Token Classification
Transformers
PyTorch
Safetensors
Persian
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-fa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wietsedv/xlm-roberta-base-ft-udpos28-fa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wietsedv/xlm-roberta-base-ft-udpos28-fa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fa") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from wietsedv/xlm-roberta-base-ft-udpos28-fa: direct link, hf CLI and curl.
- Browser
- Download file 9.38 kB
-
https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-fa/resolve/f8c834d268564cc7b4810403931013402bf24249/README.md
- Command line
-
hf download hf://wietsedv/xlm-roberta-base-ft-udpos28-fa@f8c834d268564cc7b4810403931013402bf24249/README.md
-
curl -L -o README.md https://huggingface.co/wietsedv/xlm-roberta-base-ft-udpos28-fa/resolve/f8c834d268564cc7b4810403931013402bf24249/README.md
9.38 kB
| language: | |
| - fa | |
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - part-of-speech | |
| - token-classification | |
| datasets: | |
| - universal_dependencies | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: xlm-roberta-base-ft-udpos28-fa | |
| results: | |
| - task: | |
| type: token-classification | |
| name: Part-of-Speech Tagging | |
| dataset: | |
| type: universal_dependencies | |
| name: Universal Dependencies v2.8 | |
| metrics: | |
| - type: accuracy | |
| name: English Test accuracy | |
| value: 77.1 | |
| - type: accuracy | |
| name: Dutch Test accuracy | |
| value: 75.7 | |
| - type: accuracy | |
| name: German Test accuracy | |
| value: 75.4 | |
| - type: accuracy | |
| name: Italian Test accuracy | |
| value: 76.0 | |
| - type: accuracy | |
| name: French Test accuracy | |
| value: 73.7 | |
| - type: accuracy | |
| name: Spanish Test accuracy | |
| value: 76.7 | |
| - type: accuracy | |
| name: Russian Test accuracy | |
| value: 81.5 | |
| - type: accuracy | |
| name: Swedish Test accuracy | |
| value: 80.5 | |
| - type: accuracy | |
| name: Norwegian Test accuracy | |
| value: 79.6 | |
| - type: accuracy | |
| name: Danish Test accuracy | |
| value: 81.1 | |
| - type: accuracy | |
| name: Low Saxon Test accuracy | |
| value: 51.1 | |
| - type: accuracy | |
| name: Akkadian Test accuracy | |
| value: 40.8 | |
| - type: accuracy | |
| name: Armenian Test accuracy | |
| value: 79.5 | |
| - type: accuracy | |
| name: Welsh Test accuracy | |
| value: 72.1 | |
| - type: accuracy | |
| name: Old East Slavic Test accuracy | |
| value: 71.6 | |
| - type: accuracy | |
| name: Albanian Test accuracy | |
| value: 73.6 | |
| - type: accuracy | |
| name: Slovenian Test accuracy | |
| value: 74.0 | |
| - type: accuracy | |
| name: Guajajara Test accuracy | |
| value: 32.5 | |
| - type: accuracy | |
| name: Kurmanji Test accuracy | |
| value: 78.9 | |
| - type: accuracy | |
| name: Turkish Test accuracy | |
| value: 76.9 | |
| - type: accuracy | |
| name: Finnish Test accuracy | |
| value: 79.8 | |
| - type: accuracy | |
| name: Indonesian Test accuracy | |
| value: 76.1 | |
| - type: accuracy | |
| name: Ukrainian Test accuracy | |
| value: 80.0 | |
| - type: accuracy | |
| name: Polish Test accuracy | |
| value: 82.7 | |
| - type: accuracy | |
| name: Portuguese Test accuracy | |
| value: 77.8 | |
