Update spaCy pipeline

.gitattributes

@@ -32,3 +32,7 @@ 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_engagement_Dual_RoBERTa_acad3_f4-any-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+spancat/model filter=lfs diff=lfs merge=lfs -text
+trainable_transformer/model filter=lfs diff=lfs merge=lfs -text
+transformer/model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,105 @@
+---
+tags:
+- spacy
+- token-classification
+language:
+- en
+model-index:
+- name: en_engagement_Dual_RoBERTa_acad3_f4
+  results:
+  - task:
+      name: NER
+      type: token-classification
+    metrics:
+    - name: NER Precision
+      type: precision
+      value: 0.0
+    - name: NER Recall
+      type: recall
+      value: 0.0
+    - name: NER F Score
+      type: f_score
+      value: 0.0
+  - task:
+      name: TAG
+      type: token-classification
+    metrics:
+    - name: TAG (XPOS) Accuracy
+      type: accuracy
+      value: 0.0
+  - task:
+      name: LEMMA
+      type: token-classification
+    metrics:
+    - name: Lemma Accuracy
+      type: accuracy
+      value: 0.0
+  - task:
+      name: UNLABELED_DEPENDENCIES
+      type: token-classification
+    metrics:
+    - name: Unlabeled Attachment Score (UAS)
+      type: f_score
+      value: 0.0
+  - task:
+      name: LABELED_DEPENDENCIES
+      type: token-classification
+    metrics:
+    - name: Labeled Attachment Score (LAS)
+      type: f_score
+      value: 0.0
+  - task:
+      name: SENTS
+      type: token-classification
+    metrics:
+    - name: Sentences F-Score
+      type: f_score
+      value: 0.8446808511
+---
+| Feature | Description |
+| --- | --- |
+| **Name** | `en_engagement_Dual_RoBERTa_acad3_f4` |
+| **Version** | `1.0.0` |
+| **spaCy** | `>=3.4.4,<3.5.0` |
+| **Default Pipeline** | `transformer`, `parser`, `tagger`, `ner`, `attribute_ruler`, `lemmatizer`, `trainable_transformer`, `spancat` |
+| **Components** | `transformer`, `parser`, `tagger`, `ner`, `attribute_ruler`, `lemmatizer`, `trainable_transformer`, `spancat` |
+| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
+| **Sources** | n/a |
+| **License** | n/a |
+| **Author** | [n/a]() |
+
+### Label Scheme
+
+<details>
+
+<summary>View label scheme (122 labels for 4 components)</summary>
+
+| Component | Labels |
+| --- | --- |
+| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
+| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
+| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
+| **`spancat`** | `MONOGLOSS`, `ATTRIBUTION`, `ENTERTAIN`, `PROCLAIM`, `JUSTIFYING`, `SOURCES`, `CITATION`, `COUNTER`, `DENY`, `ENDOPHORIC` |
+
+</details>
+
+### Accuracy
+
+| Type | Score |
+| --- | --- |
+| `DEP_UAS` | 0.00 |
+| `DEP_LAS` | 0.00 |
+| `DEP_LAS_PER_TYPE` | 0.00 |
+| `SENTS_P` | 80.73 |
+| `SENTS_R` | 88.57 |
+| `SENTS_F` | 84.47 |
+| `TAG_ACC` | 0.00 |
+| `ENTS_F` | 0.00 |
+| `ENTS_P` | 0.00 |
+| `ENTS_R` | 0.00 |
+| `LEMMA_ACC` | 0.00 |
+| `SPANS_SC_F` | 71.14 |
+| `SPANS_SC_P` | 71.74 |
+| `SPANS_SC_R` | 70.55 |
+| `TRAINABLE_TRANSFORMER_LOSS` | 359.10 |
+| `SPANCAT_LOSS` | 74753.57 |

config.cfg

@@ -0,0 +1,287 @@
+[paths]
+train = "data/engagement_three_train4.spacy"
+dev = "data/engagement_three_dev4.spacy"
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = "pytorch"
+seed = 0
+
+[nlp]
+lang = "en"
+pipeline = ["transformer","parser","tagger","ner","attribute_ruler","lemmatizer","trainable_transformer","spancat"]
+batch_size = 10
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.attribute_ruler]
+factory = "attribute_ruler"
+scorer = {"@scorers":"spacy.attribute_ruler_scorer.v1"}
+validate = false
+
+[components.lemmatizer]
+factory = "lemmatizer"
+mode = "rule"
+model = null
+overwrite = false
+scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
+
+[components.ner]
+factory = "ner"
+incorrect_spans_key = null
+moves = null
+scorer = {"@scorers":"spacy.ner_scorer.v1"}
+update_with_oracle_cut_size = 100
+
+[components.ner.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = false
+nO = null
+
+[components.ner.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+upstream = "transformer"
+pooling = {"@layers":"reduce_mean.v1"}
+
+[components.parser]
+factory = "parser"
+learn_tokens = false
+min_action_freq = 30
+moves = null
+scorer = {"@scorers":"spacy.parser_scorer.v1"}
+update_with_oracle_cut_size = 100
+
+[components.parser.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "parser"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = false
+nO = null
+
+[components.parser.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+upstream = "transformer"
