Update spaCy pipeline

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+en_roberta_re_pipeline-any-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+transformer/model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,42 @@
+---
+tags:
+- spacy
+language:
+- en
+model-index:
+- name: en_roberta_re_pipeline
+  results: []
+---
+| Feature | Description |
+| --- | --- |
+| **Name** | `en_roberta_re_pipeline` |
+| **Version** | `0.0.0` |
+| **spaCy** | `>=3.5.1,<3.6.0` |
+| **Default Pipeline** | `transformer`, `relation_extractor` |
+| **Components** | `transformer`, `relation_extractor` |
+| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
+| **Sources** | n/a |
+| **License** | n/a |
+| **Author** | [n/a]() |
+
+### Label Scheme
+
+<details>
+
+<summary>View label scheme (2 labels for 1 components)</summary>
+
+| Component | Labels |
+| --- | --- |
+| **`relation_extractor`** | `DEGREE_IN`, `EXPERIENCE_IN` |
+
+</details>
+
+### Accuracy
+
+| Type | Score |
+| --- | --- |
+| `REL_MICRO_P` | 83.56 |
+| `REL_MICRO_R` | 64.21 |
+| `REL_MICRO_F` | 72.62 |
+| `TRANSFORMER_LOSS` | 0.22 |
+| `RELATION_EXTRACTOR_LOSS` | 158.63 |

config.cfg

@@ -0,0 +1,142 @@
+[paths]
+train = "data/relations_training.spacy"
+dev = "data/relations_dev.spacy"
+raw = null
+init_tok2vec = null
+vectors = null
+
+[system]
+seed = 342
+gpu_allocator = "pytorch"
+
+[nlp]
+lang = "en"
+pipeline = ["transformer","relation_extractor"]
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+batch_size = 1000
+
+[components]
+
+[components.relation_extractor]
+factory = "relation_extractor"
+threshold = 0.5
+
+[components.relation_extractor.model]
+@architectures = "rel_model.v1"
+
+[components.relation_extractor.model.classification_layer]
+@architectures = "rel_classification_layer.v1"
+nI = null
+nO = null
+
+[components.relation_extractor.model.create_instance_tensor]
+@architectures = "rel_instance_tensor.v1"
+pooling = {"@layers":"reduce_mean.v1"}
+
+[components.relation_extractor.model.create_instance_tensor.get_instances]
+@misc = "rel_instance_generator.v1"
+max_length = 100
+
+[components.relation_extractor.model.create_instance_tensor.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "*"
+
+[components.transformer]
+factory = "transformer"
+max_batch_items = 4096
+set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
+
+[components.transformer.model]
+@architectures = "spacy-transformers.TransformerModel.v3"
+name = "roberta-base"
+mixed_precision = false
+
+[components.transformer.model.get_spans]
+@span_getters = "spacy-transformers.strided_spans.v1"
+window = 64
+stride = 48
+
+[components.transformer.model.grad_scaler_config]
+
+[components.transformer.model.tokenizer_config]
+use_fast = true
+
+[components.transformer.model.transformer_config]
+
+[corpora]
+
+[corpora.dev]
+@readers = "Gold_ents_Corpus.v1"
+file = ${paths.dev}
+
+[corpora.train]
+@readers = "Gold_ents_Corpus.v1"
+file = ${paths.train}
+
+[training]
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 1
+patience = 1600000
+max_epochs = 0
+max_steps = 1000
+eval_frequency = 100
+frozen_components = []
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+before_to_disk = null
+annotating_components = []
+before_update = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_padded.v1"
+discard_oversize = true
+size = 2000
+buffer = 256
+get_length = null
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = false
+
+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = false
+eps = 0.00000001
+
+[training.optimizer.learn_rate]
+@schedules = "warmup_linear.v1"
+warmup_steps = 250
+total_steps = 20000
+initial_rate = 0.00005
+
+[training.score_weights]
+rel_micro_p = 0.0
+rel_micro_r = 0.0
+rel_micro_f = 1.0
+
+[pretraining]
+
+[initialize]
+vectors = ${paths.vectors}
+init_tok2vec = ${paths.init_tok2vec}
+vocab_data = null
+lookups = null
+before_init = null
+after_init = null
+
+[initialize.components]
+
+[initialize.tokenizer]

