Update docs & tutorial (#1178)

* Release DRAGNN

* Update CoNLL evaluation table & evaluator.py

* Update documentation & tutorial for DRAGNN

syntaxnet/dragnn/python/render_parse_tree_graphviz.py

@@ -38,16 +38,19 @@ def parse_tree_graph(sentence):
     warnings.simplefilter("ignore")
     svg = graph.draw(format="svg", prog="dot")
 
+  svg = unicode(svg, "utf-8")
+
   # For both inline and "new window" displays, we show the tokens with the
   # graph. (The sentence order of nodes is sometimes difficult to read.)
-  image_and_text = "<p><em>Text:</em> {}</p>{}".format(" ".join(
+  image_and_text = u"<p><em>Text:</em> {}</p>{}".format(" ".join(
       token.word for token in sentence.token), svg)
 
   # We generate a base64 URI. This is not too big, but older browsers may not
   # handle it well.
-  new_window_html = ("<style type='text/css'>svg { max-width: 100%; }</style>" +
-                     image_and_text)
-  as_uri = "data:text/html;base64,{}".format(base64.b64encode(new_window_html))
+  new_window_html = (u"<style type='text/css'>svg { max-width: 100%; }</style>"
+                     + image_and_text).encode("utf-8")
+  as_uri = "data:text/html;charset=utf-8;base64,{}".format(
+      base64.b64encode(new_window_html))
 
-  return "{}<p><a target='_blank' href='{}'>Open in new window</a></p>".format(
+  return u"{}<p><a target='_blank' href='{}'>Open in new window</a></p>".format(
       image_and_text, as_uri)

syntaxnet/dragnn/python/render_parse_tree_graphviz_test.py

@@ -17,7 +17,7 @@ class RenderParseTreeGraphvizTest(googletest.TestCase):
     document.token.add(start=1, end=1, word='there')
     contents = render_parse_tree_graphviz.parse_tree_graph(document)
     self.assertIn('<polygon', contents)
-    self.assertIn('text/html;base64', contents)
+    self.assertIn('text/html;charset=utf-8;base64', contents)
     self.assertIn('something', contents)
     self.assertIn('hi', contents)
     self.assertIn('there', contents)

syntaxnet/examples/dragnn/data/es/char-map

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syntaxnet/examples/dragnn/data/es/label-map

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+dep 1021
+nsubj:pass 1006
+csubj 567
+compound 357
+csubj:pass 5