+pooling = {"@layers":"reduce_mean.v1"}
+
+[components.spancat]
+factory = "spancat"
+max_positive = null
+scorer = {"@scorers":"spacy.spancat_scorer.v1"}
+spans_key = ${vars.spans_key}
+threshold = 0.5
+
+[components.spancat.model]
+@architectures = "Ensemble_SpanCategorizer.v2"
+LSTMhidden = 200
+LSTMdepth = 1
+LSTMdropout = 0.0
+
+[components.spancat.model.reducer1]
+@layers = "Mish_two_way_reducer.v2"
+depth = 2
+dropout = 0.2
+hidden_size = 256
+
+[components.spancat.model.reducer2]
+@layers = "Mish_mean_max_reducer.v1"
+depth = 1
+dropout = 0.4
+hidden_size = 128
+
+[components.spancat.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO = null
+nI = null
+
+[components.spancat.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "trainable_transformer"
+
+[components.spancat.model.tok2vec_trf]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "transformer"
+
+[components.spancat.suggester]
+@misc = "spacy-experimental.ngram_subtree_suggester.v1"
+sizes = [1,2,3,4,5,6,7,8,9,10,11,12]
+
+[components.tagger]
+factory = "tagger"
+neg_prefix = "!"
+overwrite = false
+scorer = {"@scorers":"spacy.tagger_scorer.v1"}
+
+[components.tagger.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.tagger.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+upstream = "transformer"
+pooling = {"@layers":"reduce_mean.v1"}
+
+[components.trainable_transformer]
+factory = "transformer"
+max_batch_items = 4096
+set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
+
+[components.trainable_transformer.model]
+name = "egumasa/roberta-base-academic3"
+@architectures = "spacy-transformers.TransformerModel.v1"
+
+[components.trainable_transformer.model.get_spans]
+@span_getters = "spacy-transformers.strided_spans.v1"
+window = 384
+stride = 288
+
+[components.trainable_transformer.model.tokenizer_config]
+use_fast = true
+
+[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 = 128
+stride = 96
+
+[components.transformer.model.grad_scaler_config]
+
+[components.transformer.model.tokenizer_config]
+use_fast = true
+
+[components.transformer.model.transformer_config]
+
+[corpora]
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+max_length = 2000
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[training]
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 4
+patience = 3000
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 200
+frozen_components = ["transformer","parser","tagger","ner","attribute_ruler","lemmatizer"]
+annotating_components = ["transformer","parser"]
+before_to_disk = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+discard_oversize = false
+tolerance = 0.2
+get_length = null
+
+[training.batcher.size]
+start = 500
+@schedules = "compounding.v1"
+stop = 1000
+compound = 1.0002
+t = 0.0
+
+[training.logger]
+@loggers = "spacy.WandbLogger.v4"
+project_name = "Spancat_5-fold"
+remove_config_values = ["paths.train","paths.dev","corpora.train.path","corpora.dev.path"]
+model_log_interval = null
+entity = "e-masaki0101"
+log_dataset_dir = null
+run_name = null
+log_best_dir = null
+log_latest_dir = null
+
+[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]
+initial_rate = 0.0000565344
+@schedules = "warmup_linear.v1"
+warmup_steps = 1000
+total_steps = 20000
+
+[training.score_weights]
+dep_uas = null
+dep_las = null
+dep_las_per_type = null
+sents_p = null
+sents_r = null
+sents_f = null
+tag_acc = null
+ents_f = null
+ents_p = null
+ents_r = null
+ents_per_type = null
+lemma_acc = null
+spans_sc_f = 0.5
+spans_sc_p = 0.0
+spans_sc_r = 0.5
+
+[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]
+
+[vars]
+spans_key = "sc"

custom_functions.py

@@ -0,0 +1,855 @@
+from functools import partial
+from pathlib import Path
+from typing import Iterable, Callable
+import spacy
+from spacy.training import Example
+from spacy.tokens import DocBin, Doc
+
+from typing import List, Tuple, cast
+from thinc.api import Model, with_getitem, chain, list2ragged, Logistic, clone, LayerNorm
+from thinc.api import Maxout, Mish, Linear, Gelu, concatenate, glorot_uniform_init, PyTorchLSTM, residual
+from thinc.api import reduce_mean, reduce_max, reduce_first, reduce_last, reduce_sum
+from thinc.types import Ragged, Floats2d
+
+from spacy.util import registry
+from spacy.tokens import Doc
+from spacy.ml.extract_spans import extract_spans
+
+# @registry.layers("spacy.LinearLogistic.v1")
+# def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
+#     """An output layer for multi-label classification. It uses a linear layer
+#     followed by a logistic activation.