en_roberta_re_pipeline-any-py3-none-any.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d68ee64344dfaeb867f4c65c4ad758f6b9ff172f2c1329d11467f01a414ccbe
+size 425525969

meta.json

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+{
+  "lang":"en",
+  "name":"roberta_re_pipeline",
+  "version":"0.0.0",
+  "description":"",
+  "author":"",
+  "email":"",
+  "url":"",
+  "license":"",
+  "spacy_version":">=3.5.1,<3.6.0",
+  "spacy_git_version":"Unknown",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null
+  },
+  "labels":{
+    "transformer":[
+
+    ],
+    "relation_extractor":[
+      "DEGREE_IN",
+      "EXPERIENCE_IN"
+    ]
+  },
+  "pipeline":[
+    "transformer",
+    "relation_extractor"
+  ],
+  "components":[
+    "transformer",
+    "relation_extractor"
+  ],
+  "disabled":[
+
+  ],
+  "performance":{
+    "rel_micro_p":0.8356164384,
+    "rel_micro_r":0.6421052632,
+    "rel_micro_f":0.7261904762,
+    "transformer_loss":0.0021927262,
+    "relation_extractor_loss":1.5863276524
+  },
+  "requirements":[
+    "spacy-transformers>=1.2.2,<1.3.0"
+  ]
+}

rel_model.py

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+from typing import List, Tuple, Callable
+
+import spacy
+from spacy.tokens import Doc, Span
+from thinc.types import Floats2d, Ints1d, Ragged, cast
+from thinc.api import Model, Linear, chain, Logistic
+
+
+@spacy.registry.architectures("rel_model.v1")
+def create_relation_model(
+    create_instance_tensor: Model[List[Doc], Floats2d],
+    classification_layer: Model[Floats2d, Floats2d],
+) -> Model[List[Doc], Floats2d]:
+    with Model.define_operators({">>": chain}):
+        model = create_instance_tensor >> classification_layer
+        model.attrs["get_instances"] = create_instance_tensor.attrs["get_instances"]
+    return model
+
+
+@spacy.registry.architectures("rel_classification_layer.v1")
+def create_classification_layer(
+    nO: int = None, nI: int = None
+) -> Model[Floats2d, Floats2d]:
+    with Model.define_operators({">>": chain}):
+        return Linear(nO=nO, nI=nI) >> Logistic()
+
+
+@spacy.registry.misc("rel_instance_generator.v1")
+def create_instances(max_length: int) -> Callable[[Doc], List[Tuple[Span, Span]]]:
+    def get_instances(doc: Doc) -> List[Tuple[Span, Span]]:
+        instances = []
+        for ent1 in doc.ents:
+            for ent2 in doc.ents:
+                if ent1 != ent2:
+                    if max_length and abs(ent2.start - ent1.start) <= max_length:
+                        instances.append((ent1, ent2))
+        return instances
+
+    return get_instances
+
+
+@spacy.registry.architectures("rel_instance_tensor.v1")
+def create_tensors(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    pooling: Model[Ragged, Floats2d],
+    get_instances: Callable[[Doc], List[Tuple[Span, Span]]],
+) -> Model[List[Doc], Floats2d]:
+
+    return Model(
+        "instance_tensors",
+        instance_forward,
+        layers=[tok2vec, pooling],
+        refs={"tok2vec": tok2vec, "pooling": pooling},
+        attrs={"get_instances": get_instances},
+        init=instance_init,
+    )
+
+
+def instance_forward(model: Model[List[Doc], Floats2d], docs: List[Doc], is_train: bool) -> Tuple[Floats2d, Callable]:
+    pooling = model.get_ref("pooling")
+    tok2vec = model.get_ref("tok2vec")
+    get_instances = model.attrs["get_instances"]
+    all_instances = [get_instances(doc) for doc in docs]
+    tokvecs, bp_tokvecs = tok2vec(docs, is_train)
+
+    ents = []
+    lengths = []
+
+    for doc_nr, (instances, tokvec) in enumerate(zip(all_instances, tokvecs)):
+        token_indices = []
+        for instance in instances:
+            for ent in instance:
+                token_indices.extend([i for i in range(ent.start, ent.end)])
+                lengths.append(ent.end - ent.start)
+        ents.append(tokvec[token_indices])
+    lengths = cast(Ints1d, model.ops.asarray(lengths, dtype="int32"))
+    entities = Ragged(model.ops.flatten(ents), lengths)
+    pooled, bp_pooled = pooling(entities, is_train)
+
+    # Reshape so that pairs of rows are concatenated
+    relations = model.ops.reshape2f(pooled, -1, pooled.shape[1] * 2)
+
+    def backprop(d_relations: Floats2d) -> List[Doc]:
+        d_pooled = model.ops.reshape2f(d_relations, d_relations.shape[0] * 2, -1)
+        d_ents = bp_pooled(d_pooled).data
+        d_tokvecs = []
+        ent_index = 0
+        for doc_nr, instances in enumerate(all_instances):
+            shape = tokvecs[doc_nr].shape
+            d_tokvec = model.ops.alloc2f(*shape)
+            count_occ = model.ops.alloc2f(*shape)
+            for instance in instances:
+                for ent in instance:
+                    d_tokvec[ent.start : ent.end] += d_ents[ent_index]
+                    count_occ[ent.start : ent.end] += 1
+                    ent_index += ent.end - ent.start
+            d_tokvec /= count_occ + 0.00000000001
+            d_tokvecs.append(d_tokvec)
+
+        d_docs = bp_tokvecs(d_tokvecs)
+        return d_docs
+
+    return relations, backprop
+
+
+def instance_init(model: Model, X: List[Doc] = None, Y: Floats2d = None) -> Model:
+    tok2vec = model.get_ref("tok2vec")
+    if X is not None:
+        tok2vec.initialize(X)
+    return model

