Create README.md

README.md

@@ -0,0 +1,94 @@
+---
+license: apache-2.0
+language:
+- ig
+base_model:
+- Davlan/afro-xlmr-large
+---
+# masakhane/igbo-pos-tagger-afroxlmr
+## Model description
+**igbo-pos-tagger-afroxlmr** is a POS tagger for Igbo language based on [MasakhaPOS](https://github.com/masakhane-io/masakhane-pos) dataset. 
+## Intended uses & limitations
+#### How to use
+You can use this model with Transformers *pipeline* for POS.
+```python
+
+from transformers import AutoTokenizer, AutoModelForTokenClassification, TokenClassificationPipeline
+
+model_name = "masakhane/igbo-pos-tagger-afroxlmr"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
+outputs = pipeline("Nke a na-abịa dịka Trump rụtụrụ aka na inyefe okeala bụ ihe nwereike ịkwụsị agha dị n'etiti mba abụọ ahụ.")
+print(outputs)
+
+
+```
+#### Limitations and bias
+This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.  
+## Training data
+This model was fine-tuned on Igbo POS dataset with the [UD POS tags](https://universaldependencies.org/u/pos/)
+
+### BibTeX entry and citation info
+```
+@inproceedings{dione-etal-2023-masakhapos,
+    title = "{M}asakha{POS}: Part-of-Speech Tagging for Typologically Diverse {A}frican languages",
+    author = "Dione, Cheikh M. Bamba  and
+      Adelani, David Ifeoluwa  and
+      Nabende, Peter  and
+      Alabi, Jesujoba  and
+      Sindane, Thapelo  and
+      Buzaaba, Happy  and
+      Muhammad, Shamsuddeen Hassan  and
+      Emezue, Chris Chinenye  and
+      Ogayo, Perez  and
+      Aremu, Anuoluwapo  and
+      Gitau, Catherine  and
+      Mbaye, Derguene  and
+      Mukiibi, Jonathan  and
+      Sibanda, Blessing  and
+      Dossou, Bonaventure F. P.  and
+      Bukula, Andiswa  and
+      Mabuya, Rooweither  and
+      Tapo, Allahsera Auguste  and
+      Munkoh-Buabeng, Edwin  and
+      Memdjokam Koagne, Victoire  and
+      Ouoba Kabore, Fatoumata  and
+      Taylor, Amelia  and
+      Kalipe, Godson  and
+      Macucwa, Tebogo  and
+      Marivate, Vukosi  and
+      Gwadabe, Tajuddeen  and
+      Elvis, Mboning Tchiaze  and
+      Onyenwe, Ikechukwu  and
+      Atindogbe, Gratien  and
+      Adelani, Tolulope  and
+      Akinade, Idris  and
+      Samuel, Olanrewaju  and
+      Nahimana, Marien  and
+      Musabeyezu, Th{\'e}og{\`e}ne  and
+      Niyomutabazi, Emile  and
+      Chimhenga, Ester  and
+      Gotosa, Kudzai  and
+      Mizha, Patrick  and
+      Agbolo, Apelete  and
+      Traore, Seydou  and
+      Uchechukwu, Chinedu  and
+      Yusuf, Aliyu  and
+      Abdullahi, Muhammad  and
+      Klakow, Dietrich",
+    editor = "Rogers, Anna  and
+      Boyd-Graber, Jordan  and
+      Okazaki, Naoaki",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-long.609/",
+    doi = "10.18653/v1/2023.acl-long.609",
+    pages = "10883--10900",
+    abstract = "In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages."
+}
+```