+#     """
+#     return chain(Linear(nO=nO, nI=nI, init_W=glorot_uniform_init), Logistic())
+
+@registry.architectures("CustomSpanCategorizer.v2")
+def build_spancat_model(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    reducer1: Model[Ragged, Floats2d],
+    reducer2: Model[Ragged, Floats2d],
+    scorer: Model[Floats2d, Floats2d],
+) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+    """Build a span categorizer model, given a token-to-vector model, a
+    reducer model to map the sequence of vectors for each span down to a single
+    vector, and a scorer model to map the vectors to probabilities.
+    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+    reducer (Model[Ragged, Floats2d]): The reducer model.
+    scorer (Model[Floats2d, Floats2d]): The scorer model.
+    """
+    model = chain(
+        cast(
+            Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+            with_getitem(
+                0,
+                chain(tok2vec,
+                      cast(Model[List[Floats2d], Ragged], list2ragged()))),
+        ),
+        extract_spans(),
+        concatenate(reducer1, reducer2),
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("reducer1", reducer1)
+    model.set_ref("reducer2", reducer2)
+    model.set_ref("scorer", scorer)
+    return model
+
+
+@registry.architectures("LSTM_SpanCategorizer.v1")
+def build_spancat_LSTM_model(
+        tok2vec: Model[List[Doc], List[Floats2d]],
+        reducer: Model[Ragged, Floats2d],
+        scorer: Model[Floats2d, Floats2d],
+        LSTMdepth: int = 2,
+        LSTMdropout: float = 0.0,
+        LSTMhidden: int = 200) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+    """Build a span categorizer model, given a token-to-vector model, a
+    reducer model to map the sequence of vectors for each span down to a single
+    vector, and a scorer model to map the vectors to probabilities.
+    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+    reducer (Model[Ragged, Floats2d]): The reducer model.
+    scorer (Model[Floats2d, Floats2d]): The scorer model.
+    """
+    embedding = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec,
+                PyTorchLSTM(nI=768,
+                            nO=LSTMhidden,
+                            bi=True,
+                            depth=LSTMdepth,
+                            dropout=LSTMdropout),
+                cast(Model[List[Floats2d], Ragged], list2ragged()))))
+    # LSTM = PyTorchLSTM(nO = None, nI= None, bi = True, depth = LSTMdepth, dropout = LSTMdropout)
+
+    model = chain(
+        embedding,
+        extract_spans(),
+        reducer,
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("reducer", reducer)
+    model.set_ref("scorer", scorer)
+    return model
+
+@registry.architectures("LSTM_SpanCategorizer.v1.1")
+def build_spancat_LSTM_model(
+        tok2vec: Model[List[Doc], List[Floats2d]],
+        reducer: Model[Ragged, Floats2d],
+        scorer: Model[Floats2d, Floats2d],
+        lstmdepth: int = 2,
+        lstmdropout: float = 0.0,
+        lstmhidden: int = 200) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+    """Build a span categorizer model, given a token-to-vector model, a
+    reducer model to map the sequence of vectors for each span down to a single
+    vector, and a scorer model to map the vectors to probabilities.
+    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+    reducer (Model[Ragged, Floats2d]): The reducer model.
+    scorer (Model[Floats2d, Floats2d]): The scorer model.
+    """
+    embedding = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec,
+                PyTorchLSTM(nI=768,
+                            nO=lstmhidden,
+                            bi=True,
+                            depth=lstmdepth,
+                            dropout=lstmdropout),
+                cast(Model[List[Floats2d], Ragged], list2ragged()))))
+
+    model = chain(
+        embedding,
+        extract_spans(),
+        reducer,
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("reducer", reducer)
+    model.set_ref("scorer", scorer)
+    return model
+
+
+
+# @registry.architectures("LSTM_SpanCategorizer.v2")
+# def build_spancat_LSTM_model(
+#         tok2vec: Model[List[Doc], List[Floats2d]],
+#         reducer: Model[Ragged, Floats2d],
+#         scorer: Model[Floats2d, Floats2d],
+#         LSTMdepth: int = 2,
+#         LSTMdropout: float = 0.0,
+#         LSTMhidden: int = 200) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+#     """Build a span categorizer model, given a token-to-vector model, a
+#     reducer model to map the sequence of vectors for each span down to a single
+#     vector, and a scorer model to map the vectors to probabilities.
+#     tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+#     reducer (Model[Ragged, Floats2d]): The reducer model.
+#     scorer (Model[Floats2d, Floats2d]): The scorer model.
