Stage trained model weights and provenance

.gitattributes

@@ -33,3 +33,8 @@ 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
+model/parser/model filter=lfs diff=lfs merge=lfs -text
+model/tok2vec/model filter=lfs diff=lfs merge=lfs -text
+model/vocab/key2row filter=lfs diff=lfs merge=lfs -text
+model/vocab/strings.json filter=lfs diff=lfs merge=lfs -text
+model/vocab/vectors filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,79 @@
+---
+language:
+- my
+library_name: spacy
+tags:
+- dependency-parsing
+- part-of-speech
+- sentence-segmentation
+- burmese
+---
+
+# Burmese POS and Dependency Parser
+
+I trained this spaCy pipeline for the grammatical analysis stage of
+[Burmese Neural Reader](https://github.com/conradcompagna/burmese-neural-reader).
+It combines a shared tok2vec encoder, a morphologizer that assigns universal POS
+tags, and a transition-based dependency parser with sentence-boundary prediction.
+These are the trained components used by the deployed reader.
+
+## Training and evaluation
+
+I prepared the myUDTree v1.0 annotations as 4,320 multi-sentence documents and
+trained the joint pipeline on a 95/5 document split: 4,104 training documents
+and 216 development documents. Each document contains ten source sentences,
+apart from the final six-sentence group. The training partition contains 536,791
+tokens and the development partition 27,716 tokens.
+
+| Development metric | Score |
+| --- | ---: |
+| Universal POS accuracy | 96.92% |
+| Unlabeled attachment score | 92.39% |
+| Labeled attachment score | 89.41% |
+| Sentence-boundary F1 | 93.01% |
+
+These scores are saved in the selected checkpoint. Re-evaluating it against the
+retained development set with spaCy 3.8.11 reproduces the saved figures exactly.
+
+The encoder combines multihash token features with 300-dimensional fastText
+vectors and a six-layer, width-128 maxout window encoder. The task heads share
+these representations. I trained with Adam, dropout 0.1 and seed 0, selecting
+checkpoints using POS accuracy, dependency attachment and sentence-boundary F1.
+
+## Use the trained pipeline
+
+The model takes **already segmented Burmese words**. In my reader, dictionary
+dynamic programming and unigram/bigram scoring determine the final word
+boundaries before the grammatical analysis stage.
+
+With the downloaded `model` directory and spaCy 3.8.11:
+
+```python
+import spacy
+from spacy.tokens import Doc
+
+nlp = spacy.load("model")
+
+def analyze_words(words: list[str], spaces: list[bool]) -> Doc:
+    """Preserve the word boundaries and spacing supplied by your segmenter."""
+    return nlp(Doc(nlp.vocab, words=words, spaces=spaces))
+```
+
+Read POS labels from `token.pos_`, dependency relations from `token.dep_`, heads
+from `token.head`, and predicted sentences from `doc.sents`. The checkpoint uses
+spaCy's `xx` language class to accept externally segmented Burmese text. Its
+morphologizer's label set is POS-only. NER is a separate stage in the reader.
+
+## Sources
+
+The training annotations are from [myUDTree v1.0](https://github.com/ye-kyaw-thu/myUDTree)
+by Zar Zar Hlaing, Ye Kyaw Thu and collaborators. The corpus is distributed under
+[CC BY-NC-SA 4.0](https://github.com/ye-kyaw-thu/myUDTree/blob/main/LICENSE.md).
+
+The static embeddings are the Burmese Common Crawl/Wikipedia
+[fastText vectors](https://fasttext.cc/docs/en/crawl-vectors), distributed under
+CC BY-SA 3.0. The spaCy vocabulary retains 200,000 vector rows and 335,230 word
+keys. See Grave et al., *Learning Word Vectors for 157 Languages*, LREC 2018.
+
+
+[Evaluation record](evaluation.json) · [Artifact identities](artifact-manifest.json) · [Third-party notices](THIRD_PARTY_NOTICES.md)

THIRD_PARTY_NOTICES.md

@@ -0,0 +1,29 @@
+# Third-party sources
+
+## myUDTree
+
+Zar Zar Hlaing, Ye Kyaw Thu, Thepchai Supnithi and P. Netisopakul.
+myUDTree v1.0: Myanmar Universal Dependencies treebank.
+https://github.com/ye-kyaw-thu/myUDTree
+Corpus license: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.
+https://github.com/ye-kyaw-thu/myUDTree/blob/main/LICENSE.md
+I converted the retained annotations to grouped spaCy documents, partitioned
+them for training/development and trained the released joint pipeline.
+The source corpus and DocBins are not bundled in this release.
+
+## fastText word vectors
+
+Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin and Tomas Mikolov.
+*Learning Word Vectors for 157 Languages.* LREC 2018.
+https://fasttext.cc/docs/en/crawl-vectors
+Burmese Common Crawl/Wikipedia vectors (cc.my.300):
+Creative Commons Attribution-ShareAlike 3.0 Unported.
+https://creativecommons.org/licenses/by-sa/3.0/
+I converted the vectors to a spaCy vocabulary and retained 200,000 float32 rows.
+The bundled vectors and their derived vocabulary remain subject to these terms;
+they are not covered by fastText's separate MIT software license.
+
+## spaCy
+
+Explosion and spaCy contributors. https://spacy.io/ — MIT-licensed software,
+installed separately. The released model uses spaCy 3.8.11's native format.