rel_pipe.py

@@ -0,0 +1,225 @@
+from itertools import islice
+from typing import Tuple, List, Iterable, Optional, Dict, Callable, Any
+
+from spacy.scorer import PRFScore
+from thinc.types import Floats2d
+import numpy
+from spacy.training.example import Example
+from thinc.api import Model, Optimizer
+from spacy.tokens.doc import Doc
+from spacy.pipeline.trainable_pipe import TrainablePipe
+from spacy.vocab import Vocab
+from spacy import Language
+from thinc.model import set_dropout_rate
+from wasabi import Printer
+
+
+Doc.set_extension("rel", default={}, force=True)
+msg = Printer()
+
+
+@Language.factory(
+    "relation_extractor",
+    requires=["doc.ents", "token.ent_iob", "token.ent_type"],
+    assigns=["doc._.rel"],
+    default_score_weights={
+        "rel_micro_p": None,
+        "rel_micro_r": None,
+        "rel_micro_f": None,
+    },
+)
+def make_relation_extractor(
+    nlp: Language, name: str, model: Model, *, threshold: float
+):
+    """Construct a RelationExtractor component."""
+    return RelationExtractor(nlp.vocab, model, name, threshold=threshold)
+
+
+class RelationExtractor(TrainablePipe):
+    def __init__(
+        self,
+        vocab: Vocab,
+        model: Model,
+        name: str = "rel",
+        *,
+        threshold: float,
+    ) -> None:
+        """Initialize a relation extractor."""
+        self.vocab = vocab
+        self.model = model
+        self.name = name
+        self.cfg = {"labels": [], "threshold": threshold}
+
+    @property
+    def labels(self) -> Tuple[str]:
+        """Returns the labels currently added to the component."""
+        return tuple(self.cfg["labels"])
+
+    @property
+    def threshold(self) -> float:
+        """Returns the threshold above which a prediction is seen as 'True'."""
+        return self.cfg["threshold"]
+
+    def add_label(self, label: str) -> int:
+        """Add a new label to the pipe."""
+        if not isinstance(label, str):
+            raise ValueError("Only strings can be added as labels to the RelationExtractor")
+        if label in self.labels:
+            return 0
+        self.cfg["labels"] = list(self.labels) + [label]
+        return 1
+
+    def __call__(self, doc: Doc) -> Doc:
+        """Apply the pipe to a Doc."""
+        # check that there are actually any candidate instances in this batch of examples
+        total_instances = len(self.model.attrs["get_instances"](doc))
+        if total_instances == 0:
+            msg.info("Could not determine any instances in doc - returning doc as is.")
+            return doc
+
+        predictions = self.predict([doc])
+        self.set_annotations([doc], predictions)
+        return doc
+
+    def predict(self, docs: Iterable[Doc]) -> Floats2d:
+        """Apply the pipeline's model to a batch of docs, without modifying them."""
+        get_instances = self.model.attrs["get_instances"]
+        total_instances = sum([len(get_instances(doc)) for doc in docs])
+        if total_instances == 0:
+            msg.info("Could not determine any instances in any docs - can not make any predictions.")
+        scores = self.model.predict(docs)
+        return self.model.ops.asarray(scores)
+
+    def set_annotations(self, docs: Iterable[Doc], scores: Floats2d) -> None:
+        """Modify a batch of `Doc` objects, using pre-computed scores."""
+        c = 0
+        get_instances = self.model.attrs["get_instances"]
+        for doc in docs:
+            for (e1, e2) in get_instances(doc):
+                offset = (e1.start, e2.start)
+                if offset not in doc._.rel:
+                    doc._.rel[offset] = {}
+                for j, label in enumerate(self.labels):
+                    doc._.rel[offset][label] = scores[c, j]
+                c += 1
+
+    def update(
+        self,
+        examples: Iterable[Example],
+        *,
+        drop: float = 0.0,
+        set_annotations: bool = False,
+        sgd: Optional[Optimizer] = None,
+        losses: Optional[Dict[str, float]] = None,
+    ) -> Dict[str, float]:
+        """Learn from a batch of documents and gold-standard information,
+        updating the pipe's model. Delegates to predict and get_loss."""
+        if losses is None:
+            losses = {}
+        losses.setdefault(self.name, 0.0)
+        set_dropout_rate(self.model, drop)
