partial_ent_sents():
+    """Spans may be associated with multiple sentences. These .sents should always be complete, not partial, sentences,
+    which this tests for.
+    """
+    doc = Doc(
+        English().vocab,
+        words=["Mahler's", "Symphony", "No.", "8", "was", "beautiful."],
+        sent_starts=[1, 0, 0, 1, 0, 0],
+    )
+    doc.set_ents([Span(doc, 1, 4, "WORK")])
+    # The specified entity is associated with both sentences in this doc, so we expect all sentences in the doc to be
+    # equal to the sentences referenced in ent.sents.
+    for doc_sent, ent_sent in zip(doc.sents, doc.ents[0].sents):
+        assert doc_sent == ent_sent
--- a/spacy/tests/pipeline/test_entity_linker.py
+++ b/spacy/tests/pipeline/test_entity_linker.py
@ -1,9 +1,9 @@
-from typing import Callable, Iterable, Dict, Any
+from typing import Callable, Iterable, Dict, Any, Tuple

 import pytest
 from numpy.testing import assert_equal

-from spacy import registry, util
+from spacy import registry, util, Language
 from spacy.attrs import ENT_KB_ID
 from spacy.compat import pickle
 from spacy.kb import Candidate, InMemoryLookupKB, get_candidates, KnowledgeBase
@ -108,18 +108,23 @@ def test_issue7065():


@pytest.mark.issue(7065)
-def test_issue7065_b():
+@pytest.mark.parametrize("entity_in_first_sentence", [True, False])
+def test_sentence_crossing_ents(entity_in_first_sentence: bool):
+    """Tests if NEL crashes if entities cross sentence boundaries and the first associated sentence doesn't have an
+    entity.
+    entity_in_prior_sentence (bool): Whether to include an entity in the first sentence associated with the
+    sentence-crossing entity.
+    """
    # Test that the NEL doesn't crash when an entity crosses a sentence boundary
    nlp = English()
    vector_length = 3
-    nlp.add_pipe("sentencizer")
    text = "Mahler 's Symphony No. 8 was beautiful."
-    entities = [(0, 6, "PERSON"), (10, 24, "WORK")]
-    links = {
-        (0, 6): {"Q7304": 1.0, "Q270853": 0.0},
-        (10, 24): {"Q7304": 0.0, "Q270853": 1.0},
-    }
-    sent_starts = [1, -1, 0, 0, 0, 0, 0, 0, 0]
+    entities = [(10, 24, "WORK")]
+    links = {(10, 24): {"Q7304": 0.0, "Q270853": 1.0}}
+    if entity_in_first_sentence:
+        entities.append((0, 6, "PERSON"))
+        links[(0, 6)] = {"Q7304": 1.0, "Q270853": 0.0}
+    sent_starts = [1, -1, 0, 0, 0, 1, 0, 0, 0]
    doc = nlp(text)
    example = Example.from_dict(
        doc, {"entities": entities, "links": links, "sent_starts": sent_starts}
@ -145,31 +150,14 @@ def test_issue7065_b():

    # Create the Entity Linker component and add it to the pipeline
    entity_linker = nlp.add_pipe("entity_linker", last=True)
-    entity_linker.set_kb(create_kb)
+    entity_linker.set_kb(create_kb)  # type: ignore
    # train the NEL pipe
    optimizer = nlp.initialize(get_examples=lambda: train_examples)
    for i in range(2):
-        losses = {}
-        nlp.update(train_examples, sgd=optimizer, losses=losses)
+        nlp.update(train_examples, sgd=optimizer)

-    # Add a custom rule-based component to mimick NER
-    patterns = [
-        {"label": "PERSON", "pattern": [{"LOWER": "mahler"}]},
-        {
-            "label": "WORK",
-            "pattern": [
-                {"LOWER": "symphony"},
-                {"LOWER": "no"},
-                {"LOWER": "."},
-                {"LOWER": "8"},
-            ],
-        },
-    ]
-    ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
-    ruler.add_patterns(patterns)
-    # test the trained model - this should not throw E148
-    doc = nlp(text)
-    assert doc
+    # This shouldn't crash.
+    entity_linker.predict([example.reference])  # type: ignore


 def test_no_entities():
--- a/spacy/tests/pipeline/test_morphologizer.py
+++ b/spacy/tests/pipeline/test_morphologizer.py
@ -1,5 +1,5 @@
 import pytest
-from numpy.testing import assert_equal
+from numpy.testing import assert_equal, assert_almost_equal

 from spacy import util
 from spacy.training import Example
@ -19,6 +19,8 @@ def test_label_types():
        morphologizer.add_label(9)


