End of training
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README.md
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---
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license: mit
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base_model: FacebookAI/xlm-roberta-base
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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: xlm-roberta-base-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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# xlm-roberta-base-finetuned-ner-geocorpus
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1081
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- Precision: 0.8117
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- Recall: 0.8791
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- F1: 0.8440
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- Accuracy: 0.9765
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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 | 0.2364 | 0.5253 | 0.4795 | 0.5014 | 0.9411 |
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| 0.3373 | 2.0 | 552 | 0.1581 | 0.7023 | 0.7592 | 0.7297 | 0.9616 |
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| 0.3373 | 3.0 | 828 | 0.1280 | 0.8232 | 0.7245 | 0.7707 | 0.9672 |
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| 0.1133 | 4.0 | 1104 | 0.1229 | 0.7239 | 0.8601 | 0.7862 | 0.9667 |
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| 0.1133 | 5.0 | 1380 | 0.1100 | 0.7764 | 0.8801 | 0.8250 | 0.9722 |
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| 0.0635 | 6.0 | 1656 | 0.0978 | 0.8065 | 0.8896 | 0.846 | 0.9772 |
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| 0.0635 | 7.0 | 1932 | 0.0942 | 0.8189 | 0.8749 | 0.8460 | 0.9774 |
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| 0.0416 | 8.0 | 2208 | 0.1083 | 0.8097 | 0.8591 | 0.8337 | 0.9756 |
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| 0.0416 | 9.0 | 2484 | 0.1062 | 0.8027 | 0.8896 | 0.8439 | 0.9767 |
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| 0.0292 | 10.0 | 2760 | 0.1081 | 0.8117 | 0.8791 | 0.8440 | 0.9765 |
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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 2.19.2
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- Tokenizers 0.19.1
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model.safetensors
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