first commit

app/resources/models/订单尺寸识别/best_model/config.cfg

@@ -0,0 +1,135 @@
+[paths]
+train = null
+dev = null
+vectors = null
+init_tok2vec = null
+
+[system]
+seed = 0
+gpu_allocator = null
+
+[nlp]
+lang = "zh"
+pipeline = ["ner"]
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+batch_size = 1000
+vectors = {"@vectors":"spacy.Vectors.v1"}
+
+[nlp.tokenizer]
+@tokenizers = "spacy.zh.ChineseTokenizer"
+segmenter = "char"
+
+[components]
+
+[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 = true
+nO = null
+
+[components.ner.model.tok2vec]
+@architectures = "spacy.HashEmbedCNN.v2"
+pretrained_vectors = null
+width = 96
+depth = 4
+embed_size = 2000
+window_size = 1
+maxout_pieces = 3
+subword_features = true
+
+[corpora]
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+gold_preproc = false
+max_length = 0
+limit = 0
+augmenter = null
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+gold_preproc = false
+max_length = 0
+limit = 0
+augmenter = null
+
+[training]
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 1
+patience = 1600
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 200
+frozen_components = []
+annotating_components = []
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+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 = 100
+stop = 1000
+compound = 1.001
+t = 0.0
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = false
+
+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = false
+eps = 0.00000001
+learn_rate = 0.001
+
+[training.score_weights]
+ents_f = 1.0
+ents_p = 0.0
+ents_r = 0.0
+ents_per_type = null
+
+[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]
+pkuseg_model = null
+pkuseg_user_dict = "default"

app/resources/models/订单尺寸识别/best_model/meta.json

@@ -0,0 +1,41 @@
+{
+  "lang":"zh",
+  "name":"pipeline",
+  "version":"0.0.0",
+  "spacy_version":">=3.8.7,<3.9.0",
+  "description":"",
+  "author":"",
+  "email":"",
+  "url":"",
+  "license":"",
+  "spacy_git_version":"4b65aa7",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null,
+    "mode":"default"
+  },
+  "labels":{
+    "ner":[
+      "\u4ea7\u54c1",
+      "\u522e\u522e\u819c\u5c3a\u5bf8",
+      "\u52a0\u6025",
+      "\u5355\u53f7",
+      "\u5c3a\u5bf8",
+      "\u5de5\u827a",
+      "\u6570\u91cf",
+      "\u6750\u8d28",
+      "\u7528\u6237\u4fe1\u606f"
+    ]
+  },
+  "pipeline":[
+    "ner"
+  ],
+  "components":[
+    "ner"
+  ],
+  "disabled":[
+
+  ]
+}

app/resources/models/订单尺寸识别/best_model/ner/cfg

@@ -0,0 +1,13 @@
+{
+  "moves":null,
+  "update_with_oracle_cut_size":100,
+  "multitasks":[
+
+  ],
+  "min_action_freq":1,
+  "learn_tokens":false,
+  "beam_width":1,
+  "beam_density":0.0,
+  "beam_update_prob":0.0,
+  "incorrect_spans_key":null
+}

app/resources/models/订单尺寸识别/best_model/ner/moves

@@ -0,0 +1 @@
+‚¥movesÚ0{"0":{},"1":{"\u522e\u522e\u819c\u5c3a\u5bf8":-1,"\u6570\u91cf":-2,"\u5c3a\u5bf8":-3,"\u5355\u53f7":-4,"\u4ea7\u54c1":-5,"\u6750\u8d28":-6,"\u5de5\u827a":-7,"\u52a0\u6025":-8,"\u7528\u6237\u4fe1\u606f":-9},"2":{"\u522e\u522e\u819c\u5c3a\u5bf8":-1,"\u6570\u91cf":-2,"\u5c3a\u5bf8":-3,"\u5355\u53f7":-4,"\u4ea7\u54c1":-5,"\u6750\u8d28":-6,"\u5de5\u827a":-7,"\u52a0\u6025":-8,"\u7528\u6237\u4fe1\u606f":-9},"3":{"\u522e\u522e\u819c\u5c3a\u5bf8":-1,"\u6570\u91cf":-2,"\u5c3a\u5bf8":-3,"\u5355\u53f7":-4,"\u4ea7\u54c1":-5,"\u6750\u8d28":-6,"\u5de5\u827a":-7,"\u52a0\u6025":-8,"\u7528\u6237\u4fe1\u606f":-9},"4":{"":1,"\u522e\u522e\u819c\u5c3a\u5bf8":-1,"\u6570\u91cf":-2,"\u5c3a\u5bf8":-3,"\u5355\u53f7":-4,"\u4ea7\u54c1":-5,"\u6750\u8d28":-6,"\u5de5\u827a":-7,"\u52a0\u6025":-8,"\u7528\u6237\u4fe1\u606f":-9},"5":{"":1}}£cfg�§neg_keyÀ

app/resources/models/订单尺寸识别/best_model/tokenizer/cfg

@@ -0,0 +1,3 @@
+{
+  "segmenter":"char"
+}

app/resources/models/订单尺寸识别/best_model/vocab/key2row

@@ -0,0 +1 @@
+�

app/resources/models/订单尺寸识别/best_model/vocab/lookups.bin

@@ -0,0 +1 @@
+�

app/resources/models/订单尺寸识别/best_model/vocab/vectors.cfg

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