--- license: cc-by-nc-4.0 language: - fr metrics: - accuracy - recall - precision - f1 library_name: spacy pipeline_tag: token-classification tags: - spacy - token-classification model-index: - name: fr_spacy_custom_spancat_edda results: - task: name: spancat type: span-classification metrics: - name: Span Precision type: precision value: 0.948 - name: Span Recall type: recall value: 0.849 - name: Span F1 Score type: f_score value: 0.896 --- # spaCy custom spancat for Diderot & d’Alembert’s Encyclopédie entries This model identify and classify spans of text for named entities (Spatial, Person and MISC), nested named entities (Spatial, Person and MISC), spatial relations and others from French encyclopaedic entries. The spans detected by this model are: - **NC-Spatial**: - **NP-Spatial**: - **ENE-Spatial**: - **Relation**: spatial relations, e.g. - **Latlong**: geographic coordinates, e.g. - **NC-Person**: - **NP-Person**: - **ENE-Person**: - **NP-Misc**: - **ENE-Misc**: - **Head**: - **Domain-Mark**: , e.g. Géographie, Histoire ## Model Details ### Model Description - **Developed by:** [Ludovic Moncla](https://ludovicmoncla.github.io), Katherine McDonough and Denis Vigier - **Model type:** spaCy Span Categorization - **spaCy**: `>=3.7.2,<3.8.0` - **Components**: `tok2vec`, `spancat` - **Language(s) (NLP):** French - **License:** cc-by-nc-4.0 ### Model Sources - **Repository:** https://github.com/GEODE-project/ner-spancat-edda ## Uses This model can be used to extract entities from any text that are Paeleoecology related or tangential. Potential uses include identifying unique SITE names in research papers in other domains. ## Bias, Risks, and Limitations This model was trained entirely on French encyclopaedic entries and will likely not perform well on text in other languages. Also, the paragraphs used to train the model were chosen based on being already present in the Neotoma database and therefore may have selection bias as they represent what is already known to be relevant to Neotoma and may not correctly manage new, previously missed articles. ## How to Get Started with the Model Use the code below to get started with the model. ```bash pip install https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda/resolve/main/fr_spacy_custom_spancat_edda-any-py3-none-any.whl ``` ```python # Using spacy.load(). import spacy nlp = spacy.load("fr_spacy_custom_spancat_edda") # Importing as module. import fr_spacy_custom_spancat_edda nlp = fr_spacy_custom_spancat_edda.load() doc = nlp("* ALBI, (Géog.) ville de France, capitale de l'Albigeois, dans le haut Languedoc : elle est sur le Tarn. Long. 19. 49. lat. 43. 55. 44.") spans = [] for span in doc.spans['sc']: print(span) spans.append({ "start": span.start_char, "end": span.end_char, "labels": [span.label_], "text": span.text }) print(spans) # Output [{'start': 2, 'end': 6, 'labels': ['Head'], 'text': 'ALBI'}, {'start': 16, 'end': 21, 'labels': ['NC-Spatial'], 'text': 'ville'}, {'start': 25, 'end': 31, 'labels': ['NP-Spatial'], 'text': 'France'}, {'start': 33, 'end': 41, 'labels': ['NC-Spatial'], 'text': 'capitale'}, {'start': 59, 'end': 63, 'labels': ['Relation'], 'text': 'dans'}, {'start': 93, 'end': 96, 'labels': ['Relation'], 'text': 'sur'}, {'start': 9, 'end': 14, 'labels': ['Domain-mark'], 'text': 'Géog.'}, {'start': 46, 'end': 57, 'labels': ['NP-Spatial'], 'text': "l'Albigeois"}, {'start': 97, 'end': 104, 'labels': ['NP-Spatial'], 'text': 'le Tarn'}, {'start': 16, 'end': 31, 'labels': ['ENE-Spatial'], 'text': 'ville de France'}, {'start': 64, 'end': 81, 'labels': ['NP-Spatial'], 'text': 'le haut Languedoc'}, {'start': 33, 'end': 57, 'labels': ['ENE-Spatial'], 'text': "capitale de l'Albigeois"}, {'start': 33, 'end': 81, 'labels': ['ENE-Spatial'], 'text': "capitale de l'Albigeois, dans le haut Languedoc"}, {'start': 16, 'end': 81, 'labels': ['ENE-Spatial'], 'text': "ville de France, capitale de l'Albigeois, dans le haut Languedoc"}] ``` ## Training Details ### Training Data The model was trained using a set of 2200 paragraphs randomly selected out of xx Encyclopédie's entries. All paragraphs were written in French and are distributed as follows among the Encyclopédie knowledge domains: | Knowledge domain | Paragraphs | |---|:---:| | Géographie | 1096 | | Histoire | 259 | | Droit Jurisprudence | 113 | | Physique | 92 | | Métiers | 92 | | Médecine | 88 | | Philosophie | 69 | | Histoire naturelle | 65 | | Belles-lettres | 65 | | Militaire | 62 | | Commerce | 48 | | Beaux-arts | 44 | | Agriculture | 36 | | Chasse | 31 | | Religion | 23 | | Musique | 17 | The spans/entities were labeled by the project team along with using pre-labelling with early models to speed up the labelling process. A train/val/test split was used. Validation and test sets are composed of 200 paragraphs each: 100 classified as 'Géographie' and 100 from another knowledge domain. The datasets have the following breakdown of tokens and spans/entities. | | Train | Validation | Test| |---|:---:|:---:|:---:| |Paragraphs| 1800 | 200 | 200| | Tokens | | | | | NC-Spatial | | | | | NP-Spatial | | | | | ENE-Spatial | | | | | Relation | | | | | Latlong | | | | | NC-Person | | | | | NP-Person | | | | | ENE-Person | | | | | NP-Misc | | | | | ENE-Misc | | | | | Head | | | | | Domain-Mark | | | | ### Training Procedure For full training details and results please see the GitHub repository: [github.com/GEODE-project/ner-spancat-edda](https://github.com/GEODE-project/ner-spancat-edda) ## Acknowledgement Data courtesy the [ARTFL Encyclopédie Project](https://artfl-project.uchicago.edu), University of Chicago. The authors are grateful to the [ASLAN project](https://aslan.universite-lyon.fr) (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR).