Update spacy pipeline to 3.5.2

README.md

@@ -14,72 +14,72 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.9202821869
+      value: 0.9149982438
     - name: NER Recall
       type: recall
-      value: 0.9173699015
+      value: 0.9159634318
     - name: NER F Score
       type: f_score
-      value: 0.9188237366
+      value: 0.9154805834
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9823402728
+      value: 0.981431853
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.981670256
+      value: 0.980474732
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9739172051
+      value: 0.9659264931
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9899531145
+      value: 0.9894746914
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.9080729291
+      value: 0.9112312772
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.8665901043
+      value: 0.868695569
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9833887043
+      value: 0.9933184855
 ---
 Hungarian transformer pipeline (XLM-RoBERTa) for HuSpaCy. Components: transformer, senter, tagger, morphologizer, lemmatizer, parser, ner
 
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_trf_xl` |
-| **Version** | `3.5.1` |
+| **Version** | `3.5.2` |
 | **spaCy** | `>=3.5.0,<3.6.0` |
-| **Default Pipeline** | `transformer`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `lemma_smoother`, `experimental_arc_predicter`, `experimental_arc_labeler`, `ner` |
-| **Components** | `transformer`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `lemma_smoother`, `experimental_arc_predicter`, `experimental_arc_labeler`, `ner` |
+| **Default Pipeline** | `transformer`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `experimental_arc_predicter`, `experimental_arc_labeler`, `ner` |
+| **Components** | `transformer`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `experimental_arc_predicter`, `experimental_arc_labeler`, `ner` |
 | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
 | **Sources** | [UD Hungarian Szeged](https://universaldependencies.org/treebanks/hu_szeged/index.html) (Richárd Farkas, Katalin Simkó, Zsolt Szántó, Viktor Varga, Veronika Vincze (MTA-SZTE Research Group on Artificial Intelligence))<br />[NYTK-NerKor Corpus](https://github.com/nytud/NYTK-NerKor) (Eszter Simon, Noémi Vadász (Department of Language Technology and Applied Linguistics))<br />[hunNERwiki](http://hlt.sztaki.hu/resources/hunnerwiki.html) (Eszter Simon, Dávid Márk Nemeskey (HLT Group, Budapest University of Technology and Economics))<br />[Szeged NER Corpus](https://rgai.inf.u-szeged.hu/node/130) (György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik (MTA-SZTE Research Group on Artificial Intelligence))<br />[huBERT base model (cased)](https://huggingface.co/SZTAKI-HLT/hubert-base-cc) (Dávid Márk Nemeskey (SZTAKI-HLT)) |
 | **License** | `cc-by-sa-4.0` |
@@ -108,20 +108,20 @@ Hungarian transformer pipeline (XLM-RoBERTa) for HuSpaCy. Components: transforme
 | `TOKEN_P` | 99.86 |
 | `TOKEN_R` | 99.93 |
 | `TOKEN_F` | 99.89 |
-| `SENTS_P` | 97.80 |
-| `SENTS_R` | 98.89 |
-| `SENTS_F` | 98.34 |
-| `TAG_ACC` | 98.23 |
-| `POS_ACC` | 98.17 |
-| `MORPH_ACC` | 97.39 |
-| `MORPH_MICRO_P` | 99.16 |
-| `MORPH_MICRO_R` | 98.68 |
-| `MORPH_MICRO_F` | 98.92 |
-| `LEMMA_ACC` | 99.00 |
-| `BOUND_DEP_LAS` | 86.78 |
-| `BOUND_DEP_UAS` | 90.95 |
-| `DEP_UAS` | 90.81 |
-| `DEP_LAS` | 86.66 |
-| `ENTS_P` | 92.03 |
-| `ENTS_R` | 91.74 |
-| `ENTS_F` | 91.88 |
+| `SENTS_P` | 99.33 |
+| `SENTS_R` | 99.33 |
+| `SENTS_F` | 99.33 |
+| `TAG_ACC` | 98.14 |
+| `POS_ACC` | 98.05 |
+| `MORPH_ACC` | 96.59 |
+| `MORPH_MICRO_P` | 98.78 |
+| `MORPH_MICRO_R` | 98.36 |
+| `MORPH_MICRO_F` | 98.57 |
+| `LEMMA_ACC` | 98.95 |
+| `BOUND_DEP_LAS` | 86.89 |
+| `BOUND_DEP_UAS` | 91.16 |
+| `DEP_UAS` | 91.12 |
+| `DEP_LAS` | 86.87 |
+| `ENTS_P` | 91.50 |
+| `ENTS_R` | 91.60 |
+| `ENTS_F` | 91.55 |

