Davlan commited on
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adding xlmr igbo

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README.md ADDED
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+ Hugging Face's logo
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+ ---
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+ language: ig
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+ datasets:
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+
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+ ---
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+ # xlm-roberta-base-finetuned-igbo
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+ ## Model description
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+ **xlm-roberta-base-finetuned-igbo** is a **Igbo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.
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+
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+ Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Igbo corpus.
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+ ## Intended uses & limitations
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+ #### How to use
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+ You can use this model with Transformers *pipeline* for masked token prediction.
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+ ```python
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+ >>> from transformers import pipeline
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+ >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-igbo')
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+ >>> unmasker("Reno Omokri na Gọọmentị <mask> enweghị ihe ha ga-eji hiwe ya bụ mmachi.")
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+
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+
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+
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+ ```
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+ #### Limitations and bias
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+ 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.
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+ ## Training data
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+ This model was fine-tuned on JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/)
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+
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+ ## Training procedure
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+ This model was trained on a single NVIDIA V100 GPU
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+
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+ ## Eval results on Test set (F-score, average over 5 runs)
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+ Dataset| XLM-R F1 | ig_roberta F1
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+ -|-|-
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+ [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 84.51 | 88.76
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+
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+ ### BibTeX entry and citation info
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+ By David Adelani
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+ ```
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+
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+ ```
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+
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+
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+ "XLMRobertaForMaskedLM"
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+ "gradient_checkpointing": false,
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+ "initializer_range": 0.02,
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+ "max_position_embeddings": 514,
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+ "model_type": "xlm-roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "output_past": true,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.4.2",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 250002
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+ }
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