add yoruba adr model

README.md

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+Hugging Face's logo
+---
+language: yo
+datasets:
+- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
+---
+# mT5_base_yoruba_adr
+## Model description
+**mT5_base_yoruba_adr** is a **automatic diacritics restoration** model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the **state-of-the-art performance** for adding the correct diacritics or tonal marks to Yorùbá texts.  
+
+Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and  [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
+## Intended uses & limitations
+#### How to use
+You can use this model with Transformers *pipeline* for NER.
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("")
+model = AutoModelForTokenClassification.from_pretrained("")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
+ner_results = nlp(example)
+print(ner_results)
+```
+#### 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 on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
+
+## Training procedure
+This model was trained on a single NVIDIA V100 GPU
+
+## Eval results on Test set (BLEU score)
+64.63 BLEU on [Global Voices test set](https://arxiv.org/abs/2003.10564)
+70.27 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)
+
+### BibTeX entry and citation info
+```
+
+```
+
+

config.json

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+{
+  "_name_or_path": "google/mt5-base",
+  "architectures": [
+    "MT5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "transformers_version": "4.4.1",
+  "use_cache": true,
+  "vocab_size": 250112
+}

model_args.json

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optimizer.pt

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special_tokens_map.json

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tokenizer_config.json

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training_args.bin

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