How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("token-classification", model="HiTZ/xlm-roberta-large-lemma-pl")
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("HiTZ/xlm-roberta-large-lemma-pl")
model = AutoModelForTokenClassification.from_pretrained("HiTZ/xlm-roberta-large-lemma-pl", device_map="auto")
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Model Card for xlm-roberta-large-lemma-pl

This model is a fine-tuned version of xlm-roberta-large for the contextual lemmatization task.
The datasets used for training are extracted from the data of the SIGMORPHON 2019 Shared Task.
The model for the Polish language was trained using LFG corpus.

Training Hyperparameters

SEED: 42
EPOCHS: 20
BATCH SIZE: 4
GRADIENT ACCUMULATION STEPS: 2
LEARNING RATE: 0.00002
WARMUP: 0.06
WEIGHT DECAY: 0.1

Results

For more details you can see the paper and the repository:

Contact: Olia Toporkov and Rodrigo Agerri HiTZ Center - Ixa, University of the Basque Country UPV/EHU
Funding:
Model type: xlm-roberta-large
Language(s) (NLP): Polish
License: apache-2.0

Citation

@article{10.1162/coli_a_00497,
    author = {Toporkov, Olia and Agerri, Rodrigo},
    title = "{On the Role of Morphological Information for Contextual
                    Lemmatization}",
    journal = {Computational Linguistics},
    volume = {50},
    number = {1},
    pages = {157-191},
    year = {2024},
    month = {03},
    issn = {0891-2017},
    doi = {10.1162/coli_a_00497},
    url = {https://doi.org/10.1162/coli\_a\_00497},
    eprint = {https://direct.mit.edu/coli/article-pdf/50/1/157/2367156/coli\_a\_00497.pdf},
}
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