adding in Hausa XLM-R

README.md

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+Hugging Face's logo
+---
+language: ha
+datasets:
+
+---
+# xlm-roberta-base-finetuned-swahili
+## Model description
+**xlm-roberta-base-finetuned-swahili** is a **Swahili RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Swahili language texts.  It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.  
+
+Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Swahili corpus. 
+## Intended uses & limitations
+#### How to use
+You can use this model with Transformers *pipeline* for masked token prediction.
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-swahili')
+>>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko <mask> kwamba hakuna uhalifu ulitendwa")
+                    
+[{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Ufaransa kwamba hakuna uhalifu ulitendwa', 
+'score': 0.5077782273292542, 
+'token': 190096, 
+'token_str': 'Ufaransa'}, 
+{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa', 
+'score': 0.3657738268375397, 
+'token': 7270, 
+'token_str': 'Paris'}, 
+{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Gabon kwamba hakuna uhalifu ulitendwa', 
+'score': 0.01592041552066803, 
+'token': 176392, 
+'token_str': 'Gabon'}, 
+{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa', 
+'score': 0.010881908237934113, 
+'token': 9942, 
+'token_str': 'France'}, 
+{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Marseille kwamba hakuna uhalifu ulitendwa', 
+'score': 0.009554869495332241, 
+'token': 185918, 
+'token_str': 'Marseille'}]
+
+
+```
+#### 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 [Swahili CC-100](http://data.statmt.org/cc-100/)
+
+## Training procedure
+This model was trained on a single NVIDIA V100 GPU
+
+## Eval results on Test set (F-score, average over 5 runs)
+Dataset| XLM-R F1 | sw_roberta F1
+-|-|-
+[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.37 | 89.74 
+
+### BibTeX entry and citation info
+By David Adelani
+```
+
+```
+
+

config.json

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+{
+  "_name_or_path": "xlm-roberta-base",
+  "architectures": [
+    "XLMRobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.4.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

pytorch_model.bin

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sentencepiece.bpe.model

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special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

tokenizer_config.json

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+{"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "xlm-roberta-base"}

training_args.bin

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