+
+        # check that there are actually any candidate instances in this batch of examples
+        total_instances = 0
+        for eg in examples:
+            total_instances += len(self.model.attrs["get_instances"](eg.predicted))
+        if total_instances == 0:
+            msg.info("Could not determine any instances in doc.")
+            return losses
+
+        # run the model
+        docs = [eg.predicted for eg in examples]
+        predictions, backprop = self.model.begin_update(docs)
+        loss, gradient = self.get_loss(examples, predictions)
+        backprop(gradient)
+        if sgd is not None:
+            self.model.finish_update(sgd)
+        losses[self.name] += loss
+        if set_annotations:
+            self.set_annotations(docs, predictions)
+        return losses
+
+    def get_loss(self, examples: Iterable[Example], scores) -> Tuple[float, float]:
+        """Find the loss and gradient of loss for the batch of documents and
+        their predicted scores."""
+        truths = self._examples_to_truth(examples)
+        gradient = scores - truths
+        mean_square_error = (gradient ** 2).sum(axis=1).mean()
+        return float(mean_square_error), gradient
+
+    def initialize(
+        self,
+        get_examples: Callable[[], Iterable[Example]],
+        *,
+        nlp: Language = None,
+        labels: Optional[List[str]] = None,
+    ):
+        """Initialize the pipe for training, using a representative set
+        of data examples.
+        """
+        if labels is not None:
+            for label in labels:
+                self.add_label(label)
+        else:
+            for example in get_examples():
+                relations = example.reference._.rel
+                for indices, label_dict in relations.items():
+                    for label in label_dict.keys():
+                        self.add_label(label)
+        self._require_labels()
+
+        subbatch = list(islice(get_examples(), 10))
+        doc_sample = [eg.reference for eg in subbatch]
+        label_sample = self._examples_to_truth(subbatch)
+        if label_sample is None:
+            raise ValueError("Call begin_training with relevant entities and relations annotated in "
+                             "at least a few reference examples!")
+        self.model.initialize(X=doc_sample, Y=label_sample)
+
+    def _examples_to_truth(self, examples: List[Example]) -> Optional[numpy.ndarray]:
+        # check that there are actually any candidate instances in this batch of examples
+        nr_instances = 0
+        for eg in examples:
+            nr_instances += len(self.model.attrs["get_instances"](eg.reference))
+        if nr_instances == 0:
+            return None
+
+        truths = numpy.zeros((nr_instances, len(self.labels)), dtype="f")
+        c = 0
+        for i, eg in enumerate(examples):
+            for (e1, e2) in self.model.attrs["get_instances"](eg.reference):
+                gold_label_dict = eg.reference._.rel.get((e1.start, e2.start), {})
+                for j, label in enumerate(self.labels):
+                    truths[c, j] = gold_label_dict.get(label, 0)
+                c += 1
+
+        truths = self.model.ops.asarray(truths)
+        return truths
+
+    def score(self, examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
+        """Score a batch of examples."""
+        return score_relations(examples, self.threshold)
+
+
+def score_relations(examples: Iterable[Example], threshold: float) -> Dict[str, Any]:
+    """Score a batch of examples."""
+    micro_prf = PRFScore()
+    for example in examples:
+        gold = example.reference._.rel
+        pred = example.predicted._.rel
+        for key, pred_dict in pred.items():
+            gold_labels = [k for (k, v) in gold[key].items() if v == 1.0]
+            for k, v in pred_dict.items():
+                if v >= threshold:
+                    if k in gold_labels:
+                        micro_prf.tp += 1
+                    else:
+                        micro_prf.fp += 1
+                else:
+                    if k in gold_labels:
+                        micro_prf.fn += 1
+    return {
+        "rel_micro_p": micro_prf.precision,
+        "rel_micro_r": micro_prf.recall,
+        "rel_micro_f": micro_prf.fscore,
+    }