| - type: accuracy | |
| name: Kazakh Test accuracy | |
| value: 78.5 | |
| - type: accuracy | |
| name: Latin Test accuracy | |
| value: 73.5 | |
| - type: accuracy | |
| name: Old French Test accuracy | |
| value: 53.0 | |
| - type: accuracy | |
| name: Buryat Test accuracy | |
| value: 62.4 | |
| - type: accuracy | |
| name: Kaapor Test accuracy | |
| value: 19.6 | |
| - type: accuracy | |
| name: Korean Test accuracy | |
| value: 62.6 | |
| - type: accuracy | |
| name: Estonian Test accuracy | |
| value: 83.5 | |
| - type: accuracy | |
| name: Croatian Test accuracy | |
| value: 81.5 | |
| - type: accuracy | |
| name: Gothic Test accuracy | |
| value: 22.6 | |
| - type: accuracy | |
| name: Swiss German Test accuracy | |
| value: 50.2 | |
| - type: accuracy | |
| name: Assyrian Test accuracy | |
| value: 20.1 | |
| - type: accuracy | |
| name: North Sami Test accuracy | |
| value: 39.4 | |
| - type: accuracy | |
| name: Naija Test accuracy | |
| value: 39.6 | |
| - type: accuracy | |
| name: Latvian Test accuracy | |
| value: 80.0 | |
| - type: accuracy | |
| name: Chinese Test accuracy | |
| value: 41.2 | |
| - type: accuracy | |
| name: Tagalog Test accuracy | |
| value: 79.0 | |
| - type: accuracy | |
| name: Bambara Test accuracy | |
| value: 34.4 | |
| - type: accuracy | |
| name: Lithuanian Test accuracy | |
| value: 78.5 | |
| - type: accuracy | |
| name: Galician Test accuracy | |
| value: 74.9 | |
| - type: accuracy | |
| name: Vietnamese Test accuracy | |
| value: 57.0 | |
| - type: accuracy | |
| name: Greek Test accuracy | |
| value: 66.7 | |
| - type: accuracy | |
| name: Catalan Test accuracy | |
| value: 75.0 | |
| - type: accuracy | |
| name: Czech Test accuracy | |
| value: 80.3 | |
| - type: accuracy | |
| name: Erzya Test accuracy | |
| value: 47.8 | |
| - type: accuracy | |
| name: Bhojpuri Test accuracy | |
| value: 61.1 | |
| - type: accuracy | |
| name: Thai Test accuracy | |
| value: 53.6 | |
| - type: accuracy | |
| name: Marathi Test accuracy | |
| value: 84.0 | |
| - type: accuracy | |
| name: Basque Test accuracy | |
| value: 72.2 | |
| - type: accuracy | |
| name: Slovak Test accuracy | |
| value: 77.2 | |
| - type: accuracy | |
| name: Kiche Test accuracy | |
| value: 37.9 | |
| - type: accuracy | |
| name: Yoruba Test accuracy | |
| value: 30.8 | |
| - type: accuracy | |
| name: Warlpiri Test accuracy | |
| value: 35.2 | |
| - type: accuracy | |
| name: Tamil Test accuracy | |
| value: 82.0 | |
| - type: accuracy | |
| name: Maltese Test accuracy | |
| value: 30.5 | |
| - type: accuracy | |
| name: Ancient Greek Test accuracy | |
| value: 62.9 | |
| - type: accuracy | |
| name: Icelandic Test accuracy | |
| value: 82.3 | |
| - type: accuracy | |
| name: Mbya Guarani Test accuracy | |
| value: 31.0 | |
| - type: accuracy | |
| name: Urdu Test accuracy | |
| value: 74.4 | |
| - type: accuracy | |
| name: Romanian Test accuracy | |
| value: 79.2 | |
| - type: accuracy | |
| name: Persian Test accuracy | |
| value: 91.4 | |
| - type: accuracy | |
| name: Apurina Test accuracy | |
| value: 41.8 | |
| - type: accuracy | |
| name: Japanese Test accuracy | |
| value: 31.8 | |
| - type: accuracy | |
| name: Hungarian Test accuracy | |
| value: 71.0 | |
| - type: accuracy | |