+TAGS = ["Feat=N", "Feat=V", "Feat=J"]
+
 TRAIN_DATA = [
    (
        "I like green eggs",
@ -32,6 +34,29 @@ TRAIN_DATA = [
 ]


+def test_label_smoothing():
+    nlp = Language()
+    morph_no_ls = nlp.add_pipe("morphologizer", "no_label_smoothing")
+    morph_ls = nlp.add_pipe(
+        "morphologizer", "label_smoothing", config=dict(label_smoothing=0.05)
+    )
+    train_examples = []
+    losses = {}
+    for tag in TAGS:
+        morph_no_ls.add_label(tag)
+        morph_ls.add_label(tag)
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+
+    nlp.initialize(get_examples=lambda: train_examples)
+    tag_scores, bp_tag_scores = morph_ls.model.begin_update(
+        [eg.predicted for eg in train_examples]
+    )
+    no_ls_grads = morph_no_ls.get_loss(train_examples, tag_scores)[1][0]
+    ls_grads = morph_ls.get_loss(train_examples, tag_scores)[1][0]
+    assert_almost_equal(ls_grads / no_ls_grads, 0.94285715)
+
+
 def test_no_label():
    nlp = Language()
    nlp.add_pipe("morphologizer")
--- a/spacy/tests/pipeline/test_spancat.py
+++ b/spacy/tests/pipeline/test_spancat.py
@ -15,6 +15,8 @@ OPS = get_current_ops()

 SPAN_KEY = "labeled_spans"

+SPANCAT_COMPONENTS = ["spancat", "spancat_singlelabel"]
+
 TRAIN_DATA = [
    ("Who is Shaka Khan?", {"spans": {SPAN_KEY: [(7, 17, "PERSON")]}}),
    (
@ -41,38 +43,42 @@ def make_examples(nlp, data=TRAIN_DATA):
    return train_examples


-def test_no_label():
+@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
+def test_no_label(name):
    nlp = Language()
-    nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
+    nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
    with pytest.raises(ValueError):
        nlp.initialize()


-def test_no_resize():
+@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
+def test_no_resize(name):
    nlp = Language()
-    spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
+    spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
    spancat.add_label("Thing")
    spancat.add_label("Phrase")
    assert spancat.labels == ("Thing", "Phrase")
    nlp.initialize()
-    assert spancat.model.get_dim("nO") == 2
+    assert spancat.model.get_dim("nO") == spancat._n_labels
    # this throws an error because the spancat can't be resized after initialization
    with pytest.raises(ValueError):
        spancat.add_label("Stuff")


-def test_implicit_labels():
+@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
+def test_implicit_labels(name):
    nlp = Language()
-    spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
+    spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
    assert len(spancat.labels) == 0
    train_examples = make_examples(nlp)
    nlp.initialize(get_examples=lambda: train_examples)
    assert spancat.labels == ("PERSON", "LOC")


-def test_explicit_labels():
+@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
+def test_explicit_labels(name):
    nlp = Language()
-    spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
+    spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
    assert len(spancat.labels) == 0
    spancat.add_label("PERSON")
    spancat.add_label("LOC")
@ -102,13 +108,13 @@ def test_doc_gc():
            # XXX This fails with length 0 sometimes
            assert len(spangroup) > 0
            with pytest.raises(RuntimeError):
-                span = spangroup[0]
+                spangroup[0]


