Update spaCy pipeline

.gitattributes

@@ -33,3 +33,7 @@ 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_spl_RoBERTa_base_attention-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_spl_RoBERTa_base_attention
+  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: 0.9469411424
+---
+| Feature | Description |
+| --- | --- |
+| **Name** | `en_engagement_spl_RoBERTa_base_attention` |
+| **Version** | `0.0.1` |
+| **spaCy** | `>=3.6.0,<3.7.0` |
+| **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`** | `ATTRIBUTION`, `ENTERTAIN`, `PROCLAIM`, `SOURCES`, `MONOGLOSS`, `CITATION`, `ENDOPHORIC`, `DENY`, `JUSTIFYING`, `COUNTER` |
+
+</details>
+
+### Accuracy
+
+| Type | Score |
+| --- | --- |
+| `DEP_UAS` | 0.00 |
+| `DEP_LAS` | 0.00 |
+| `DEP_LAS_PER_TYPE` | 0.00 |
+| `SENTS_P` | 93.64 |
+| `SENTS_R` | 95.78 |
+| `SENTS_F` | 94.69 |
+| `TAG_ACC` | 0.00 |
+| `ENTS_F` | 0.00 |
+| `ENTS_P` | 0.00 |
+| `ENTS_R` | 0.00 |
+| `LEMMA_ACC` | 0.00 |
+| `SPANS_SC_F` | 77.65 |
+| `SPANS_SC_P` | 78.19 |
+| `SPANS_SC_R` | 77.12 |
+| `TRAINABLE_TRANSFORMER_LOSS` | 5917.79 |
+| `SPANCAT_LOSS` | 76188.74 |

