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:
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.
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:
('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:
@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
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Evaluation results
- precisionself-reported98.010
- Recallself-reported97.080