@pytest.mark.parametrize(
    "max_positive,nr_results", [(None, 4), (1, 2), (2, 3), (3, 4), (4, 4)]
 )
-def test_make_spangroup(max_positive, nr_results):
+def test_make_spangroup_multilabel(max_positive, nr_results):
    fix_random_seed(0)
    nlp = Language()
    spancat = nlp.add_pipe(
@ -120,10 +126,12 @@ def test_make_spangroup(max_positive, nr_results):
    indices = ngram_suggester([doc])[0].dataXd
    assert_array_equal(OPS.to_numpy(indices), numpy.asarray([[0, 1], [1, 2], [0, 2]]))
    labels = ["Thing", "City", "Person", "GreatCity"]
+    for label in labels:
+        spancat.add_label(label)
    scores = numpy.asarray(
        [[0.2, 0.4, 0.3, 0.1], [0.1, 0.6, 0.2, 0.4], [0.8, 0.7, 0.3, 0.9]], dtype="f"
    )
-    spangroup = spancat._make_span_group(doc, indices, scores, labels)
+    spangroup = spancat._make_span_group_multilabel(doc, indices, scores)
    assert len(spangroup) == nr_results

    # first span is always the second token "London"
@ -154,6 +162,118 @@ def test_make_spangroup(max_positive, nr_results):
    assert_almost_equal(0.9, spangroup.attrs["scores"][-1], 5)


+@pytest.mark.parametrize(
+    "threshold,allow_overlap,nr_results",
+    [(0.05, True, 3), (0.05, False, 1), (0.5, True, 2), (0.5, False, 1)],
+)
+def test_make_spangroup_singlelabel(threshold, allow_overlap, nr_results):
+    fix_random_seed(0)
+    nlp = Language()
+    spancat = nlp.add_pipe(
+        "spancat",
+        config={
+            "spans_key": SPAN_KEY,
+            "threshold": threshold,
+            "max_positive": 1,
+        },
+    )
+    doc = nlp.make_doc("Greater London")
+    ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2])
+    indices = ngram_suggester([doc])[0].dataXd
+    assert_array_equal(OPS.to_numpy(indices), numpy.asarray([[0, 1], [1, 2], [0, 2]]))
+    labels = ["Thing", "City", "Person", "GreatCity"]
+    for label in labels:
+        spancat.add_label(label)
+    scores = numpy.asarray(
+        [[0.2, 0.4, 0.3, 0.1], [0.1, 0.6, 0.2, 0.4], [0.8, 0.7, 0.3, 0.9]], dtype="f"
+    )
+    spangroup = spancat._make_span_group_singlelabel(
+        doc, indices, scores, allow_overlap
+    )
+    assert len(spangroup) == nr_results
+    if threshold > 0.4:
+        if allow_overlap:
+            assert spangroup[0].text == "London"
+            assert spangroup[0].label_ == "City"
+            assert spangroup[1].text == "Greater London"
+            assert spangroup[1].label_ == "GreatCity"
+
+        else:
+            assert spangroup[0].text == "Greater London"
+            assert spangroup[0].label_ == "GreatCity"
+    else:
+        if allow_overlap:
+            assert spangroup[0].text == "Greater"
+            assert spangroup[0].label_ == "City"
+            assert spangroup[1].text == "London"
+            assert spangroup[1].label_ == "City"
+            assert spangroup[2].text == "Greater London"
+            assert spangroup[2].label_ == "GreatCity"
+        else:
+            assert spangroup[0].text == "Greater London"
+
+
+def test_make_spangroup_negative_label():
+    fix_random_seed(0)
+    nlp_single = Language()
+    nlp_multi = Language()
+    spancat_single = nlp_single.add_pipe(
+        "spancat",
+        config={
+            "spans_key": SPAN_KEY,
+            "threshold": 0.1,
+            "max_positive": 1,
+        },
+    )
+    spancat_multi = nlp_multi.add_pipe(
+        "spancat",
+        config={
+            "spans_key": SPAN_KEY,
+            "threshold": 0.1,
+            "max_positive": 2,
+        },
+    )
+    spancat_single.add_negative_label = True
+    spancat_multi.add_negative_label = True
+    doc = nlp_single.make_doc("Greater London")
+    labels = ["Thing", "City", "Person", "GreatCity"]
+    for label in labels:
+        spancat_multi.add_label(label)
+        spancat_single.add_label(label)
+    ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2])
+    indices = ngram_suggester([doc])[0].dataXd
+    assert_array_equal(OPS.to_numpy(indices), numpy.asarray([[0, 1], [1, 2], [0, 2]]))
+    scores = numpy.asarray(
+        [
+            [0.2, 0.4, 0.3, 0.1, 0.1],
+            [0.1, 0.6, 0.2, 0.4, 0.9],
+            [0.8, 0.7, 0.3, 0.9, 0.1],
+        ],
+        dtype="f",
+    )
+    spangroup_multi = spancat_multi._make_span_group_multilabel(doc, indices, scores)
+    spangroup_single = spancat_single._make_span_group_singlelabel(doc, indices, scores)
+    assert len(spangroup_single) == 2
+    assert spangroup_single[0].text == "Greater"
+    assert spangroup_single[0].label_ == "City"
+    assert spangroup_single[1].text == "Greater London"
+    assert spangroup_single[1].label_ == "GreatCity"
+
+    assert len(spangroup_multi) == 6
+    assert spangroup_multi[0].text == "Greater"
+    assert spangroup_multi[0].label_ == "City"
+    assert spangroup_multi[1].text == "Greater"
+    assert spangroup_multi[1].label_ == "Person"
+    assert spangroup_multi[2].text == "London"
+    assert spangroup_multi[2].label_ == "City"
+    assert spangroup_multi[3].text == "London"
+    assert spangroup_multi[3].label_ == "GreatCity"
+    assert spangroup_multi[4].text == "Greater London"
+    assert spangroup_multi[4].label_ == "Thing"
+    assert spangroup_multi[5].text == "Greater London"
+    assert spangroup_multi[5].label_ == "GreatCity"
+
+
 def test_ngram_suggester(en_tokenizer):
    # test different n-gram lengths
    for size in [1, 2, 3]:
@ -371,9 +491,9 @@ def test_overfitting_IO_overlapping():
        assert set([span.label_ for span in spans2]) == {"LOC", "DOUBLE_LOC"}