+#     """
+#     embedding = cast(
+#         Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+#         with_getitem(
+#             0,
+#             chain(
+#                 tok2vec,
+#                 cast(Model[List[Floats2d], Ragged], list2ragged()))))
+    
+#     lstm_layer = PyTorchLSTM(nO = LSTMhidden, nI= 768, bi = True, depth = LSTMdepth, dropout = LSTMdropout)
+
+#     model = chain(
+#         embedding,
+#         lstm_layer,
+#         extract_spans(),
+#         reducer,
+#         scorer,
+#     )
+#     model.set_ref("tok2vec", tok2vec)
+#     model.set_ref("reducer", reducer)
+#     model.set_ref("scorer", scorer)
+#     return model
+
+
+
+@registry.architectures("Ensemble_SpanCategorizer.v1")
+def build_dual_transformer_model(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    tok2vec_trf: Model[List[Doc], List[Floats2d]],
+    reducer1: Model[Ragged, Floats2d],
+    reducer2: Model[Ragged, Floats2d],
+    scorer: Model[Floats2d, Floats2d],
+    LSTMhidden: int = 200,
+    LSTMdepth: int = 1,
+    LSTMdropout: float = 0.0,
+    lstmhidden: int = 200,
+    lstmdepth: int = 1,
+    lstmdropout: float = 0.0,
+) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+    """Build a span categorizer model, given a token-to-vector model, a
+    reducer model to map the sequence of vectors for each span down to a single
+    vector, and a scorer model to map the vectors to probabilities.
+    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+    reducer (Model[Ragged, Floats2d]): The reducer model.
+    scorer (Model[Floats2d, Floats2d]): The scorer model.
+    """
+    trainable_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(tok2vec, cast(Model[List[Floats2d], Ragged],
+                                list2ragged()))),
+    )
+    en_core_web_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(tok2vec_trf,
+                  cast(Model[List[Floats2d], Ragged], list2ragged()))),
+    )
+    reduce_trainable = chain(trainable_trf, extract_spans(), reducer1)
+    reduce_default = chain(en_core_web_trf, extract_spans(), reducer2)
+    model = chain(
+        concatenate(reduce_trainable, reduce_default),
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("tok2vec_trf", tok2vec_trf)
+    model.set_ref("reducer1", reducer1)
+    model.set_ref("reducer2", reducer2)
+    model.set_ref("scorer", scorer)
+    return model
+
+
+@registry.architectures("Ensemble_SpanCategorizer.v2")
+def build_dual_transformer_model2(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    tok2vec_trf: Model[List[Doc], List[Floats2d]],
+    reducer1: Model[Ragged, Floats2d],
+    reducer2: Model[Ragged, Floats2d],
+    scorer: Model[Floats2d, Floats2d],
+    LSTMhidden: int = 200,
+    LSTMdepth: int = 1,
+    LSTMdropout: float = 0.0,
+    lstmhidden: int = 200,
+    lstmdepth: int = 1,
+    lstmdropout: float = 0.0,
+) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+    """Build a span categorizer model, given a token-to-vector model, a
+    reducer model to map the sequence of vectors for each span down to a single
+    vector, and a scorer model to map the vectors to probabilities.
+    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+    reducer (Model[Ragged, Floats2d]): The reducer model.
+    scorer (Model[Floats2d, Floats2d]): The scorer model.
+    """
+    trainable_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(tok2vec, cast(Model[List[Floats2d], Ragged],
+                                list2ragged()))),
+    )
+    en_core_web_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec_trf,
+                PyTorchLSTM(nI=768,
+                            nO=lstmhidden,
+                            bi=True,
+                            depth=lstmdepth,
+                            dropout=lstmdropout),
+                cast(Model[List[Floats2d], Ragged], list2ragged()))),
+    )
+    reduce_trainable = chain(trainable_trf, extract_spans(), reducer1)
+    reduce_default = chain(en_core_web_trf, extract_spans(), reducer2)
+    model = chain(
+        concatenate(reduce_trainable, reduce_default),
+        # Mish(),
+        # LayerNorm(),
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("tok2vec_trf", tok2vec_trf)
+    model.set_ref("reducer1", reducer1)
+    model.set_ref("reducer2", reducer2)
+    model.set_ref("scorer", scorer)
+    return model
+
+
+@registry.architectures("Ensemble_SpanCategorizer.v3")
+def build_dual_transformer_model3(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    tok2vec_trf: Model[List[Doc], List[Floats2d]],
+    reducer1: Model[Ragged, Floats2d],
+    reducer2: Model[Ragged, Floats2d],
+    scorer: Model[Floats2d, Floats2d],
+    LSTMhidden: int = 200,
+    LSTMdepth: int = 1,
+    LSTMdropout: float = 0.0,
+    lstmhidden: int = 200,
+    lstmdepth: int = 1,
+    lstmdropout: float = 0.0,
+) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+    """Build a span categorizer model, given a token-to-vector model, a
+    reducer model to map the sequence of vectors for each span down to a single
+    vector, and a scorer model to map the vectors to probabilities.
+    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+    reducer (Model[Ragged, Floats2d]): The reducer model.
+    scorer (Model[Floats2d, Floats2d]): The scorer model.
