--- language: - lat - fra - spa - multilingual license: cc-by-nc-4.0 tags: - text # Example: audio - named entity recognition - roberta - historical languages - precision # Example: wer. Use metric id from https://hf.co/metrics - recall model-index: - name: roberta-multilingual-medieval-ner results: - task: type: named entity recognition # Required. Example: automatic-speech-recognition metrics: - type: precision value: 98.01 - type: Recall value: 97.08 inference: parameters: aggregation_strategy: 'simple' widget: - text: "In nomine sanctæ et individuæ Trinitatis. Ego Guido, Dei gratia Cathalaunensis episcopus, propter inevitabilem temporum mutationem et casum decedentium quotidie personarum, necesse habemus litteris annotare quod dampnosa delere non possit oblivio. Eapropter notum fieri volumus tam futuris quam presentibus quod, pro remedio animæ meæ et predecessorum nostrorum, abbati et fratribus de Insula altare de Hattunmaisnil dedimus et perpetuo habendum concessimus, salvis custumiis nostris et archidiaconi loci illius. Ne hoc ergo malignorum hominum perversitate aut temporis alteratur incommodo presentem paginam sigilli nostri impressione firmavimus, testibus subnotatis : S. Raynardy capellani, Roberti Armensis, Mathei de Waisseio, Michaeli decani, Hugonis de Monasterio, Hervaudi de Panceio. Data per manum Gerardi cancellarii, anno ab incarnatione Domini millesimo centesimo septuagesimo octavo. " --- ## Model Details This is a Fine-tuned version of the multilingual Roberta model on medieval charters. The model is intended to recognize Locations and persons in medieval texts in a Flat and nested manner. The train dataset entails 8k annotated texts on medieval latin, french and Spanish from a period ranging from 11th to 15th centuries. ### How to Get Started with the Model The model is intended to be used in a simple way manner: ```python import torch from transformers import pipeline pipe = pipeline("token-classification", model="magistermilitum/roberta-multilingual-medieval-ner") results = list(map(pipe, list_of_sentences)) results =[[[y["entity"],y["word"], y["start"], y["end"]] for y in x] for x in results] print(results) ``` ### Model Description The following snippet can transforms model inferences to CONLL format using the BIO format. ```python class TextProcessor: def __init__(self, filename): self.filename = filename self.sent_detector = nltk.data.load("tokenizers/punkt/english.pickle") #sentence tokenizer self.sentences = [] self.new_sentences = [] self.results = [] self.new_sentences_token_info = [] self.new_sentences_bio = [] self.BIO_TAGS = [] self.stripped_BIO_TAGS = [] def read_file(self): #Reading a txt file with one document per line. with open(self.filename, 'r') as f: text = f.read() self.sentences = self.sent_detector.tokenize(text.strip()) def process_sentences(self): #We split long sentences as encoder has a 256 max-lenght. Sentences with les of 40 words will be merged. for sentence in self.sentences: if len(sentence.split()) < 40 and self.new_sentences: self.new_sentences[-1] += " " + sentence else: self.new_sentences.append(sentence) def apply_model(self, pipe): self.results = list(map(pipe, self.new_sentences)) self.results=[[[y["entity"],y["word"], y["start"], y["end"]] for y in x] for x in self.results] def tokenize_sentences(self): for n_s in self.new_sentences: tokens=n_s.split() # Basic tokenization token_info = [] # Initialize a variable to keep track of character index char_index = 0 # Iterate through the tokens and record start and end info for token in tokens: start = char_index end = char_index + len(token) # Subtract 1 for the last character of the token token_info.append((token, start, end)) char_index += len(token) + 1 # Add 1 for the whitespace self.new_sentences_token_info.append(token_info) def process_results(self): #merge subwords and BIO tags for result in self.results: merged_bio_result = [] current_word = "" current_label = None current_start = None current_end = None for entity, subword, start, end in result: if subword.startswith("▁"): subword = subword[1:] merged_bio_result.append([current_word, current_label, current_start, current_end]) current_word = "" ; current_label = None ; current_start = None ; current_end = None if current_start is None: current_word = subword ; current_label = entity ; current_start = start+1 ; current_end= end else: current_word += subword ; current_end = end if current_word: merged_bio_result.append([current_word, current_label, current_start, current_end]) self.new_sentences_bio.append(merged_bio_result[1:]) def match_tokens_with_entities(self): #match BIO tags with tokens for i,ss in enumerate(self.new_sentences_token_info): for word in ss: for ent in self.new_sentences_bio[i]: if word[1]==ent[2]: if ent[1]=="L-PERS": self.BIO_TAGS.append([word[0], "I-PERS", "B-LOC"]) break else: if "LOC" in ent[1]: self.BIO_TAGS.append([word[0], "O", ent[1]]) else: self.BIO_TAGS.append([word[0], ent[1], "O"]) break else: self.BIO_TAGS.append([word[0], "O", "O"]) def separate_dots_and_comma(self): #optional signs=[",", ";", ":", "."] for bio in self.BIO_TAGS: if any(bio[0][-1]==sign for sign in signs) and len(bio[0])>1: self.stripped_BIO_TAGS.append([bio[0][:-1], bio[1], bio[2]]); self.stripped_BIO_TAGS.append([bio[0][-1], "O", "O"]) else: self.stripped_BIO_TAGS.append(bio) def save_BIO(self): with open('output_BIO_a.txt', 'w', encoding='utf-8') as output_file: output_file.write("TOKEN\tPERS\tLOCS\n"+"\n".join(["\t".join(x) for x in self.stripped_BIO_TAGS])) # Usage: processor = TextProcessor('my_docs_file.txt') processor.read_file() processor.process_sentences() processor.apply_model(pipe) processor.tokenize_sentences() processor.process_results() processor.match_tokens_with_entities() processor.separate_dots_and_comma() processor.save_BIO() ``` - **Developed by:** [Sergio Torres Aguilar] - **Model type:** [XLM-Roberta] - **Language(s) (NLP):** [Medieval Latin, Spanish, French] - **Finetuned from model [optional]:** [Named Entity Recognition] ### Direct Use A sentence as : "Ego Radulfus de Francorvilla miles, notum facio tam presentibus cum futuris quod, cum Guillelmo Bateste militi de Miliaco" Will be annotated in BIO format as: ```python ('Ego', 'O', 'O') ('Radulfus', 'B-PERS') ('de', 'I-PERS', 'O') ('Francorvilla', 'I-PERS', 'B-LOC') ('miles', 'O') (',', 'O', 'O') ('notum', 'O', 'O') ('facio', 'O', 'O') ('tam', 'O', 'O') ('presentibus', 'O', 'O') ('quam', 'O', 'O') ('futuris', 'O', 'O') ('quod', 'O', 'O') (',', 'O', 'O') ('cum', 'O', 'O') ('Guillelmo', 'B-PERS', 'O') ('Bateste', 'I-PERS', 'O') ('militi', 'O', 'O') ('de', 'O', 'O') ('Miliaco', 'O', 'B-LOC') ``` ### Training Procedure The model was fine-tuned during 5 epoch on the XML-Roberta-Large using a 5e-5 Lr and a batch size of 16. **BibTeX:** ```bibtex @inproceedings{aguilar2022multilingual, title={Multilingual Named Entity Recognition for Medieval Charters Using Stacked Embeddings and Bert-based Models.}, author={Aguilar, Sergio Torres}, booktitle={Proceedings of the second workshop on language technologies for historical and ancient languages}, pages={119--128}, year={2022} } ``` ## Model Card Contact [sergio.torres@uni.lu]