syntaxnet/examples/dragnn/data/es/parser_spec.textproto

+component {
+  name: "char_lstm"
+  transition_system {
+    registered_name: "char-shift-only"
+    parameters {
+      key: "left_to_right"
+      value: "true"
+    }
+  }
+  fixed_feature {
+    name: "chars"
+    fml: "char-input.text-char"
+    embedding_dim: 16
+  }
+  network_unit {
+    registered_name: "wrapped_units.LayerNormBasicLSTMNetwork"
+    parameters {
+      key: "hidden_layer_sizes"
+      value: "256"
+    }
+  }
+  backend {
+    registered_name: "SyntaxNetComponent"
+  }
+  component_builder {
+    registered_name: "DynamicComponentBuilder"
+  }
+}
+component {
+  name: "lookahead"
+  transition_system {
+    registered_name: "shift-only"
+    parameters {
+      key: "left_to_right"
+      value: "false"
+    }
+  }
+  linked_feature {
+    name: "char_lstm"
+    fml: "input.last-char-focus"
+    embedding_dim: 64
+    source_component: "char_lstm"
+    source_translator: "identity"
+    source_layer: "state_h_0"
+  }
+  network_unit {
+    registered_name: "wrapped_units.LayerNormBasicLSTMNetwork"
+    parameters {
+      key: "hidden_layer_sizes"
+      value: "256"
+    }
+  }
+  backend {
+    registered_name: "SyntaxNetComponent"
+  }
+  component_builder {
+    registered_name: "DynamicComponentBuilder"
+  }
+}
+component {
+  name: "tagger"
+  transition_system {
+    registered_name: "tagger"
+  }
+  linked_feature {
+    name: "lookahead"
+    fml: "input.focus"
+    embedding_dim: 64
+    source_component: "lookahead"
+    source_translator: "reverse-token"
+    source_layer: "state_h_0"
+  }
+  network_unit {
+    registered_name: "wrapped_units.LayerNormBasicLSTMNetwork"
+    parameters {
+      key: "hidden_layer_sizes"
+      value: "256"
+    }
+  }
+  backend {
+    registered_name: "SyntaxNetComponent"
+  }
+  component_builder {
+    registered_name: "DynamicComponentBuilder"
+  }
+}
+component {
+  name: "parser"
+  transition_system {
+    registered_name: "arc-standard"
+  }
+  fixed_feature {
+    name: "labels"
+    fml: "stack.child(1).label stack.child(1).sibling(-1).label stack.child(-1).label stack.child(-1).sibling(1).label stack(1).child(1).label stack(1).child(1).sibling(-1).label stack(1).child(-1).label stack(1).child(-1).sibling(1).label stack.child(2).label stack.child(-2).label stack(1).child(2).label stack(1).child(-2).label"
+    embedding_dim: 16
+  }
+  linked_feature {
+    name: "lookahead"
+    fml: "input.focus"
+    embedding_dim: 64
+    source_component: "lookahead"
+    source_translator: "reverse-token"
+    source_layer: "state_h_0"
+  }
+  linked_feature {
+    name: "tagger"
+    fml: "input.focus stack.focus stack(1).focus"
+    embedding_dim: 64
+    source_component: "tagger"
+    source_translator: "identity"
+    source_layer: "state_h_0"
+  }
+  linked_feature {
+    name: "rnn-stack"
+    fml: "stack.focus stack(1).focus"
+    embedding_dim: 64
+    source_component: "parser"
+    source_translator: "shift-reduce-step"
+    source_layer: "layer_0"
+  }
+  network_unit {
+    registered_name: "FeedForwardNetwork"
+    parameters {
+      key: "hidden_layer_sizes"
+      value: "256"
+    }
+    parameters {
+      key: "layer_norm_hidden"
+      value: "true"
+    }
+  }
+  backend {
+    registered_name: "SyntaxNetComponent"
+  }
+  component_builder {
+    registered_name: "DynamicComponentBuilder"
+  }
+}

syntaxnet/examples/dragnn/data/es/segmenter/char-map

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syntaxnet/examples/dragnn/data/es/segmenter/char-ngram-map

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syntaxnet/examples/dragnn/data/es/segmenter/label-map

+32
+case 56548
+det 51438
+punct 40104
+nmod 33120
+obl 20916
+amod 19460
+conj 14420
+root 13477
+nsubj 13214
+obj 11448
+cc 11100
+mark 9681
+advmod 9517
+appos 7524
+iobj 6239
+flat 6110
+nummod 6054
+cop 5274
+acl 4722
+acl:relcl 4478
+advcl 3547
+aux 3483
+fixed 2365
+aux:pass 1578
+parataxis 1370
+ccomp 1320
+xcomp 1188
+dep 1021
+nsubj:pass 1006
+csubj 567
+compound 357
+csubj:pass 5