config.cfg

@@ -1,8 +1,8 @@
 [paths]
-tagger_model = "models/hu_core_news_trf_xl-tagger-3.5.0/model-best"
-parser_model = "models/hu_core_news_trf_xl-parser-3.5.0/model-best"
-ner_model = "models/hu_core_news_trf_xl-ner-3.5.0/model-best"
-lemmatizer_lookups = "models/hu_core_news_trf_xl-lookup-lemmatizer-3.5.0"
+tagger_model = "models/hu_core_news_trf_xl-tagger-3.5.2/model-best"
+parser_model = "models/hu_core_news_trf_xl-parser-3.5.2/model-best"
+ner_model = "models/hu_core_news_trf_xl-ner-3.5.2/model-best"
+lemmatizer_lookups = "models/hu_core_news_trf_xl-lookup-lemmatizer-3.5.2"
 train = null
 dev = null
 vectors = null
@@ -14,7 +14,7 @@ gpu_allocator = null
 
 [nlp]
 lang = "hu"
-pipeline = ["transformer","senter","tagger","morphologizer","lookup_lemmatizer","trainable_lemmatizer","lemma_smoother","experimental_arc_predicter","experimental_arc_labeler","ner"]
+pipeline = ["transformer","senter","tagger","morphologizer","lookup_lemmatizer","trainable_lemmatizer","experimental_arc_predicter","experimental_arc_labeler","ner"]
 tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
 disabled = []
 before_creation = null
@@ -30,30 +30,17 @@ scorer = {"@scorers":"spacy-experimental.biaffine_parser_scorer.v1"}
 
 [components.experimental_arc_labeler.model]
 @architectures = "spacy-experimental.Bilinear.v1"
-hidden_width = 128
-mixed_precision = false
+hidden_width = 256
+mixed_precision = true
 nO = null
 dropout = 0.1
 grad_scaler = null
 
 [components.experimental_arc_labeler.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecTransformer.v3"
-name = "xlm-roberta-large"
-mixed_precision = false
-pooling = {"@layers":"reduce_mean.v1"}
+@architectures = "spacy-transformers.TransformerListener.v1"
 grad_factor = 1.0
-
-[components.experimental_arc_labeler.model.tok2vec.get_spans]
-@span_getters = "spacy-transformers.strided_spans.v1"
-window = 128
-stride = 96
-
-[components.experimental_arc_labeler.model.tok2vec.grad_scaler_config]
-
-[components.experimental_arc_labeler.model.tok2vec.tokenizer_config]
-use_fast = true
-
-[components.experimental_arc_labeler.model.tok2vec.transformer_config]
+upstream = "transformer"
+pooling = {"@layers":"reduce_mean.v1"}
 
 [components.experimental_arc_predicter]
 factory = "experimental_arc_predicter"
@@ -61,33 +48,17 @@ scorer = {"@scorers":"spacy-experimental.biaffine_parser_scorer.v1"}
 
 [components.experimental_arc_predicter.model]
 @architectures = "spacy-experimental.PairwiseBilinear.v1"
-hidden_width = 256
+hidden_width = 64
 nO = 1
 mixed_precision = false
 dropout = 0.1
 grad_scaler = null
 
 [components.experimental_arc_predicter.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecTransformer.v3"
-name = "xlm-roberta-large"
-mixed_precision = false
-pooling = {"@layers":"reduce_mean.v1"}
+@architectures = "spacy-transformers.TransformerListener.v1"
 grad_factor = 1.0
-
-[components.experimental_arc_predicter.model.tok2vec.get_spans]
-@span_getters = "spacy-transformers.strided_spans.v1"
-window = 128
-stride = 96
-
-[components.experimental_arc_predicter.model.tok2vec.grad_scaler_config]
-
-[components.experimental_arc_predicter.model.tok2vec.tokenizer_config]
-use_fast = true
-
-[components.experimental_arc_predicter.model.tok2vec.transformer_config]
-
-[components.lemma_smoother]
-factory = "hu.lemma_smoother"
+upstream = "transformer"
+pooling = {"@layers":"reduce_mean.v1"}
 
