End of training
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README.md
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---
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license: apache-2.0
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base_model: google-bert/bert-base-multilingual-uncased
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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: bert-base-multilingual-uncased-finetuned-ner-geocorpus
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results: []
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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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# bert-base-multilingual-uncased-finetuned-ner-geocorpus
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1337
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- Precision: 0.7867
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- Recall: 0.8827
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- F1: 0.8320
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- Accuracy: 0.9727
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 | 276 | 0.1785 | 0.6910 | 0.6597 | 0.6750 | 0.9527 |
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| 0.2507 | 2.0 | 552 | 0.1321 | 0.7761 | 0.7689 | 0.7725 | 0.9630 |
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| 0.2507 | 3.0 | 828 | 0.1158 | 0.7691 | 0.8165 | 0.7921 | 0.9669 |
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| 0.084 | 4.0 | 1104 | 0.1186 | 0.7503 | 0.8479 | 0.7961 | 0.9668 |
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| 0.084 | 5.0 | 1380 | 0.1287 | 0.7629 | 0.8560 | 0.8068 | 0.9657 |
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| 0.0443 | 6.0 | 1656 | 0.1295 | 0.7453 | 0.8769 | 0.8058 | 0.9666 |
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| 0.0443 | 7.0 | 1932 | 0.1423 | 0.7592 | 0.8862 | 0.8178 | 0.9685 |
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| 0.0243 | 8.0 | 2208 | 0.1267 | 0.7970 | 0.8664 | 0.8303 | 0.9724 |
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| 0.0243 | 9.0 | 2484 | 0.1309 | 0.7747 | 0.8746 | 0.8216 | 0.9710 |
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| 0.0164 | 10.0 | 2760 | 0.1337 | 0.7867 | 0.8827 | 0.8320 | 0.9727 |
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### Framework versions
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- Transformers 4.41.1
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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