-def test_zero_suggestions():
+@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
+def test_zero_suggestions(name):
    # Test with a suggester that can return 0 suggestions
-
    @registry.misc("test_mixed_zero_suggester")
    def make_mixed_zero_suggester():
        def mixed_zero_suggester(docs, *, ops=None):
@ -400,7 +520,7 @@ def test_zero_suggestions():
    fix_random_seed(0)
    nlp = English()
    spancat = nlp.add_pipe(
-        "spancat",
+        name,
        config={
            "suggester": {"@misc": "test_mixed_zero_suggester"},
            "spans_key": SPAN_KEY,
@ -408,7 +528,7 @@ def test_zero_suggestions():
    )
    train_examples = make_examples(nlp)
    optimizer = nlp.initialize(get_examples=lambda: train_examples)
-    assert spancat.model.get_dim("nO") == 2
+    assert spancat.model.get_dim("nO") == spancat._n_labels
    assert set(spancat.labels) == {"LOC", "PERSON"}

    nlp.update(train_examples, sgd=optimizer)
@ -424,9 +544,10 @@ def test_zero_suggestions():
    list(nlp.pipe(["", "one", "three three three"]))


-def test_set_candidates():
+@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
+def test_set_candidates(name):
    nlp = Language()
-    spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
+    spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
    train_examples = make_examples(nlp)
    nlp.initialize(get_examples=lambda: train_examples)
    texts = [
--- a/spacy/tests/pipeline/test_tagger.py
+++ b/spacy/tests/pipeline/test_tagger.py
@ -1,5 +1,5 @@
 import pytest
-from numpy.testing import assert_equal
+from numpy.testing import assert_equal, assert_almost_equal
 from spacy.attrs import TAG

 from spacy import util
@ -67,6 +67,29 @@ PARTIAL_DATA = [
 ]


