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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-large |
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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-large-finetuned-ner-harem |
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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-large-finetuned-ner-harem |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1622 |
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- Precision: 0.8344 |
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- Recall: 0.8412 |
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- F1: 0.8378 |
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- Accuracy: 0.9745 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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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 | 0.9938 | 140 | 0.1806 | 0.6310 | 0.6557 | 0.6431 | 0.9533 | |
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| No log | 1.9947 | 281 | 0.1334 | 0.7314 | 0.7691 | 0.7497 | 0.9642 | |
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| No log | 2.9956 | 422 | 0.1332 | 0.7751 | 0.8103 | 0.7923 | 0.9712 | |
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| 0.2049 | 3.9965 | 563 | 0.1133 | 0.7948 | 0.8144 | 0.8045 | 0.9706 | |
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| 0.2049 | 4.9973 | 704 | 0.1215 | 0.814 | 0.8392 | 0.8264 | 0.9748 | |
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| 0.2049 | 5.9982 | 845 | 0.1274 | 0.8097 | 0.8247 | 0.8172 | 0.9726 | |
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| 0.2049 | 6.9991 | 986 | 0.1725 | 0.8079 | 0.8062 | 0.8070 | 0.9687 | |
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| 0.0307 | 8.0 | 1127 | 0.1647 | 0.8396 | 0.8309 | 0.8352 | 0.9736 | |
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| 0.0307 | 8.9938 | 1267 | 0.1678 | 0.8420 | 0.8351 | 0.8385 | 0.9726 | |
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| 0.0307 | 9.9379 | 1400 | 0.1622 | 0.8344 | 0.8412 | 0.8378 | 0.9745 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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