relation_extractor/cfg

@@ -0,0 +1,7 @@
+{
+  "labels":[
+    "DEGREE_IN",
+    "EXPERIENCE_IN"
+  ],
+  "threshold":0.5
+}

tokenizer

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+  "\u00a0",
+  "\u00ac",
+  "\u00ac_\u00ac",
+  "\u00af",
+  "\u00af\\(x)/\u00af",
+  "\u00af\\(\u30c4)/\u00af",
+  "\u00b0",
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+  "\u00b0K.",
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+  "\u00b0f.",
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+  "\u00b0x.",
+  "\u00e4",
+  "\u00e4.",
+  "\u00f6",
+  "\u00f6.",
+  "\u00fc",
+  "\u00fc.",
+  "\u0ca0",
+  "\u0ca0_\u0ca0",
+  "\u0ca0\ufe35\u0ca0",
+  "\u2014",
+  "\u2018",
+  "\u2018S",
+  "\u2018X",
+  "\u2018s",
+  "\u2018x",
+  "\u2019",
+  "\u2019-(",
+  "\u2019-)",
+  "\u2019Cause",
+  "\u2019Cos",
+  "\u2019Coz",
+  "\u2019Cuz",
+  "\u2019S",
+  "\u2019X",
+  "\u2019Xxx",
+  "\u2019Xxxxx",
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+  "\u2019x",
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+  "\u2019xxx",
+  "\u2019xxxx",
+  "\u2019y",
+  "\u2019\u2019",
+  "\u2501",
+  "\u253b",
+  "\u253b\u2501\u253b",
+  "\u256f",
+  "\u25a1",
+  "\ufe35",
+  "\uff09"
+]

vocab/vectors.cfg

@@ -0,0 +1,3 @@
+{
+  "mode":"default"
+}