config.cfg

@@ -0,0 +1,265 @@
+[paths]
+train = "data/engagement_spl_train.spacy"
+dev = "data/engagement_spl_dev.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 = 32
+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 = "Attention_SpanCategorizer.v3"
+
+[components.spancat.model.reducer]
+@layers = "spacy.mean_max_reducer.v1"
+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.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"
+label_smoothing = 0.0
+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]
+@architectures = "spacy-transformers.TransformerModel.v1"
+name = "roberta-base"
+
+[components.trainable_transformer.model.get_spans]
+@span_getters = "spacy-transformers.strided_spans.v1"
+window = 128
+stride = 96
+
+[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]
+name = "roberta-base"
+@architectures = "spacy-transformers.TransformerModel.v3"
+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 = ["parser"]
+before_to_disk = null
+before_update = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+discard_oversize = false
+tolerance = 0.2
+get_length = null
+
+[training.batcher.size]
+@schedules = "compounding.v1"
+start = 500
+stop = 1000
+compound = 1.0002
+t = 0.0
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = true
+
+[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 = 1000
+total_steps = 20000
+initial_rate = 0.00003
+
+[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 +1,1117 @@
+from functools import partial
+from pathlib import Path
+from typing import Iterable, Callable, Optional
+import spacy
+from spacy.training import Example
+from spacy.tokens import DocBin, Doc
+
+from typing import List, Tuple, cast
+from thinc.layers.chain import init as init_chain
+from thinc.api import Model, with_getitem, chain, list2ragged, Logistic, Softmax, clone, LayerNorm, ParametricAttention, Dropout
+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
+
+## For initializing parametric attention
+from spacy.ml.models.tok2vec import get_tok2vec_width
+# @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())
+
+
+# from typing import Callable, Optional, Tuple, cast
+
+# from thinc.config import registry
+# from thinc.model import Model
+# from thinc.types import Floats2d, Ragged
+# from thinc.util import get_width
+# from thinc.layers.noop import noop
+
+# InT = Ragged
+# OutT = Ragged
+
+# KEY_TRANSFORM_REF: str = "key_transform"
+
+# # @registry.layers("ParametricAttention.v2")
+# def ParametricAttention_v2(
+
+#     key_transform: Optional[Model[Floats2d, Floats2d]] = None,
+#     nO: Optional[int] = None
+# ) -> Model[InT, OutT]:
+#     if key_transform is None:
+#         key_transform = noop()
+
+#     """Weight inputs by similarity to a learned vector"""
+#     return Model(
+#         "para-attn",
+#         forward,
+#         init=init,
+#         params={"Q": None},
+#         dims={"nO": nO},
+#         refs={KEY_TRANSFORM_REF: key_transform},
+#         layers=[key_transform],
+#     )
+
+
+# def forward(model: Model[InT, OutT], Xr: InT, is_train: bool) -> Tuple[OutT, Callable]:
+#     Q = model.get_param("Q")
+#     key_transform = model.get_ref(KEY_TRANSFORM_REF)
+
+#     attention, bp_attention = _get_attention(
+#         model.ops, Q, key_transform, Xr.dataXd, Xr.lengths, is_train
+#     )
+#     output, bp_output = _apply_attention(model.ops, attention, Xr.dataXd, Xr.lengths)
+
+#     def backprop(dYr: OutT) -> InT:
+#         dX, d_attention = bp_output(dYr.dataXd)
+#         dQ, dX2 = bp_attention(d_attention)
+#         model.inc_grad("Q", dQ.ravel())
+#         dX += dX2
+#         return Ragged(dX, dYr.lengths)
+
+#     return Ragged(output, Xr.lengths), backprop
+
+
+# def init(
+#     model: Model[InT, OutT], X: Optional[InT] = None, Y: Optional[OutT] = None
+# ) -> None:
+#     key_transform = model.get_ref(KEY_TRANSFORM_REF)
+#     width = get_width(X) if X is not None else None
+#     if width:
+#         model.set_dim("nO", width)
+#         if key_transform.has_dim("nO"):
+#             key_transform.set_dim("nO", width)
+
+#     # Randomly initialize the parameter, as though it were an embedding.
+#     Q = model.ops.alloc1f(model.get_dim("nO"))
+#     Q += model.ops.xp.random.uniform(-0.1, 0.1, Q.shape)
+#     model.set_param("Q", Q)
+
+#     X_array = X.dataXd if X is not None else None
+#     Y_array = Y.dataXd if Y is not None else None
+
+#     key_transform.initialize(X_array, Y_array)
+
+
+# def _get_attention(ops, Q, key_transform, X, lengths, is_train):
+#     K, K_bp = key_transform(X, is_train=is_train)
+
+#     attention = ops.gemm(K, ops.reshape2f(Q, -1, 1))
+#     attention = ops.softmax_sequences(attention, lengths)
+
+#     def get_attention_bwd(d_attention):
+#         d_attention = ops.backprop_softmax_sequences(d_attention, attention, lengths)
+#         dQ = ops.gemm(K, d_attention, trans1=True)
+#         dY = ops.xp.outer(d_attention, Q)
+#         dX = K_bp(dY)
+#         return dQ, dX
+
+#     return attention, get_attention_bwd
+
+
+# def _apply_attention(ops, attention, X, lengths):
+#     output = X * attention
+
+#     def apply_attention_bwd(d_output):
+#         d_attention = (X * d_output).sum(axis=1, keepdims=True)
+#         dX = d_output * attention
+#         return dX, d_attention
+
+#     return output, apply_attention_bwd
+
+
+
+@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("Attention_SpanCategorizer.v1")
