multi_home_ner_best_model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,60 @@
 ---
+library_name: Spacy
 license: mit
+tags:
+- Spacy
+- Named entity recognition
+metrics:
+- P
+- R
+- F1
+language:
+- 'la'
+- 'de'
+- 'cs'
+​version:
+- 'Spacy v2'
 ---
+
+# HOME-NACR Named entity recognition
+
+This model detects Person and Location entities in Latin, Czech and German.
+
+## Model description
+
+The model has been trained using the Spacy v2 library on the HOME-NACR document annotations. The model is compatible with version 2.3.2 of Spacy and incompatible with versions 3.x.x.
+
+## Evaluation results
+
+The model achieves the following results on HOME-NACR:
+
+| tag  | predicted | matched | Precision | Recall | F1    | Support |
+| ---- | --------- | ------- | --------- | ------ | ----- | ------- |
+| PERS | 28276     | 28006   | 0.99      | 0.997  | 0.994 | 28087   |
+| LOC  | 27541     | 27165   | 0.986     | 0.987  | 0.987 | 27528   |
+| All  | 55817     | 55171   | 0.988     | 0.992  | 0.990 | 55615   |
+
+## How to use
+
+Please refer to the Spacy library page (https://pypi.org/project/spacy/2.3.2/) to use this model.
+
+# Cite us!
+
+```bibtex
+@inproceedings{10.1007/978-3-031-06555-2_29,
+    author = {Monroc, Claire Bizon and Miret, Blanche and Bonhomme, Marie-Laurence and Kermorvant, Christopher},
+    title = {A Comprehensive Study Of Open-Source Libraries For Named Entity Recognition On Handwritten Historical Documents},
+    year = {2022},
+    isbn = {978-3-031-06554-5},
+    publisher = {Springer-Verlag},
+    address = {Berlin, Heidelberg},
+    url = {https://doi.org/10.1007/978-3-031-06555-2_29},
+    doi = {10.1007/978-3-031-06555-2_29},
+    abstract = {In this paper, we propose an evaluation of several state-of-the-art open-source natural language processing (NLP) libraries for named entity recognition (NER) on handwritten historical documents: spaCy, Stanza and Flair. The comparison is carried out on three low-resource multilingual datasets of handwritten historical documents: HOME (a multilingual corpus of medieval charters), Balsac (a corpus of parish records from Quebec), and Esposalles (a corpus of marriage records in Catalan). We study the impact of the document recognition processes (text line detection and handwriting recognition) on the performance of the NER. We show that current off-the-shelf NER libraries yield state-of-the-art results, even on low-resource languages or multilingual documents using multilingual models. We show, in an end-to-end evaluation, that text line detection errors have a greater impact than handwriting recognition errors. Finally, we also report state-of-the-art results on the public Esposalles dataset.},
+    booktitle = {Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings},
+    pages = {429–444},
+    numpages = {16},
+    keywords = {Text line detection, Named entity recognition, Handwritten historical documents},
+    location = {La Rochelle, France}
+}
+```

meta.json

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+{
+  "lang":"xx",
+  "pipeline":[
+    "ner"
+  ],
+  "spacy_version":">=2.3.2<3.0",
+  "speed":{
+    "nwords":22118,
+    "cpu":14078.2482701708,
+    "gpu":36967.2712529745
+  },
+  "accuracy":{
+    "ents_f":81.7114729269,
+    "ents_p":82.5554705432,
+    "ents_r":80.8845577211,
+    "ents_per_type":{
+      "LOC":{
+        "p":93.4687953556,
+        "r":87.027027027,
+        "f":90.132960112
+      },
+      "PER":{
+        "p":71.1538461538,
+        "r":74.0,
+        "f":72.5490196078
+      },
+      "DAT":{
+        "p":60.8695652174,
+        "r":63.6363636364,
+        "f":62.2222222222
+      }
+    },
+    "token_acc":100.0
+  },
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":"spacy_pretrained_vectors"
+  },
+  "name":"model0",
+  "version":"0.0.0",
+  "labels":{
+    "ner":[
+      "DAT",
+      "LOC",
+      "PER"
+    ]
+  }
+}

ner/cfg

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+{
+  "beam_width":1,
+  "beam_density":0.0,
+  "beam_update_prob":1.0,
+  "cnn_maxout_pieces":3,
+  "nr_feature_tokens":6,
+  "nr_class":14,
+  "hidden_depth":1,
+  "token_vector_width":96,
+  "hidden_width":64,
+  "maxout_pieces":2,
+  "pretrained_vectors":null,
+  "bilstm_depth":0,
+  "self_attn_depth":0,
+  "conv_depth":4,
+  "conv_window":1,
+  "embed_size":2000
+}

ner/model

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+size 4004808

ner/moves

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+��movesٸ{"0":{},"1":{"PER":11554,"DAT":5976,"LOC":5662},"2":{"PER":11554,"DAT":5976,"LOC":5662},"3":{"PER":11554,"DAT":5976,"LOC":5662},"4":{"PER":11554,"DAT":5976,"LOC":5662,"":1},"5":{"":1}}

tokenizer

@@ -0,0 +1,3 @@
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