+    """
+    en_core_web_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(tok2vec_trf, cast(Model[List[Floats2d], Ragged],
+                                list2ragged()))),
+    )
+    trainable_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec,
+                PyTorchLSTM(nI=768,
+                            nO=lstmhidden,
+                            bi=True,
+                            depth=lstmdepth,
+                            dropout=lstmdropout),
+                cast(Model[List[Floats2d], Ragged], list2ragged()))),
+    )
+    reduce_trainable = chain(trainable_trf, extract_spans(), reducer1)
+    reduce_default = chain(en_core_web_trf, extract_spans(), reducer2)
+    model = chain(
+        concatenate(reduce_trainable, reduce_default),
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("tok2vec_trf", tok2vec_trf)
+    model.set_ref("reducer1", reducer1)
+    model.set_ref("reducer2", reducer2)
+    model.set_ref("scorer", scorer)
+    return model
+
+@registry.architectures("Ensemble_SpanCategorizer.v4")
+def build_dual_transformer_model4(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    tok2vec_trf: Model[List[Doc], List[Floats2d]],
+    reducer1: Model[Ragged, Floats2d],
+    reducer2: Model[Ragged, Floats2d],
+    scorer: Model[Floats2d, Floats2d],
+    LSTMhidden: int = 200,
+    LSTMdepth: int = 1,
+    LSTMdropout: float = 0.0,
+    lstmhidden: int = 200,
+    lstmdepth: int = 1,
+    lstmdropout: float = 0.0,
+) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+    """Build a span categorizer model, given a token-to-vector model, a
+    reducer model to map the sequence of vectors for each span down to a single
+    vector, and a scorer model to map the vectors to probabilities.
+    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+    reducer (Model[Ragged, Floats2d]): The reducer model.
+    scorer (Model[Floats2d, Floats2d]): The scorer model.
+    """
+    trainable_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(tok2vec, cast(Model[List[Floats2d], Ragged],
+                                list2ragged()))),
+    )
+    en_core_web_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec_trf,
+                PyTorchLSTM(nI=768,
+                            nO=lstmhidden,
+                            bi=True,
+                            depth=lstmdepth,
+                            dropout=lstmdropout),
+                cast(Model[List[Floats2d], Ragged], list2ragged()))),
+    )
+    reduce_trainable = chain(trainable_trf, extract_spans(), reducer1)
+    reduce_default = chain(en_core_web_trf, extract_spans(), reducer2)
+    model = chain(
+        concatenate(reduce_trainable, reduce_default),
+        Mish(nO = 128),
+        LayerNorm(),
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("tok2vec_trf", tok2vec_trf)
+    model.set_ref("reducer1", reducer1)
+    model.set_ref("reducer2", reducer2)
+    model.set_ref("scorer", scorer)
+    return model
+
+
+@registry.architectures("Ensemble_SpanCategorizer.v5")
+def build_dual_transformer_model3(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    tok2vec_trf: Model[List[Doc], List[Floats2d]],
+    reducer1: Model[Ragged, Floats2d],
+    reducer2: Model[Ragged, Floats2d],
+    scorer: Model[Floats2d, Floats2d],
+    LSTMhidden: int = 200,
+    LSTMdepth: int = 1,
+    LSTMdropout: float = 0.0,
+    lstmhidden: int = 200,
+    lstmdepth: int = 1,
+    lstmdropout: float = 0.0,
+) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
+    """Build a span categorizer model, given a token-to-vector model, a
+    reducer model to map the sequence of vectors for each span down to a single
+    vector, and a scorer model to map the vectors to probabilities.
+    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
+    reducer (Model[Ragged, Floats2d]): The reducer model.
+    scorer (Model[Floats2d, Floats2d]): The scorer model.
+    """
+    en_core_web_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(tok2vec_trf, 
+                PyTorchLSTM(nI=768,
+                            nO=lstmhidden,
+                            bi=True,
+                            depth=lstmdepth,
+                            dropout=lstmdropout),
+                  cast(Model[List[Floats2d], Ragged],
+                                list2ragged()))),
+    )
+    trainable_trf = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec,
+                PyTorchLSTM(nI=768,
+                            nO=lstmhidden,
+                            bi=True,
+                            depth=lstmdepth,
+                            dropout=lstmdropout),
+                cast(Model[List[Floats2d], Ragged], list2ragged()))),
+    )
+    reduce_trainable = chain(trainable_trf, extract_spans(), reducer1)
+    reduce_default = chain(en_core_web_trf, extract_spans(), reducer2)
+    model = chain(
+        concatenate(reduce_trainable, reduce_default),
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("tok2vec_trf", tok2vec_trf)
+    model.set_ref("reducer1", reducer1)
+    model.set_ref("reducer2", reducer2)
+    model.set_ref("scorer", scorer)
+    return model
+
+
+
+@registry.layers("mean_max_reducer.v1.5")
+def build_mean_max_reducer1(hidden_size: int,
+                            dropout: float = 0.0,
+                            depth: int = 1) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    return chain(
+        concatenate(
+            cast(Model[Ragged, Floats2d], reduce_last()),
+            cast(Model[Ragged, Floats2d], reduce_first()),
+            reduce_mean(),
+            reduce_max(),
+        ),
+        clone(Maxout(nO=hidden_size, normalize=True, dropout=dropout), depth),
+    )
+
+
+# @registry.layers("mean_max_reducer.v2")
+# def build_mean_max_reducer2(hidden_size: int,
+#                             dropout: float = 0.0) -> Model[Ragged, Floats2d]:
+#     """Reduce sequences by concatenating their mean and max pooled vectors,
+#     and then combine the concatenated vectors with a hidden layer.