+def test_label_smoothing():
+    nlp = Language()
+    tagger_no_ls = nlp.add_pipe("tagger", "no_label_smoothing")
+    tagger_ls = nlp.add_pipe(
+        "tagger", "label_smoothing", config=dict(label_smoothing=0.05)
+    )
+    train_examples = []
+    losses = {}
+    for tag in TAGS:
+        tagger_no_ls.add_label(tag)
+        tagger_ls.add_label(tag)
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+
+    nlp.initialize(get_examples=lambda: train_examples)
+    tag_scores, bp_tag_scores = tagger_ls.model.begin_update(
+        [eg.predicted for eg in train_examples]
+    )
+    no_ls_grads = tagger_no_ls.get_loss(train_examples, tag_scores)[1][0]
+    ls_grads = tagger_ls.get_loss(train_examples, tag_scores)[1][0]
+    assert_almost_equal(ls_grads / no_ls_grads, 0.925)
+
+
 def test_no_label():
    nlp = Language()
    nlp.add_pipe("tagger")
--- a/spacy/tests/test_cli.py
+++ b/spacy/tests/test_cli.py
@ -397,7 +397,14 @@ def test_parse_cli_overrides():

@pytest.mark.parametrize("lang", ["en", "nl"])
@pytest.mark.parametrize(
-    "pipeline", [["tagger", "parser", "ner"], [], ["ner", "textcat", "sentencizer"]]
+    "pipeline",
+    [
+        ["tagger", "parser", "ner"],
+        [],
+        ["ner", "textcat", "sentencizer"],
+        ["morphologizer", "spancat", "entity_linker"],
+        ["spancat_singlelabel", "textcat_multilabel"],
+    ],
 )
@pytest.mark.parametrize("optimize", ["efficiency", "accuracy"])
@pytest.mark.parametrize("pretraining", [True, False])
--- a/spacy/tests/test_cli_app.py
+++ b/spacy/tests/test_cli_app.py
@ -5,10 +5,18 @@ import srsly
 from typer.testing import CliRunner
 from spacy.tokens import DocBin, Doc

-from spacy.cli._util import app
+from spacy.cli._util import app, get_git_version
 from .util import make_tempdir, normalize_whitespace


+def has_git():
+    try:
+        get_git_version()
+        return True
+    except RuntimeError:
+        return False
+
+
 def test_convert_auto():
    with make_tempdir() as d_in, make_tempdir() as d_out:
        for f in ["data1.iob", "data2.iob", "data3.iob"]:
@ -181,6 +189,7 @@ def test_project_run(project_dir):
    assert "okokok" in result.stdout


+@pytest.mark.skipif(not has_git(), reason="git not installed")
@pytest.mark.parametrize(
    "options",
    [
--- a/spacy/tokens/span.pyx
+++ b/spacy/tokens/span.pyx
@ -460,9 +460,8 @@ cdef class Span:
                    start = i
                    if start >= self.end:
                        break
-            if start < self.end:
-                yield Span(self.doc, start, self.end)
-
+                elif i == self.doc.length - 1:
+                    yield Span(self.doc, start, self.doc.length)

    @property
    def ents(self):
--- a/website/docs/api/cli.mdx
+++ b/website/docs/api/cli.mdx
@ -1254,19 +1254,19 @@ be provided.
 > $ python -m spacy find-threshold my_nlp data.spacy spancat threshold spans_sc_f
 > ```

-| Name                    | Description                                                                                                                                                                          |
-| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `model`                 | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~                                                                                           |
-| `data_path`             | Path to file with DocBin with docs to use for threshold search. ~~Path (positional)~~                                                                                                |
-| `pipe_name`             | Name of pipe to examine thresholds for. ~~str (positional)~~                                                                                                                         |
-| `threshold_key`         | Key of threshold attribute in component's configuration. ~~str (positional)~~                                                                                                        |
-| `scores_key`            | Name of score to metric to optimize. ~~str (positional)~~                                                                                                                            |
-| `--n_trials`, `-n`      | Number of trials to determine optimal thresholds. ~~int (option)~~                                                                                                                   |
-| `--code`, `-c`          | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
-| `--gpu-id`, `-g`        | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~                                                                                                                       |
-| `--gold-preproc`, `-G`  | Use gold preprocessing. ~~bool (flag)~~                                                                                                                                              |
-| `--silent`, `-V`, `-VV` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~                                                                                                                       |
-| `--help`, `-h`          | Show help message and available arguments. ~~bool (flag)~~                                                                                                                           |
+| Name                     | Description                                                                                                                                                                          |
+| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `model`                  | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~                                                                                           |
+| `data_path`              | Path to file with DocBin with docs to use for threshold search. ~~Path (positional)~~                                                                                                |
+| `pipe_name`              | Name of pipe to examine thresholds for. ~~str (positional)~~                                                                                                                         |
+| `threshold_key`          | Key of threshold attribute in component's configuration. ~~str (positional)~~                                                                                                        |
+| `scores_key`             | Name of score to metric to optimize. ~~str (positional)~~                                                                                                                            |
+| `--n_trials`, `-n`       | Number of trials to determine optimal thresholds. ~~int (option)~~                                                                                                                   |
+| `--code`, `-c`           | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
+| `--gpu-id`, `-g`         | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~                                                                                                                       |
+| `--gold-preproc`, `-G`   | Use gold preprocessing. ~~bool (flag)~~                                                                                                                                              |
+| `--verbose`, `-V`, `-VV` | Display more information for debugging purposes. ~~bool (flag)~~                                                                                                                     |
+| `--help`, `-h`           | Show help message and available arguments. ~~bool (flag)~~                                                                                                                           |