artifact-manifest.json

@@ -0,0 +1,119 @@
+{
+  "author": "Conrad Compagna",
+  "model_id": "burmese-pos-dependency-spacy",
+  "format": "spaCy 3.8.11 native directory",
+  "components": [
+    "tok2vec",
+    "morphologizer",
+    "parser"
+  ],
+  "metadata_changes": "Author, description, model name/version and portable corpus/vector path placeholders; learned weights and vocabulary unchanged.",
+  "deployment_identity_checked": "2026-09-23",
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evaluation.json

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+{
+  "pipeline": [
+    "tok2vec",
+    "morphologizer",
+    "parser"
+  ],
+  "model_origin": "Conrad Compagna custom training on myUDTree v1.0 annotations",
+  "source": {
+    "path": "source/myUDTree_ver1.0.conllu.pred.jsonl",
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+    "matched_selected_documents": 4320
+  },
+  "partitions": {
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+        "6": 1
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+  "annotation_identity": {
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+    "morphologizer_loss": 22737.99054479599,
+    "parser_loss": 156748.46875
+  },
+  "development_reevaluation": {
+    "performed": "2026-09-24",
+    "spacy": "3.8.11",
+    "documents": 216,
+    "elapsed_seconds": 6.834078899933957,
+    "current_scores": {
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+      "sents_p": 0.0,
+      "sents_r": 0.0,
+      "sents_f": 0.0
+    },
+    "scope": "Same retained development DocBin; this is a verification of saved evaluation, not a new test split."
+  },
+  "vectors": {
+    "source": "fastText cc.my.300 Common Crawl/Wikipedia",
+    "license": "CC BY-SA 3.0",
+    "source_sha256": "7810d202899d3ca3e1256eaf7901f18f2eea1c02197f86ae2baa2d22e803987f",
+    "rows": 200000,
+    "dimensions": 300,
+    "source_rows_verified_identical": 200000
+  },
+  "other_runtime_neural_components": {
+    "Stanza tokenize/NER": "upstream ALT/UCSY models; final application segmentation is dictionary DP plus unigram/bigram scoring"
+  }
+}

model/config.cfg

@@ -0,0 +1,168 @@
+[paths]
+train = "corpus/train_95.spacy"
+dev = "corpus/dev_5.spacy"
+vectors = "resources/cc_my_300_spacy_vectors"
+init_tok2vec = null
+
+[system]
+gpu_allocator = null
+seed = 0
+
+[nlp]
+lang = "xx"
+pipeline = ["tok2vec","morphologizer","parser"]
+batch_size = 256
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+vectors = {"@vectors":"spacy.Vectors.v1"}
+
+[components]
+
+[components.morphologizer]
+factory = "morphologizer"
+extend = false
+label_smoothing = 0.0
+overwrite = true
+scorer = {"@scorers":"spacy.morphologizer_scorer.v1"}
+
+[components.morphologizer.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.morphologizer.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode:width}
+upstream = "tok2vec"
+
+[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 = 128
+maxout_pieces = 2
+use_upper = true
+nO = null
+
+[components.parser.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode:width}
+upstream = "tok2vec"
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 128
+attrs = ["ORTH","PREFIX","SUFFIX","SHAPE","LENGTH"]
+rows = [50000,10000,10000,1000,500]
+include_static_vectors = true
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 128
+depth = 6
+window_size = 2
+maxout_pieces = 3
+
+[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 = 0
+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 = 1
+patience = 5000
+max_epochs = 0
+max_steps = 100000
+eval_frequency = 1000
+frozen_components = []
+annotating_components = []
+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 = true
+eps = 1e-8
+learn_rate = 0.001
+
+[training.score_weights]
+pos_acc = 0.3
+morph_acc = 0.0
+morph_per_feat = null
+dep_uas = 0.3
+dep_las = 0.3
+dep_las_per_type = null
+sents_p = null
+sents_r = null
+sents_f = 0.1
+
+[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]

model/meta.json

@@ -0,0 +1,128 @@
+{
+  "lang": "xx",
+  "name": "burmese_pos_dependency",
+  "version": "1.0.0",
+  "spacy_version": ">=3.8.11,<3.9.0",
+  "description": "Burmese POS, dependency parsing and sentence boundaries over pretokenized words.",
+  "author": "Conrad Compagna",
+  "email": "conradcompagna@gmail.com",
+  "url": "https://github.com/conradcompagna/burmese-neural-reader",
+  "license": "",
+  "spacy_git_version": "e7a662a",
+  "vectors": {
+    "width": 300,
+    "vectors": 200000,
+    "keys": 335230,
+    "name": "xx_pipeline.vectors",
+    "mode": "default"
+  },
+  "labels": {
+    "tok2vec": [],
+    "morphologizer": [
+      "POS=NUM",
+      "POS=SCONJ",
+      "POS=NOUN",
+      "POS=PART",
+      "POS=ADP",
+      "POS=VERB",
+      "POS=PUNCT",
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