+#     """
+#     return chain(
+#         concatenate(
+#             cast(Model[Ragged, Floats2d], reduce_last()),
+#             cast(Model[Ragged, Floats2d], reduce_first()),
+#             reduce_mean(),
+#             reduce_mean(),
+#             reduce_max(),
+#         ),
+#         Maxout(nO=hidden_size, normalize=True, dropout=dropout),
+#         )
+
+
+@registry.layers("Gelu_mean_max_reducer.v1")
+def build_mean_max_reducer_gelu(hidden_size: int,
+                                dropout: float = 0.0,
+                                depth: int = 1) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    gelu_unit = Gelu(nO=hidden_size, normalize=True, dropout=dropout)
+    return chain(
+        concatenate(
+            cast(Model[Ragged, Floats2d], reduce_last()),
+            cast(Model[Ragged, Floats2d], reduce_first()),
+            reduce_mean(),
+            reduce_max(),
+        ),
+        clone(gelu_unit, depth),
+    )
+
+
+@registry.layers("Mish_mean_max_reducer.v1")
+def build_mean_max_reducer3(hidden_size: int,
+                            dropout: float = 0.0,
+                            depth: int = 4) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    mish_unit = Mish(nO=hidden_size, normalize=True, dropout=dropout)
+    return chain(
+        concatenate(
+            cast(Model[Ragged, Floats2d], reduce_last()),
+            cast(Model[Ragged, Floats2d], reduce_first()),
+            reduce_mean(),
+            reduce_max(),
+        ),
+        clone(mish_unit, depth),
+    )
+
+@registry.layers("Maxout_mean_max_reducer.v2")
+def build_mean_max_reducer3(hidden_size: int,
+                            dropout: float = 0.0,
+                            depth: int = 4) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    maxout_unit = Maxout(nO=hidden_size, normalize=True, dropout=dropout)
+    return chain(
+        concatenate(
+            cast(Model[Ragged, Floats2d], reduce_last()),
+            cast(Model[Ragged, Floats2d], reduce_first()),
+            reduce_mean(),
+            reduce_max(),
+        ),
+        clone(maxout_unit, depth),
+    )
+
+@registry.layers("mean_max_reducer.v2")
+def build_mean_max_reducer2(hidden_size: int,
+                            dropout: float = 0.0) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    return chain(
+        concatenate(
+            cast(Model[Ragged, Floats2d], reduce_last()),
+            cast(Model[Ragged, Floats2d], reduce_first()),
+            reduce_mean(),
+            reduce_mean(),
+            reduce_max(),
+        ), 
+        Maxout(nO=hidden_size, normalize=True, dropout=dropout),
+        )
+
+@registry.layers("two_way_reducer.v1")
+def build_two_way_reducer(hidden_size: int,
+                          dropout: float = 0.0) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    default_reducer = concatenate(
+        cast(Model[Ragged, Floats2d], reduce_last()),
+        cast(Model[Ragged, Floats2d], reduce_first()),
+        reduce_mean(),
+        reduce_max(),
+    )
+    mean_sum_reducer = concatenate(reduce_mean(), reduce_sum())
+
+    return concatenate(
+        chain(default_reducer,
+              Maxout(nO=hidden_size, normalize=True, dropout=dropout)),
+        chain(mean_sum_reducer,
+              Maxout(nO=hidden_size // 2, normalize=True, dropout=dropout)))
+
+
+@registry.layers("Mish_two_way_reducer.v1")
+def build_Mish_two_way_reducer(hidden_size: int,
+                               dropout: float = 0.0,
+                               depth: int = 1) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    default_reducer = concatenate(
+        cast(Model[Ragged, Floats2d], reduce_last()),
+        cast(Model[Ragged, Floats2d], reduce_first()),
+        reduce_mean(),
+        reduce_max(),
+    )
+    mean_sum_reducer = concatenate(reduce_mean(), reduce_sum())
+
+    return concatenate(
+        chain(
+            default_reducer,
+            clone(Mish(nO=hidden_size // 2, normalize=True, dropout=dropout),
+                  depth)),
+        chain(
+            mean_sum_reducer,
+            clone(Mish(nO=hidden_size // 2, normalize=True, dropout=dropout),
+                  depth)))
+
+@registry.layers("Mish_two_way_reducer.v2")
+def build_Mish_two_way_reducer2(hidden_size: int,
+                               dropout: float = 0.0,
+                               depth: int = 1) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    default_reducer = concatenate(
+        cast(Model[Ragged, Floats2d], reduce_last()),
+        cast(Model[Ragged, Floats2d], reduce_first()),
+        reduce_mean(),
+        reduce_max(),
+    )
+    mean_sum_reducer = concatenate(
+        cast(Model[Ragged, Floats2d], reduce_last()),
+        cast(Model[Ragged, Floats2d], reduce_first()),
+        reduce_mean(),
+        reduce_sum(),
+    )
+
+    return concatenate(
+        chain(
+            default_reducer,
+            clone(Mish(nO=hidden_size // 2, normalize=True, dropout=dropout),
+                  depth)),
+        chain(
+            mean_sum_reducer,
+            clone(Mish(nO=hidden_size // 2, normalize=True, dropout=dropout),
+                  depth)))