 ## assemble {id="assemble",tag="command"}

--- a/website/docs/api/morphologizer.mdx
+++ b/website/docs/api/morphologizer.mdx
@ -42,12 +42,13 @@ architectures and their arguments and hyperparameters.
 > nlp.add_pipe("morphologizer", config=config)
 > ```

-| Setting                                  | Description                                                                                                                                                                                                                                                            |
-| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `model`                                  | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                |
-| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~                                                                                                                                                                                  |
-| `extend` <Tag variant="new">3.2</Tag>    | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~                                                                                                                      |
-| `scorer` <Tag variant="new">3.2</Tag>    | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
+| Setting                                        | Description                                                                                                                                                                                                                                                            |
+| ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `model`                                        | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                |
+| `overwrite` <Tag variant="new">3.2</Tag>       | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~                                                                                                                                                                                  |
+| `extend` <Tag variant="new">3.2</Tag>          | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~                                                                                                                      |
+| `scorer` <Tag variant="new">3.2</Tag>          | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
+| `label_smoothing` <Tag variant="new">3.6</Tag> | [Label smoothing](https://arxiv.org/abs/1906.02629) factor. Defaults to `0.0`. ~~float~~                                                                                                                                                                               |

 ```python
 %%GITHUB_SPACY/spacy/pipeline/morphologizer.pyx
--- a/website/docs/api/spancategorizer.mdx
+++ b/website/docs/api/spancategorizer.mdx
@ -13,6 +13,13 @@ A span categorizer consists of two parts: a [suggester function](#suggesters)
 that proposes candidate spans, which may or may not overlap, and a labeler model
 that predicts zero or more labels for each candidate.

+This component comes in two forms: `spancat` and `spancat_singlelabel` (added in
+spaCy v3.5.1). When you need to perform multi-label classification on your
+spans, use `spancat`. The `spancat` component uses a `Logistic` layer where the
+output class probabilities are independent for each class. However, if you need
+to predict at most one true class for a span, then use `spancat_singlelabel`. It
+uses a `Softmax` layer and treats the task as a multi-class problem.
+
 Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the doc.
 Individual span scores can be found in `spangroup.attrs["scores"]`.

@ -38,7 +45,7 @@ how the component should be configured. You can override its settings via the
 [model architectures](/api/architectures) documentation for details on the
 architectures and their arguments and hyperparameters.

-> #### Example
+> #### Example (spancat)
 >
 > ```python
 > from spacy.pipeline.spancat import DEFAULT_SPANCAT_MODEL
@ -52,14 +59,33 @@ architectures and their arguments and hyperparameters.
 > nlp.add_pipe("spancat", config=config)
 > ```

-| Setting        | Description                                                                                                                                                                                                                                                                                             |
-| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `suggester`    | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. Defaults to [`ngram_suggester`](#ngram_suggester). ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~                                                  |
-| `model`        | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. Defaults to [SpanCategorizer](/api/architectures#SpanCategorizer). ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ |
-| `spans_key`    | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~                                                                                  |
-| `threshold`    | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~                                                                                                                                                          |
-| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~                                                                                                                                                                                      |
-| `scorer`       | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~                                                                                                                                       |
+> #### Example (spancat_singlelabel)
+>
+> ```python
+> from spacy.pipeline.spancat import DEFAULT_SPANCAT_SINGLELABEL_MODEL
+> config = {
+>     "threshold": 0.5,
+>     "spans_key": "labeled_spans",
+>     "model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
+>     "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
+>     # Additional spancat_singlelabel parameters
+>     "negative_weight": 0.8,
+>     "allow_overlap": True,
+> }
+> nlp.add_pipe("spancat_singlelabel", config=config)
+> ```
+
+| Setting                                             | Description                                                                                                                                                                                                                                                                                             |
+| --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `suggester`                                         | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. Defaults to [`ngram_suggester`](#ngram_suggester). ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~                                                  |
+| `model`                                             | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. Defaults to [SpanCategorizer](/api/architectures#SpanCategorizer). ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ |
+| `spans_key`                                         | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~                                                                                  |
+| `threshold`                                         | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Meant to be used in combination with the multi-class `spancat` component with a `Logistic` scoring layer. Defaults to `0.5`. ~~float~~                                                |
+| `max_positive`                                      | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. Meant to be used together with the `spancat` component and defaults to 0 with `spancat_singlelabel`. ~~Optional[int]~~                                                                                 |
+| `scorer`                                            | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~                                                                                                                                       |
+| `add_negative_label` <Tag variant="new">3.5.1</Tag> | Whether to learn to predict a special negative label for each unannotated `Span` . This should be `True` when using a `Softmax` classifier layer and so its `True` by default for `spancat_singlelabel`. Spans with negative labels and their scores are not stored as annotations. ~~bool~~            |
+| `negative_weight` <Tag variant="new">3.5.1</Tag>    | Multiplier for the loss terms. It can be used to downweight the negative samples if there are too many. It is only used when `add_negative_label` is `True`. Defaults to `1.0`. ~~float~~                                                                                                               |
+| `allow_overlap` <Tag variant="new">3.5.1</Tag>      | If `True`, the data is assumed to contain overlapping spans. It is only available when `max_positive` is exactly 1. Defaults to `True`. ~~bool~~                                                                                                                                                        |