| name: Hindi Test accuracy | |
| value: 79.2 | |
| - type: accuracy | |
| name: Classical Chinese Test accuracy | |
| value: 30.2 | |
| - type: accuracy | |
| name: Komi Permyak Test accuracy | |
| value: 46.7 | |
| - type: accuracy | |
| name: Faroese Test accuracy | |
| value: 73.4 | |
| - type: accuracy | |
| name: Sanskrit Test accuracy | |
| value: 35.1 | |
| - type: accuracy | |
| name: Livvi Test accuracy | |
| value: 63.9 | |
| - type: accuracy | |
| name: Arabic Test accuracy | |
| value: 81.7 | |
| - type: accuracy | |
| name: Wolof Test accuracy | |
| value: 35.7 | |
| - type: accuracy | |
| name: Bulgarian Test accuracy | |
| value: 83.5 | |
| - type: accuracy | |
| name: Akuntsu Test accuracy | |
| value: 37.5 | |
| - type: accuracy | |
| name: Makurap Test accuracy | |
| value: 15.1 | |
| - type: accuracy | |
| name: Kangri Test accuracy | |
| value: 52.3 | |
| - type: accuracy | |
| name: Breton Test accuracy | |
| value: 57.4 | |
| - type: accuracy | |
| name: Telugu Test accuracy | |
| value: 82.1 | |
| - type: accuracy | |
| name: Cantonese Test accuracy | |
| value: 45.4 | |
| - type: accuracy | |
| name: Old Church Slavonic Test accuracy | |
| value: 51.2 | |
| - type: accuracy | |
| name: Karelian Test accuracy | |
| value: 65.1 | |
| - type: accuracy | |
| name: Upper Sorbian Test accuracy | |
| value: 68.9 | |
| - type: accuracy | |
| name: South Levantine Arabic Test accuracy | |
| value: 69.7 | |
| - type: accuracy | |
| name: Komi Zyrian Test accuracy | |
| value: 41.6 | |
| - type: accuracy | |
| name: Irish Test accuracy | |
| value: 66.5 | |
| - type: accuracy | |
| name: Nayini Test accuracy | |
| value: 48.7 | |
| - type: accuracy | |
| name: Munduruku Test accuracy | |
| value: 24.9 | |
| - type: accuracy | |
| name: Manx Test accuracy | |
| value: 33.3 | |
| - type: accuracy | |
| name: Skolt Sami Test accuracy | |
| value: 38.3 | |
| - type: accuracy | |
| name: Afrikaans Test accuracy | |
| value: 75.4 | |
| - type: accuracy | |
| name: Old Turkish Test accuracy | |
| value: 37.1 | |
| - type: accuracy | |
| name: Tupinamba Test accuracy | |
| value: 39.2 | |
| - type: accuracy | |
| name: Belarusian Test accuracy | |
| value: 78.9 | |
| - type: accuracy | |
| name: Serbian Test accuracy | |
| value: 82.5 | |
| - type: accuracy | |
| name: Moksha Test accuracy | |
| value: 45.4 | |
| - type: accuracy | |
| name: Western Armenian Test accuracy | |
| value: 76.4 | |
| - type: accuracy | |
| name: Scottish Gaelic Test accuracy | |
| value: 54.7 | |
| - type: accuracy | |
| name: Khunsari Test accuracy | |
| value: 44.6 | |
| - type: accuracy | |
| name: Hebrew Test accuracy | |
| value: 89.6 | |
| - type: accuracy | |
| name: Uyghur Test accuracy | |
| value: 77.0 | |
| - type: accuracy | |
| name: Chukchi Test accuracy | |
| value: 33.6 | |
| # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Persian | |
| This model is part of our paper called: | |
| - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages | |
| Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | |
| ## Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForTokenClassification | |
| tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fa") | |
| model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fa") | |
| ``` | |