+# def build_spancat_attention_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.
+#     """
+#     with Model.define_operators({">>": chain, "|": concatenate}):
+#         width = tok2vec.maybe_get_dim("nO")
+#         attention_layer = ParametricAttention(width)
+#         maxout_layer = Maxout(nO=width, nI=width)
+#         norm_layer = LayerNorm(nI=width)
+#         cnn_model = (
+#             cast(
+#         Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+#         with_getitem(
+#             0,
+#             chain(
+#                 tok2vec,
+#                 cast(Model[List[Floats2d], Ragged], list2ragged()))))
+#             # >> list2ragged()
+#             >> attention_layer
+#             >> reduce_sum()
+#             >> residual(maxout_layer >> norm_layer >> Dropout(0.0))
+#         )
+
+#     embedding = cast(
+#         Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+#         with_getitem(
+#             0,
+#             chain(
+#                 tok2vec,
+#                 cast(Model[List[Floats2d], Ragged], list2ragged()))))
+#     # LSTM = PyTorchLSTM(nO = None, nI= None, bi = True, depth = LSTMdepth, dropout = LSTMdropout)
+
+#     model = chain(
+#         concatenate(embedding, cnn_model),
+#         extract_spans(),
+#         reducer,
+#         scorer,
+#     )
+#     model.set_ref("tok2vec", tok2vec)
+#     model.set_ref("reducer", reducer)
+#     model.set_ref("scorer", scorer)
+#     return model
+
+
+@registry.architectures("Attention_SpanCategorizer.v1")
+def build_spancat_LSTM_model(
+        tok2vec: Model[List[Doc], List[Floats2d]],
+        reducer: 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.
+    """
+    width = tok2vec.maybe_get_dim("nO")
+    embedding = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec,
+                ParametricAttention(nO = 768),
+                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("Attention_SpanCategorizer.v2")
+def build_spancat_LSTM_model(
+        tok2vec: Model[List[Doc], List[Floats2d]],
+        reducer: 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.
+    """
+    width = tok2vec.maybe_get_dim("nO")
+    # embedding = cast(
+    #     Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+    #     with_getitem(
+    #         0,
+    #         chain(
+    #             tok2vec,
+    #             cast(Model[List[Floats2d], Ragged], list2ragged()))))
+    # LSTM = PyTorchLSTM(nO = None, nI= None, bi = True, depth = LSTMdepth, dropout = LSTMdropout)
+
+    model = chain(
+        tok2vec,
+        list2ragged(),
+        ParametricAttention(width),
+        extract_spans(),
+        reducer,
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("reducer", reducer)
+    model.set_ref("scorer", scorer)
+    return model
+
+@registry.architectures("Attention_SpanCategorizer.v3")
+def build_spancat_attention_model(
+        tok2vec: Model[List[Doc], List[Floats2d]],
+        reducer: 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.
+    """
+    width = tok2vec.maybe_get_dim("nO")
+    embedding = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec,
+                cast(Model[List[Floats2d], Ragged], list2ragged()))))
+    
+
+    model = chain(
+        embedding,
+        extract_spans(),
+        ParametricAttention(nO = width),
+        reducer,
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("reducer", reducer)
+    model.set_ref("scorer", scorer)
+    return model
+
+@registry.architectures("Attention_SpanCategorizer.v4")
+def build_spancat_LSTM_model(
+        tok2vec: Model[List[Doc], List[Floats2d]],
+        reducer: 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.
+    """
+    width = tok2vec.maybe_get_dim("nO")
+    embedding = cast(
+        Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
+        with_getitem(
+            0,
+            chain(
+                tok2vec,
+                cast(Model[List[Floats2d], Ragged], list2ragged()))))
+
+    attention_layer = chain(
+                ParametricAttention(nO = width),
+                list2ragged())
+  
+
+    model = chain(
+        embedding,
+        attention_layer,
+        extract_spans(),
+        reducer,
+        scorer,
+    )
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("reducer", reducer)
+    model.set_ref("scorer", scorer)
+    return model
+
+
+@registry.architectures("CustomSpanCategorizer.v2")
+def build_spancat_model(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    reducer: 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(),
+        reducer,
+        scorer,
+    )
+
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("reducer", reducer)
+    model.set_ref("scorer", scorer)
+    return model
+
+@registry.architectures("SpanCatParametricAttention.v1")
+def build_textcat_parametric_attention_v1(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    exclusive_classes: bool = False,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    width = tok2vec.maybe_get_dim("nO")
+    parametric_attention = _build_parametric_attention_with_residual_nonlinear(
+        tok2vec=tok2vec,
+        nonlinear_layer=Maxout(nI=width, nO=width),
+        key_transform=Gelu(nI=width, nO=width),
+    )
+    with Model.define_operators({">>": chain}):
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO)
+        else:
