nhanv commited on
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upload model

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README.md CHANGED
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: vi-word-segmentation
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # vi-word-segmentation
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+
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+ This model is a fine-tuned version of [NlpHUST/electra-base-vn](https://huggingface.co/NlpHUST/electra-base-vn) on an vlsp 2013 word segmentation dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0501
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+ - Precision: 0.9833
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+ - Recall: 0.9838
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+ - F1: 0.9835
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+ - Accuracy: 0.9911
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ You can use this model with Transformers *pipeline* for NER.
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+
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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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+
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+ tokenizer = AutoTokenizer.from_pretrained("NlpHUST/vi-word-segmentation")
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+ model = AutoModelForTokenClassification.from_pretrained("NlpHUST/vi-word-segmentation")
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+
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+ nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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+ example = "Phát biểu tại phiên thảo luận về tình hình kinh tế xã hội của Quốc hội sáng 28/10 , Bộ trưởng Bộ LĐ-TB&XH Đào Ngọc Dung khái quát , tại phiên khai mạc kỳ họp , lãnh đạo chính phủ đã báo cáo , đề cập tương đối rõ ràng về việc thực hiện các chính sách an sinh xã hội"
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+
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+ ner_results = nlp(example)
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+ print(ner_results)
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+
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+ Phát_biểu tại phiên thảo_luận về tình_hình kinh_tế xã_hội của Quốc_hội sáng 28 / 10 , Bộ_trưởng Bộ LĐ - TB [UNK] XH Đào_Ngọc_Dung khái_quát , tại phiên khai_mạc kỳ họp , lãnh_đạo chính_phủ đã báo_cáo , đề_cập tương_đối rõ_ràng về việc thực_hiện các chính_sách an_sinh xã_hội
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+
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+ ```
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 16
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0168 | 1.0 | 4712 | 0.0284 | 0.9813 | 0.9825 | 0.9819 | 0.9904 |
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+ | 0.0107 | 2.0 | 9424 | 0.0350 | 0.9789 | 0.9814 | 0.9802 | 0.9895 |
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+ | 0.005 | 3.0 | 14136 | 0.0364 | 0.9826 | 0.9843 | 0.9835 | 0.9909 |
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+ | 0.0033 | 4.0 | 18848 | 0.0434 | 0.9830 | 0.9831 | 0.9830 | 0.9908 |
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+ | 0.0017 | 5.0 | 23560 | 0.0501 | 0.9833 | 0.9838 | 0.9835 | 0.9911 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.22.2
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.4.0
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+ - Tokenizers 0.12.1
config.json ADDED
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+ {
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+ "architectures": [
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+ "ElectraForTokenClassification"
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+ ],
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+ "finetuning_task": "ner",
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+ "I": 1
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "electra",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "summary_activation": "gelu",
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+ "summary_last_dropout": 0.1,
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+ "summary_type": "first",
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+ "summary_use_proj": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.22.2",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 62000
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+ }
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tokenizer.json ADDED
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tokenizer_config.json ADDED
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+ {
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+ }
vocab.txt ADDED
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