 ```python
 %%GITHUB_SPACY/spacy/pipeline/spancat.py
@ -71,6 +97,7 @@ architectures and their arguments and hyperparameters.
 >
 > ```python
 > # Construction via add_pipe with default model
+> # Replace 'spancat' with 'spancat_singlelabel' for exclusive classes
 > spancat = nlp.add_pipe("spancat")
 >
 > # Construction via add_pipe with custom model
@ -86,16 +113,19 @@ Create a new pipeline instance. In your application, you would normally use a
 shortcut for this and instantiate the component using its string name and
 [`nlp.add_pipe`](/api/language#create_pipe).

-| Name           | Description                                                                                                                                                                                                                          |
-| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `vocab`        | The shared vocabulary. ~~Vocab~~                                                                                                                                                                                                     |
-| `model`        | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ |
-| `suggester`    | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~                                  |
-| `name`         | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~                                                                                                                                  |
-| _keyword-only_ |                                                                                                                                                                                                                                      |
-| `spans_key`    | Key of the [`Doc.spans`](/api/doc#sans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~                |
-| `threshold`    | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~                                                                                       |
-| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~                                                                                                                   |
+| Name                                                | Description                                                                                                                                                                                                                                                                                  |
+| --------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `vocab`                                             | The shared vocabulary. ~~Vocab~~                                                                                                                                                                                                                                                             |
+| `model`                                             | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~                                                         |
+| `suggester`                                         | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~                                                                                          |
+| `name`                                              | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~                                                                                                                                                                                          |
+| _keyword-only_                                      |                                                                                                                                                                                                                                                                                              |
+| `spans_key`                                         | Key of the [`Doc.spans`](/api/doc#sans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~                                                                        |
+| `threshold`                                         | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~                                                                                                                                               |
+| `max_positive`                                      | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~                                                                                                                                                                           |
+| `allow_overlap` <Tag variant="new">3.5.1</Tag>      | If `True`, the data is assumed to contain overlapping spans. It is only available when `max_positive` is exactly 1. Defaults to `True`. ~~bool~~                                                                                                                                             |
+| `add_negative_label` <Tag variant="new">3.5.1</Tag> | Whether to learn to predict a special negative label for each unannotated `Span`. This should be `True` when using a `Softmax` classifier layer and so its `True` by default for `spancat_singlelabel` . Spans with negative labels and their scores are not stored as annotations. ~~bool~~ |
+| `negative_weight` <Tag variant="new">3.5.1</Tag>    | Multiplier for the loss terms. It can be used to downweight the negative samples if there are too many . It is only used when `add_negative_label` is `True`. Defaults to `1.0`. ~~float~~                                                                                                   |