+            output_layer = Linear(nO=nO, init_W=glorot_uniform_init) >> Logistic()
+        model = parametric_attention >> output_layer
+    if model.has_dim("nO") is not False and nO is not None:
+        model.set_dim("nO", cast(int, nO))
+    model.set_ref("output_layer", output_layer)
+
+    return model
+
+
+def _build_parametric_attention_with_residual_nonlinear(
+    *,
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    nonlinear_layer: Model[Floats2d, Floats2d],
+    key_transform: Optional[Model[Floats2d, Floats2d]] = None,
+) -> Model[List[Doc], Floats2d]:
+    with Model.define_operators({">>": chain, "|": concatenate}):
+        width = tok2vec.maybe_get_dim("nO")
+        attention_layer = ParametricAttention(nO=width)
+        norm_layer = LayerNorm(nI=width)
+        parametric_attention = (
+            tok2vec
+            >> list2ragged()
+            >> attention_layer
+            >> reduce_sum()
+            >> residual(nonlinear_layer >> norm_layer >> Dropout(0.0))
+        )
+
+        parametric_attention.init = _init_parametric_attention_with_residual_nonlinear
+
+        parametric_attention.set_ref("tok2vec", tok2vec)
+        parametric_attention.set_ref("attention_layer", attention_layer)
+        # parametric_attention.set_ref("key_transform", key_transform)
+        parametric_attention.set_ref("nonlinear_layer", nonlinear_layer)
+        parametric_attention.set_ref("norm_layer", norm_layer)
+
+        return parametric_attention
+
+def _init_parametric_attention_with_residual_nonlinear(model, X, Y) -> Model:
+    # When tok2vec is lazily initialized, we need to initialize it before
+    # the rest of the chain to ensure that we can get its width.
+    tok2vec = model.get_ref("tok2vec")
+    tok2vec.initialize(X)
+
+    tok2vec_width = get_tok2vec_width(model)
+    model.get_ref("attention_layer").set_dim("nO", tok2vec_width)
+    # model.get_ref("key_transform").set_dim("nI", tok2vec_width)
+    # model.get_ref("key_transform").set_dim("nO", tok2vec_width)
+    model.get_ref("nonlinear_layer").set_dim("nI", tok2vec_width)
+    model.get_ref("nonlinear_layer").set_dim("nO", tok2vec_width)
+    model.get_ref("norm_layer").set_dim("nI", tok2vec_width)
+    model.get_ref("norm_layer").set_dim("nO", tok2vec_width)
+    init_chain(model, X, Y)
+    return model
+
+@registry.architectures("SpanCatEnsemble.v2")
+def build_text_classifier_v2(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    # TODO: build the model with _build_parametric_attention_with_residual_nonlinear
+    # in spaCy v4. We don't do this in spaCy v3 to preserve model
+    # compatibility.
+    
+    with Model.define_operators({">>": chain, "|": concatenate}):
+        width = tok2vec.maybe_get_dim("nO")
+        attention_layer = ParametricAttention(width)
+        maxout_layer = Maxout(nO=width, nI=width)
+        norm_layer = LayerNorm(nI=width)
+        cnn_model = (
+            tok2vec
+            >> list2ragged()
+            >> attention_layer
+            >> reduce_sum()
+            >> residual(maxout_layer >> norm_layer >> Dropout(0.0))
+        )
+
+        nO_double = nO * 2 if nO else None
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO, nI=nO_double)
+        else:
+            output_layer = Linear(nO=nO, nI=nO_double) >> Logistic()
+        model = cnn_model >> output_layer
+        model.set_ref("tok2vec", tok2vec)
+    if model.has_dim("nO") is not False and nO is not None:
+        model.set_dim("nO", cast(int, nO))
+
+    model.set_ref("attention_layer", attention_layer)
+    model.set_ref("maxout_layer", maxout_layer)
+    model.set_ref("norm_layer", norm_layer)
+
+
+    model.init = init_ensemble_textcat  # type: ignore[assignment]
+    return model
+
+
+
+@registry.architectures("Ensemble_SpanCategorizer.v1")
+def build_spancat_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],
+) -> 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),
+        # 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.v2")
+def build_spancat_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,
+) -> 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.v4")
+def build_spancat_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,
+) -> 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.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("sum_reducer.v1")
+def build_sum_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.
+    """
+    return chain(
+        reduce_sum(),
+        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("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.layers("attention_reducer.v1")
+def build_mean_max_reducer1(hidden_size: int,
+                            dropout: float = 0.0,
+                            depth: int = 1) -> Model[Ragged, Floats2d]:
+    """
+    """
+
+    with Model.define_operators({">>": chain, "|": concatenate}):
+        width = tok2vec.maybe_get_dim("nO")
+        attention_layer = ParametricAttention(width)
+        maxout_layer = Maxout(nO=width, nI=width)
+        norm_layer = LayerNorm(nI=width)
+        cnn_model = (
+            tok2vec
+            >> list2ragged()
+            >> attention_layer
+            >> reduce_sum()
+            >> residual(maxout_layer >> norm_layer >> Dropout(0.0))
+        )
+
+
+    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.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)
+
+
+