+
+@registry.layers("Mish_two_way_reducer.v3")
+def build_Mish_two_way_reducer3(hidden_size: int,
+                               dropout: float = 0.0,
+                               depth: int = 1) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    default_reducer = concatenate(
+        cast(Model[Ragged, Floats2d], reduce_last()),
+        cast(Model[Ragged, Floats2d], reduce_first()),
+        reduce_mean(),
+        reduce_max(),
+    )
+    mean_sum_reducer = concatenate(
+        cast(Model[Ragged, Floats2d], reduce_last()),
+        cast(Model[Ragged, Floats2d], reduce_first()),
+        reduce_max(),
+    )
+
+    return concatenate(
+        chain(
+            default_reducer,
+            clone(Mish(nO=hidden_size // 2, normalize=True, dropout=dropout),
+                  depth)),
+        chain(
+            mean_sum_reducer,
+            clone(Mish(nO=hidden_size // 2, normalize=True, dropout=dropout),
+                  depth)))
+
+
+
+@registry.layers("three_way_reducer.v3")
+def build_mean_max_reducer2(hidden_size: int,
+                            dropout: float = 0.0,
+                            depth: int = 2) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    default_reducer = concatenate(
+                cast(Model[Ragged, Floats2d], reduce_last()),
+                cast(Model[Ragged, Floats2d], reduce_first()),
+                reduce_mean(),
+                reduce_max(),
+            )
+    mean_sum_reducer = concatenate(
+            reduce_mean(),
+            reduce_sum())
+
+    return concatenate(chain(default_reducer,
+                        Maxout(nO=hidden_size, normalize=True, dropout=dropout)),
+                       chain(mean_sum_reducer,
+                        Maxout(nO=hidden_size//2, normalize=True, dropout=dropout)),
+                       chain(mean_sum_reducer,
+                        clone(Maxout(nO=hidden_size//2, normalize=True, dropout=dropout),depth))
+                    )
+
+@registry.layers("Maxout_three_way_reducer.v1")
+def build_Maxout_three_way_reducer(hidden_size: int,
+                                   dropout: float = 0.0,
+                                   depth: int = 2) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    default_reducer = concatenate(
+        cast(Model[Ragged, Floats2d], reduce_last()),
+        cast(Model[Ragged, Floats2d], reduce_first()),
+        reduce_mean(),
+        reduce_max(),
+    )
+    mean_sum_reducer = concatenate(reduce_mean(), reduce_sum())
+
+    return concatenate(
+        chain(
+            default_reducer,
+            clone(Maxout(nO=hidden_size // 2, normalize=True, dropout=dropout),
+                  depth)),
+        chain(mean_sum_reducer,
+              Maxout(nO=hidden_size // 4, normalize=True, dropout=dropout)),
+        chain(
+            mean_sum_reducer,
+            clone(Maxout(nO=hidden_size // 4, normalize=True, dropout=dropout),
+                  depth)))
+
+
+@registry.layers("Mish_three_way_reducer.v1")
+def build_Mish_three_way_reducer(hidden_size: int,
+                                 dropout: float = 0.0,
+                                 depth: int = 2) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    default_reducer = concatenate(
+        cast(Model[Ragged, Floats2d], reduce_last()),
+        cast(Model[Ragged, Floats2d], reduce_first()),
+        reduce_mean(),
+        reduce_max(),
+    )
+    mean_sum_reducer = concatenate(reduce_mean(), reduce_sum())
+
+    return concatenate(
+        chain(
+            default_reducer,
+            clone(Mish(nO=hidden_size // 2, normalize=True, dropout=dropout),
+                  depth)),
+        chain(mean_sum_reducer,
+              Mish(nO=hidden_size // 4, normalize=True, dropout=dropout)),
+        chain(
+            mean_sum_reducer,
+            clone(Mish(nO=hidden_size // 4, normalize=True, dropout=dropout),
+                  depth)))
+
+
+@registry.layers("mean_max_reducer.v4")
+def build_mean_max_reducer3(hidden_size: int,
+                            maxout_pieces: int = 3,
+                            dropout: float = 0.0) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    hidden_size2 = int(hidden_size / 2)
+    hidden_size3 = int(hidden_size / 2)
+    return chain(
+        concatenate(
+            cast(Model[Ragged, Floats2d], reduce_last()),
+            cast(Model[Ragged, Floats2d], reduce_first()),
+            reduce_mean(),
+            reduce_max(),
+        ),
+        Maxout(nO=hidden_size,
+               nP=maxout_pieces,
+               normalize=True,
+               dropout=dropout),
+        Maxout(nO=hidden_size2,
+               nP=maxout_pieces,
+               normalize=True,
+               dropout=dropout),
+        Maxout(nO=hidden_size3,
+               nP=maxout_pieces,
+               normalize=True,
+               dropout=dropout))
+
+
+@registry.layers("mean_max_reducer.v3.3")
+def build_mean_max_reducer4(hidden_size: int,
+                            depth: int) -> Model[Ragged, Floats2d]:
+    """Reduce sequences by concatenating their mean and max pooled vectors,
+    and then combine the concatenated vectors with a hidden layer.
