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Training complete

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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: roberta-base
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+ tags:
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+ - bert-ner-address-3
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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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+ model-index:
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+ - name: ner-results-2
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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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+ # ner-results-2
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0131
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+ - Precision: 0.9940
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+ - Recall: 0.9953
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+ - F1: 0.9946
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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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+ More information needed
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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: 32
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 2
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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 |
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+ |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|
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+ | 0.0271 | 1.0 | 71290 | 0.0140 | 0.9942 | 0.9942 | 0.9942 |
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+ | 0.0055 | 2.0 | 142580 | 0.0107 | 0.9950 | 0.9961 | 0.9955 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.3
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+ - Pytorch 2.4.0
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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+ "RobertaForTokenClassification"
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+ ],
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "layer_norm_eps": 1e-05,
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+ "model_type": "roberta",
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+ "position_embedding_type": "absolute",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 50265
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+ }
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