en_engagement_spl_RoBERTa_base_attention-any-py3-none-any.whl

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lemmatizer/lookups/lookups.bin

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meta.json

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+{
+  "lang":"en",
+  "name":"engagement_spl_RoBERTa_base_attention",
+  "version":"0.0.1",
+  "description":"",
+  "author":"",
+  "email":"",
+  "url":"",
+  "license":"",
+  "spacy_version":">=3.6.0,<3.7.0",
+  "spacy_git_version":"6fc153a26",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null
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+      "auxpass",
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+      "meta",
+      "neg",
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+      "nsubj",
+      "nsubjpass",
+      "nummod",
+      "oprd",
+      "parataxis",
+      "pcomp",
+      "pobj",
+      "poss",
+      "preconj",
+      "predet",
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+      "quantmod",
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+      "xcomp"
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+    "ner":[
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+      "DATE",
+      "EVENT",
+      "FAC",
+      "GPE",
+      "LANGUAGE",
+      "LAW",
+      "LOC",
+      "MONEY",
+      "NORP",
+      "ORDINAL",
+      "ORG",
+      "PERCENT",
+      "PERSON",
+      "PRODUCT",
+      "QUANTITY",
+      "TIME",
+      "WORK_OF_ART"
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+    "attribute_ruler":[
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+    ],
+    "lemmatizer":[
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+    ],
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+    ],
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+  "requirements":[
+    "spacy-transformers>=1.2.5,<1.3.0",
+    "spacy-experimental>=0.6.4,<0.7.0"
+  ]
+}

ner/cfg

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+{
+  "moves":null,
+  "update_with_oracle_cut_size":100,
+  "multitasks":[
+
+  ],
+  "min_action_freq":1,
+  "learn_tokens":false,
+  "beam_width":1,
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+  "beam_update_prob":0.0,
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+}

ner/moves

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parser/cfg

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+{
+  "moves":null,
+  "update_with_oracle_cut_size":100,
+  "multitasks":[
+
+  ],
+  "min_action_freq":30,
+  "learn_tokens":false,
+  "beam_width":1,
+  "beam_density":0.0,
+  "beam_update_prob":0.0,
+  "incorrect_spans_key":null
+}

parser/moves

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spancat/cfg

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+{
+  "labels":[
+    "ATTRIBUTION",
+    "ENTERTAIN",
+    "PROCLAIM",
+    "SOURCES",
+    "MONOGLOSS",
+    "CITATION",
+    "ENDOPHORIC",
+    "DENY",
+    "JUSTIFYING",
+    "COUNTER"
+  ],
+  "spans_key":"sc",
+  "threshold":0.5,
+  "max_positive":null,
+  "negative_weight":null,
+  "allow_overlap":true
+}

spancat/model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:57f0eae0e1a723c68852e60899998d0ab54d3c5dd3a9605b47c87568f5e1c46f
+size 4731308

tagger/cfg

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+{
+  "label_smoothing":0.0,
+  "labels":[
+    "$",
+    "''",
+    ",",
+    "-LRB-",
+    "-RRB-",
+    ".",
+    ":",
+    "ADD",
+    "AFX",
+    "CC",
+    "CD",
+    "DT",
+    "EX",
+    "FW",
+    "HYPH",
+    "IN",
+    "JJ",
+    "JJR",
+    "JJS",
+    "LS",
+    "MD",
+    "NFP",
+    "NN",
+    "NNP",
+    "NNPS",
+    "NNS",
+    "PDT",
+    "POS",
+    "PRP",
+    "PRP$",
+    "RB",
+    "RBR",
+    "RBS",
+    "RP",
+    "SYM",
+    "TO",
+    "UH",
+    "VB",
+    "VBD",
+    "VBG",
+    "VBN",
+    "VBP",
+    "VBZ",
+    "WDT",
+    "WP",
+    "WP$",
+    "WRB",
+    "XX",
+    "``"
+  ],
+  "neg_prefix":"!",
+  "overwrite":false
+}

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