+    """
+    hidden_size2 = int(hidden_size / 2)
+    hidden_size3 = int(hidden_size / 2)
+    return chain(
+        concatenate(
+            cast(Model[Ragged, Floats2d], reduce_last()),
+            cast(Model[Ragged, Floats2d], reduce_first()),
+            reduce_mean(),
+            reduce_max(),
+        ), Maxout(nO=hidden_size, nP=3, normalize=True, dropout=0.0),
+        Maxout(nO=hidden_size2, nP=3, normalize=True, dropout=0.0),
+        Maxout(nO=hidden_size3, nP=3, normalize=True, dropout=0.0))
+
+
+# @registry.architectures("spacy.MaxoutWindowEncoder.v2")
+# def MaxoutWindowEncoder(
+#     width: int, window_size: int, maxout_pieces: int, depth: int
+# ) -> Model[List[Floats2d], List[Floats2d]]:
+#     """Encode context using convolutions with maxout activation, layer
+#     normalization and residual connections.
+#     width (int): The input and output width. These are required to be the same,
+#         to allow residual connections. This value will be determined by the
+#         width of the inputs. Recommended values are between 64 and 300.
+#     window_size (int): The number of words to concatenate around each token
+#         to construct the convolution. Recommended value is 1.
+#     maxout_pieces (int): The number of maxout pieces to use. Recommended
+#         values are 2 or 3.
+#     depth (int): The number of convolutional layers. Recommended value is 4.
+#     """
+#     cnn = chain(
+#         expand_window(window_size=window_size),
+#         Maxout(
+#             nO=width,
+#             nI=width * ((window_size * 2) + 1),
+#             nP=maxout_pieces,
+#             dropout=0.0,
+#             normalize=True,
+#         ),
+#     )
+#     model = clone(residual(cnn), depth)
+#     model.set_dim("nO", width)
+#     receptive_field = window_size * depth
+#     return with_array(model, pad=receptive_field)
+
+
+# @registry.architectures("spacy.MishWindowEncoder.v2")
+# def MishWindowEncoder(
+#     width: int, window_size: int, depth: int
+# ) -> Model[List[Floats2d], List[Floats2d]]:
+#     """Encode context using convolutions with mish activation, layer
+#     normalization and residual connections.
+#     width (int): The input and output width. These are required to be the same,
+#         to allow residual connections. This value will be determined by the
+#         width of the inputs. Recommended values are between 64 and 300.
+#     window_size (int): The number of words to concatenate around each token
+#         to construct the convolution. Recommended value is 1.
+#     depth (int): The number of convolutional layers. Recommended value is 4.
+#     """
+#     cnn = chain(
+#         expand_window(window_size=window_size),
+#         Mish(nO=width, nI=width * ((window_size * 2) + 1), dropout=0.0, normalize=True),
+#     )
+#     model = clone(residual(cnn), depth)
+#     model.set_dim("nO", width)
+#     return with_array(model)
+
+
+

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meta.json

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+{
+  "lang":"en",
+  "name":"engagement_Dual_RoBERTa_acad3_f4",
+  "version":"1.0.0",
+  "description":"",
+  "author":"",
+  "email":"",
+  "url":"",
+  "license":"",
+  "spacy_version":">=3.4.4,<3.5.0",
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+      "nsubjpass",
+      "nummod",
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+      "preconj",
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+      "WRB",
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+      "``"
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+    "ner":[
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+      "DATE",
+      "EVENT",
+      "FAC",
+      "GPE",
+      "LANGUAGE",
+      "LAW",
+      "LOC",
+      "MONEY",
+      "NORP",
+      "ORDINAL",
+      "ORG",
+      "PERCENT",
+      "PERSON",
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