 ## SpanCategorizer.\_\_call\_\_ {id="call",tag="method"}

--- a/website/docs/api/tagger.mdx
+++ b/website/docs/api/tagger.mdx
@ -40,12 +40,13 @@ architectures and their arguments and hyperparameters.
 > nlp.add_pipe("tagger", config=config)
 > ```

-| Setting                                     | Description                                                                                                                                                                                                                                                                                            |
-| ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `model`                                     | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
-| `overwrite` <Tag variant="new">3.2</Tag>    | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~                                                                                                                                                                                                                              |
-| `scorer` <Tag variant="new">3.2</Tag>       | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"tag"`. ~~Optional[Callable]~~                                                                                                                                                            |
-| `neg_prefix` <Tag variant="new">3.2.1</Tag> | The prefix used to specify incorrect tags while training. The tagger will learn not to predict exactly this tag. Defaults to `!`. ~~str~~                                                                                                                                                              |
+| Setting                                        | Description                                                                                                                                                                                                                                                                                            |
+| ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `model`                                        | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
+| `overwrite` <Tag variant="new">3.2</Tag>       | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~                                                                                                                                                                                                                              |
+| `scorer` <Tag variant="new">3.2</Tag>          | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"tag"`. ~~Optional[Callable]~~                                                                                                                                                            |
+| `neg_prefix` <Tag variant="new">3.2.1</Tag>    | The prefix used to specify incorrect tags while training. The tagger will learn not to predict exactly this tag. Defaults to `!`. ~~str~~                                                                                                                                                              |
+| `label_smoothing` <Tag variant="new">3.6</Tag> | [Label smoothing](https://arxiv.org/abs/1906.02629) factor. Defaults to `0.0`. ~~float~~                                                                                                                                                                                                               |

 ```python
 %%GITHUB_SPACY/spacy/pipeline/tagger.pyx
--- a/website/package.json
+++ b/website/package.json
@ -6,6 +6,7 @@
    "dev": "next dev",
    "build": "next build && npm run sitemap && next export",
    "prebuild": "pip install -r setup/requirements.txt && sh setup/setup.sh",
+    "predev": "npm run prebuild",
    "sitemap": "next-sitemap --config next-sitemap.config.mjs",
    "start": "next start",
    "lint": "next lint",
--- a/website/src/styles/navigation.module.sass
+++ b/website/src/styles/navigation.module.sass
@ -111,11 +111,12 @@
    line-height: var(--line-height-xs)
    text-align: center

-@include breakpoint(max, xs)
-    .list
+@include breakpoint(max, md)
+    .alert
        display: none

-    .alert
+@include breakpoint(max, xs)
+    .list
        display: none

    .has-alert
--- a/website/src/templates/index.js
+++ b/website/src/templates/index.js
@ -57,9 +57,15 @@ const AlertSpace = ({ nightly, legacy }) => {
    )
 }

+// const navAlert = (
+//     <Link to="/usage/v3-5" noLinkLayout>
+//         <strong>💥 Out now:</strong> spaCy v3.5
+//     </Link>
+// )
+
 const navAlert = (
-    <Link to="/usage/v3-5" noLinkLayout>
-        <strong>💥 Out now:</strong> spaCy v3.5
+    <Link to="https://form.typeform.com/to/aMel9q9f" noLinkLayout>
+        <strong>💥 Take the user survey!</strong>
    </Link>
 )