syntaxnet/examples/dragnn/data/es/segmenter/spec.textproto

+component {
+  name: "lookahead"
+  transition_system {
+    registered_name: "shift-only"
+    parameters {
+      key: "left_to_right"
+      value: "false"
+    }
+  }
+  fixed_feature {
+    name: "char"
+    fml: "input(-1).char input.char input(1).char"
+    embedding_dim: 32
+    vocabulary_size: 119
+    size: 3
+  }
+  fixed_feature {
+    name: "char-bigram"
+    fml: "input.char-bigram"
+    embedding_dim: 32
+    vocabulary_size: 1158
+    size: 1
+  }
+  network_unit {
+    registered_name: "wrapped_units.LayerNormBasicLSTMNetwork"
+    parameters {
+      key: "hidden_layer_sizes"
+      value: "256"
+    }
+  }
+  backend {
+    registered_name: "SyntaxNetComponent"
+  }
+  num_actions: 1
+  component_builder {
+    registered_name: "DynamicComponentBuilder"
+  }
+}
+component {
+  name: "segmenter"
+  transition_system {
+    registered_name: "binary-segment-transitions"
+  }
+  resource {
+    name: "suffix-table"
+    part {
+      file_pattern: "suffix-table"
+    }
+  }
+  linked_feature {
+    name: "lookahead"
+    fml: "input.focus stack.focus"
+    embedding_dim: 32
+    size: 2
+    source_component: "lookahead"
+    source_translator: "reverse-token"
+    source_layer: "state_h_0"
+  }
+  network_unit {
+    registered_name: "wrapped_units.LayerNormBasicLSTMNetwork"
+    parameters {
+      key: "hidden_layer_sizes"
+      value: "128"
+    }
+  }
+  backend {
+    registered_name: "SyntaxNetComponent"
+  }
+  num_actions: 2
+  component_builder {
+    registered_name: "DynamicComponentBuilder"
+  }
+}

