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--- |
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language: |
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- lat |
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- fra |
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- spa |
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- multilingual |
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license: cc-by-nc-4.0 |
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tags: |
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- text |
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- named entity recognition |
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- roberta |
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- historical languages |
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- precision |
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- recall |
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model-index: |
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- name: roberta-multilingual-medieval-ner |
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results: |
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- task: |
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type: named entity recognition |
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metrics: |
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- type: precision |
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value: 98.01 |
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- type: Recall |
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value: 97.08 |
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inference: |
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parameters: |
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aggregation_strategy: 'simple' |
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widget: |
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- 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. " |
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--- |
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## Model Details |
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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 |
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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. |
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### How to Get Started with the Model |
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The model is intended to be used in a simple way manner: |
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```python |
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import torch |
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from transformers import pipeline |
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pipe = pipeline("token-classification", model="magistermilitum/roberta-multilingual-medieval-ner") |
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results = list(map(pipe, list_of_sentences)) |
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results =[[[y["entity"],y["word"], y["start"], y["end"]] for y in x] for x in results] |
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print(results) |
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``` |
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### Model Description |
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The following snippet can transforms model inferences to CONLL format using the BIO format. |
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```python |
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class TextProcessor: |
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def __init__(self, filename): |
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self.filename = filename |
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self.sent_detector = nltk.data.load("tokenizers/punkt/english.pickle") #sentence tokenizer |
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self.sentences = [] |
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self.new_sentences = [] |
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self.results = [] |
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self.new_sentences_token_info = [] |
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self.new_sentences_bio = [] |
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self.BIO_TAGS = [] |
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self.stripped_BIO_TAGS = [] |
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def read_file(self): |
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#Reading a txt file with one document per line. |
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with open(self.filename, 'r') as f: |
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text = f.read() |
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self.sentences = self.sent_detector.tokenize(text.strip()) |
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def process_sentences(self): #We split long sentences as encoder has a 256 max-lenght. Sentences with les of 40 words will be merged. |
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for sentence in self.sentences: |
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if len(sentence.split()) < 40 and self.new_sentences: |
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self.new_sentences[-1] += " " + sentence |
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else: |
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self.new_sentences.append(sentence) |
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def apply_model(self, pipe): |
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self.results = list(map(pipe, self.new_sentences)) |
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self.results=[[[y["entity"],y["word"], y["start"], y["end"]] for y in x] for x in self.results] |
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def tokenize_sentences(self): |
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for n_s in self.new_sentences: |
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tokens=n_s.split() # Basic tokenization |
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token_info = [] |
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# Initialize a variable to keep track of character index |
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char_index = 0 |
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# Iterate through the tokens and record start and end info |
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for token in tokens: |
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start = char_index |
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end = char_index + len(token) # Subtract 1 for the last character of the token |
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token_info.append((token, start, end)) |
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char_index += len(token) + 1 # Add 1 for the whitespace |
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self.new_sentences_token_info.append(token_info) |
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def process_results(self): #merge subwords and BIO tags |
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for result in self.results: |
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merged_bio_result = [] |
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current_word = "" |
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current_label = None |
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current_start = None |
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current_end = None |
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for entity, subword, start, end in result: |
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if subword.startswith("▁"): |
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subword = subword[1:] |
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merged_bio_result.append([current_word, current_label, current_start, current_end]) |
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current_word = "" ; current_label = None ; current_start = None ; current_end = None |
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if current_start is None: |
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current_word = subword ; current_label = entity ; current_start = start+1 ; current_end= end |
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else: |
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current_word += subword ; current_end = end |
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if current_word: |
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merged_bio_result.append([current_word, current_label, current_start, current_end]) |
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self.new_sentences_bio.append(merged_bio_result[1:]) |
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def match_tokens_with_entities(self): #match BIO tags with tokens |
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for i,ss in enumerate(self.new_sentences_token_info): |
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for word in ss: |
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for ent in self.new_sentences_bio[i]: |
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if word[1]==ent[2]: |
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if ent[1]=="L-PERS": |
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self.BIO_TAGS.append([word[0], "I-PERS", "B-LOC"]) |
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break |
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else: |
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if "LOC" in ent[1]: |
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self.BIO_TAGS.append([word[0], "O", ent[1]]) |
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else: |
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self.BIO_TAGS.append([word[0], ent[1], "O"]) |
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break |
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else: |
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self.BIO_TAGS.append([word[0], "O", "O"]) |
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def separate_dots_and_comma(self): #optional |
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signs=[",", ";", ":", "."] |
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for bio in self.BIO_TAGS: |
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if any(bio[0][-1]==sign for sign in signs) and len(bio[0])>1: |
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self.stripped_BIO_TAGS.append([bio[0][:-1], bio[1], bio[2]]); |
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self.stripped_BIO_TAGS.append([bio[0][-1], "O", "O"]) |
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else: |
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self.stripped_BIO_TAGS.append(bio) |
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def save_BIO(self): |
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with open('output_BIO_a.txt', 'w', encoding='utf-8') as output_file: |
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output_file.write("TOKEN\tPERS\tLOCS\n"+"\n".join(["\t".join(x) for x in self.stripped_BIO_TAGS])) |
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# Usage: |
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processor = TextProcessor('my_docs_file.txt') |
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processor.read_file() |
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processor.process_sentences() |
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processor.apply_model(pipe) |
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processor.tokenize_sentences() |
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processor.process_results() |
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processor.match_tokens_with_entities() |
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processor.separate_dots_and_comma() |
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processor.save_BIO() |
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``` |
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- **Developed by:** [Sergio Torres Aguilar] |
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- **Model type:** [XLM-Roberta] |
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- **Language(s) (NLP):** [Medieval Latin, Spanish, French] |
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- **Finetuned from model [optional]:** [Named Entity Recognition] |
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### Direct Use |
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A sentence as : "Ego Radulfus de Francorvilla miles, notum facio tam presentibus cum futuris quod, cum Guillelmo Bateste militi de Miliaco" |
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Will be annotated in BIO format as: |
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```python |
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('Ego', 'O', 'O') |
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('Radulfus', 'B-PERS') |
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('de', 'I-PERS', 'O') |
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('Francorvilla', 'I-PERS', 'B-LOC') |
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('miles', 'O') |
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(',', 'O', 'O') |
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('notum', 'O', 'O') |
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('facio', 'O', 'O') |
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('tam', 'O', 'O') |
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('presentibus', 'O', 'O') |
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('quam', 'O', 'O') |
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('futuris', 'O', 'O') |
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('quod', 'O', 'O') |
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(',', 'O', 'O') |
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('cum', 'O', 'O') |
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('Guillelmo', 'B-PERS', 'O') |
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('Bateste', 'I-PERS', 'O') |
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('militi', 'O', 'O') |
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('de', 'O', 'O') |
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('Miliaco', 'O', 'B-LOC') |
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``` |
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### Training Procedure |
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The model was fine-tuned during 5 epoch on the XML-Roberta-Large using a 5e-5 Lr and a batch size of 16. |
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**BibTeX:** |
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```bibtex |
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@inproceedings{aguilar2022multilingual, |
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title={Multilingual Named Entity Recognition for Medieval Charters Using Stacked Embeddings and Bert-based Models.}, |
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author={Aguilar, Sergio Torres}, |
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booktitle={Proceedings of the second workshop on language technologies for historical and ancient languages}, |
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pages={119--128}, |
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year={2022} |
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} |
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``` |
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## Model Card Contact |
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[[email protected]] |
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