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deb7145
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add byt5 eng-yor mt

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README.md ADDED
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+ Hugging Face's logo
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+ ---
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+ language:
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+ - yo
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+ - en
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+ datasets:
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+ - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
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+ ---
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+ # byt5-base-eng_yor_mt
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+ ## Model description
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+ **byt5_base_yor_eng_mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.
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+
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+ Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
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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 on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
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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 (BLEU score)
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+ Fine-tuning byt5-base achieves **12.23 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82
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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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+
config.json ADDED
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+ {
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+ "_name_or_path": "google/byt5-base",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 3968,
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+ "decoder_start_token_id": 0,
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "gated-gelu",
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+ "gradient_checkpointing": false,
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "num_decoder_layers": 6,
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+ "num_heads": 12,
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+ "num_layers": 18,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "relative_attention_num_buckets": 32,
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+ "tie_word_embeddings": false,
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+ "tokenizer_class": "ByT5Tokenizer",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.9.1",
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+ "use_cache": true,
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+ "vocab_size": 384
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+ }
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