adding mt5_base_yor_eng_mt model
Browse files- README.md +42 -0
- config.json +28 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- spiece.model +3 -0
- tokenizer_config.json +1 -0
- trainer_state.json +1823 -0
- training_args.bin +3 -0
README.md
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Hugging Face's logo
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---
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language: yo
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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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# mT5_base_yoruba_adr
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## Model description
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**mT5_base_yor_eng_mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.
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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)
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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 ADR.
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```python
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from transformers import AutoTokenizer, AutoModelForTokenClassification
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from transformers import pipeline
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tokenizer = AutoTokenizer.from_pretrained("")
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model = AutoModelForTokenClassification.from_pretrained("")
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nlp = pipeline("", model=model, tokenizer=tokenizer)
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example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
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ner_results = nlp(example)
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print(ner_results)
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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 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
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## Training procedure
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This model was trained on a single NVIDIA V100 GPU
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## Eval results on Test set (BLEU score)
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15.57 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)
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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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config.json
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{
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"_name_or_path": "google/mt5-base",
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"architectures": [
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"MT5ForConditionalGeneration"
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],
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"d_ff": 2048,
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"d_kv": 64,
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"d_model": 768,
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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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"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": "mt5",
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"num_decoder_layers": 12,
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"num_heads": 12,
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"num_layers": 12,
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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": "T5Tokenizer",
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"transformers_version": "4.4.2",
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"use_cache": true,
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"vocab_size": 250112
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:4b4979a822497b043e28f59ed760b5adc4248b003a6e9d9927dafffd98492b33
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size 2329707353
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special_tokens_map.json
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{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
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spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:ef78f86560d809067d12bac6c09f19a462cb3af3f54d2b8acbba26e1433125d6
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size 4309802
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tokenizer_config.json
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{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 0, "additional_special_tokens": null, "special_tokens_map_file": "/home/patrick/.cache/torch/transformers/685ac0ca8568ec593a48b61b0a3c272beee9bc194a3c7241d15dcadb5f875e53.f76030f3ec1b96a8199b2593390c610e76ca8028ef3d24680000619ffb646276", "name_or_path": "google/mt5-base"}
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trainer_state.json
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