 [components.lookup_lemmatizer]
 factory = "hu.lookup_lemmatizer"
@@ -145,6 +116,7 @@ stride = 96
 
 [components.ner.model.tok2vec.tokenizer_config]
 use_fast = true
+model_max_length = 512
 
 [components.ner.model.tok2vec.transformer_config]
 
@@ -193,10 +165,24 @@ top_k = 3
 nO = null
 
 [components.trainable_lemmatizer.model.tok2vec]
-@architectures = "spacy-transformers.TransformerListener.v1"
-grad_factor = 1.0
-upstream = "transformer"
+@architectures = "spacy-transformers.Tok2VecTransformer.v3"
+name = "xlm-roberta-large"
+mixed_precision = false
 pooling = {"@layers":"reduce_mean.v1"}
+grad_factor = 1.0
+
+[components.trainable_lemmatizer.model.tok2vec.get_spans]
+@span_getters = "spacy-transformers.strided_spans.v1"
+window = 128
+stride = 96
+
+[components.trainable_lemmatizer.model.tok2vec.grad_scaler_config]
+
+[components.trainable_lemmatizer.model.tok2vec.tokenizer_config]
+use_fast = true
+model_max_length = 512
+
+[components.trainable_lemmatizer.model.tok2vec.transformer_config]
 
 [components.transformer]
 factory = "transformer"
@@ -217,6 +203,7 @@ stride = 96
 
 [components.transformer.model.tokenizer_config]
 use_fast = true
+model_max_length = 512
 
 [components.transformer.model.transformer_config]
 

experimental_arc_labeler/model

@@ -1,3 +1,3 @@
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experimental_arc_predicter/model

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hu_core_news_trf_xl-any-py3-none-any.whl

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+size 5554448736

meta.json

@@ -1,7 +1,7 @@
 {
   "lang":"hu",
   "name":"core_news_trf_xl",
-  "version":"3.5.1",
+  "version":"3.5.2",
   "description":"Hungarian transformer pipeline (XLM-RoBERTa) for HuSpaCy. Components: transformer, senter, tagger, morphologizer, lemmatizer, parser, ner",
   "author":"SzegedAI, MILAB",
   "email":"gyorgy@orosz.link",
@@ -1187,9 +1187,6 @@
     ],
     "lookup_lemmatizer":[
 
-    ],
-    "lemma_smoother":[
-
     ],
     "experimental_arc_predicter":[
 
@@ -1261,7 +1258,6 @@
     "morphologizer",
     "lookup_lemmatizer",
     "trainable_lemmatizer",
-    "lemma_smoother",
     "experimental_arc_predicter",
     "experimental_arc_labeler",
     "ner"
@@ -1273,7 +1269,6 @@
     "morphologizer",
     "lookup_lemmatizer",
     "trainable_lemmatizer",
-    "lemma_smoother",
     "experimental_arc_predicter",
     "experimental_arc_labeler",
     "ner"
@@ -1286,297 +1281,292 @@
     "token_p":0.998565417,
     "token_r":0.9993300153,
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-    "sents_p":0.9779735683,
-    "sents_r":0.9888641425,
-    "sents_f":0.9833887043,
-    "tag_acc":0.9823402728,
-    "pos_acc":0.981670256,
-    "morph_acc":0.9739172051,
-    "morph_micro_p":0.9915792201,
-    "morph_micro_r":0.9867640739,
-    "morph_micro_f":0.9891657871,
+    "sents_p":0.9933184855,
+    "sents_r":0.9933184855,
+    "sents_f":0.9933184855,
+    "tag_acc":0.981431853,
+    "pos_acc":0.980474732,
+    "morph_acc":0.9659264931,
+    "morph_micro_p":0.9877869843,
+    "morph_micro_r":0.9836269875,
+    "morph_micro_f":0.9857025968,
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