syntaxnet/examples/dragnn/data/es/segmenter/tag-map

+4
+DET 3
+ADP 2
+ADJ 1
+VERB 1

syntaxnet/examples/dragnn/data/es/segmenter/tag-to-category

+	ADV
+ADJ	X
+ADP	X
+DET	DET
+VERB	X

syntaxnet/examples/dragnn/interactive_text_analyzer.ipynb

@@ -6,61 +6,82 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "scrolled": false
    },
    "outputs": [],
    "source": [
+    "import os\n",
     "import ipywidgets as widgets\n",
     "import tensorflow as tf\n",
-    "import uuid\n",
     "from IPython import display\n",
     "from dragnn.protos import spec_pb2\n",
     "from dragnn.python import graph_builder\n",
+    "from dragnn.python import spec_builder\n",
     "from dragnn.python import load_dragnn_cc_impl  # This loads the actual op definitions\n",
     "from dragnn.python import render_parse_tree_graphviz\n",
     "from dragnn.python import visualization\n",
     "from google.protobuf import text_format\n",
     "from syntaxnet import load_parser_ops  # This loads the actual op definitions\n",
     "from syntaxnet import sentence_pb2\n",
+    "from syntaxnet.ops import gen_parser_ops\n",
     "from tensorflow.python.platform import tf_logging as logging\n",
     "\n",
-    "# Read the master spec\n",
-    "master_spec = spec_pb2.MasterSpec()\n",
-    "with open(\"data/master_spec_es.prototext\", \"r\") as f:\n",
+    "def load_model(base_dir, master_spec_name, checkpoint_name):\n",
+    "    # Read the master spec\n",
+    "    master_spec = spec_pb2.MasterSpec()\n",
+    "    with open(os.path.join(base_dir, master_spec_name), \"r\") as f:\n",
     "        text_format.Merge(f.read(), master_spec)\n",
+    "    spec_builder.complete_master_spec(master_spec, None, base_dir)\n",
+    "    logging.set_verbosity(logging.WARN)  # Turn off TensorFlow spam.\n",
     "\n",
-    "logging.set_verbosity(logging.WARN)  # Turn off TensorFlow spam.\n",
-    "\n",
-    "# Initialize a graph\n",
-    "graph = tf.Graph()\n",
-    "with graph.as_default():\n",
+    "    # Initialize a graph\n",
+    "    graph = tf.Graph()\n",
+    "    with graph.as_default():\n",
     "        hyperparam_config = spec_pb2.GridPoint()\n",
     "        builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)\n",
     "        # This is the component that will annotate test sentences.\n",
     "        annotator = builder.add_annotation(enable_tracing=True)\n",
-    "    builder.add_saver()  # \"Savers\" can save and load models; here, we're only going to load."
+    "        builder.add_saver()  # \"Savers\" can save and load models; here, we're only going to load.\n",
+    "\n",
+    "    sess = tf.Session(graph=graph)\n",
+    "    with graph.as_default():\n",
+    "        sess.run(tf.global_variables_initializer())\n",
+    "        sess.run('save/restore_all', {'save/Const:0': os.path.join(base_dir, checkpoint_name)})\n",
+    "        \n",
+    "    def annotate_sentence(sentence):\n",
+    "        with graph.as_default():\n",
+    "            return sess.run([annotator['annotations'], annotator['traces']],\n",
+    "                            feed_dict={annotator['input_batch']: [sentence]})\n",
+    "    return annotate_sentence\n",
+    "\n",
+    "segmenter_model = load_model(\"data/es/segmenter\", \"spec.textproto\", \"checkpoint\")\n",
+    "parser_model = load_model(\"data/es\", \"parser_spec.textproto\", \"checkpoint\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "sess = tf.Session(graph=graph)\n",
-    "with graph.as_default():\n",
-    "    sess.run(tf.global_variables_initializer())\n",
-    "    sess.run('save/restore_all', {'save/Const:0': \"data/mini-spanish.checkpoint\"})\n",
-    "\n",
     "def annotate_text(text):\n",
     "    sentence = sentence_pb2.Sentence(\n",
+    "        text=text,\n",
     "        token=[sentence_pb2.Token(word=word, start=-1, end=-1) for word in text.split()]\n",
     "    )\n",
-    "    with graph.as_default():\n",
-    "        annotations, traces = sess.run([annotator['annotations'], annotator['traces']],\n",
-    "                                       feed_dict={annotator['input_batch']: [sentence.SerializeToString()]})\n",
+    "\n",
+    "    # preprocess\n",
+    "    with tf.Session(graph=tf.Graph()) as tmp_session:\n",
+    "        char_input = gen_parser_ops.char_token_generator([sentence.SerializeToString()])\n",
+    "        preprocessed = tmp_session.run(char_input)[0]\n",
+    "    segmented, _ = segmenter_model(preprocessed)\n",
+    "\n",
+    "    annotations, traces = parser_model(segmented[0])\n",
     "    assert len(annotations) == 1\n",
     "    assert len(traces) == 1\n",
     "    return sentence_pb2.Sentence.FromString(annotations[0]), traces[0]\n",
@@ -69,7 +90,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Interactive trace explorer\n",
     "Run the cell below, and then enter text in the interactive widget."
@@ -79,7 +103,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -102,7 +128,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Interactive parse tree explorer\n",
     "Run the cell below, and then enter text in the interactive widget."
@@ -112,7 +141,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -125,7 +156,10 @@
     "    def handle_submit(sender):\n",
     "        del sender  # unused\n",
     "        parse_tree, trace = annotate_text(text.value)\n",
-    "        html.value = render_parse_tree_graphviz.parse_tree_graph(parse_tree)\n",
+    "        html.value = u\"\"\"\n",
+    "        <div style=\"max-width: 100%\">{}</div>\n",
+    "        <style type=\"text/css\">svg {{ max-width: 100%; }}</style>\n",
+    "        \"\"\".format(render_parse_tree_graphviz.parse_tree_graph(parse_tree))\n",
     "\n",
     "    text.on_submit(handle_submit)\n",
     "_parse_tree_explorer()"

syntaxnet/g3doc/conll2017/README.md

@@ -11,8 +11,7 @@ Please see our [paper](paper.pdf) more technical details about the model.
 
 *   Install SyntaxNet/DRAGNN following the install instructions.
 *   Download the models [here](https://drive.google.com/file/d/0BxpbZGYVZsEeSFdrUnBNMUp1YzQ/view?usp=sharing)
-*   Download the contest [data data and
-    tools](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1976]).
+*   Download the contest [data and tools](http://universaldependencies.org/conll17/)
 *   Run the baseline_eval.py to run the pre-trained tokenizer and evaluate on
     the dev set.