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--- |
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base_model: FacebookAI/xlm-roberta-base |
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library_name: transformers |
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license: mit |
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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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tags: |
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- generated_from_trainer |
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model-index: |
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- name: scenario-kd-pre-ner-full-xlmr_data-univner_full44 |
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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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# scenario-kd-pre-ner-full-xlmr_data-univner_full44 |
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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: 48.3210 |
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- Precision: 0.8129 |
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- Recall: 0.8285 |
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- F1: 0.8206 |
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- Accuracy: 0.9813 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 44 |
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- gradient_accumulation_steps: 4 |
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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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| 126.6202 | 0.2911 | 500 | 87.2751 | 0.7296 | 0.7159 | 0.7227 | 0.9734 | |
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| 79.5244 | 0.5822 | 1000 | 74.0801 | 0.7585 | 0.7846 | 0.7713 | 0.9774 | |
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| 71.0482 | 0.8732 | 1500 | 69.1703 | 0.7824 | 0.7813 | 0.7818 | 0.9785 | |
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| 66.2795 | 1.1643 | 2000 | 65.4836 | 0.8008 | 0.7984 | 0.7996 | 0.9800 | |
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| 62.9041 | 1.4554 | 2500 | 63.7689 | 0.8089 | 0.7806 | 0.7945 | 0.9794 | |
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| 60.9832 | 1.7465 | 3000 | 61.2403 | 0.7992 | 0.8150 | 0.8071 | 0.9804 | |
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| 59.0595 | 2.0375 | 3500 | 59.8787 | 0.7874 | 0.8251 | 0.8058 | 0.9800 | |
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| 56.95 | 2.3286 | 4000 | 58.4894 | 0.8164 | 0.8054 | 0.8109 | 0.9807 | |
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| 55.6946 | 2.6197 | 4500 | 57.2299 | 0.8001 | 0.8189 | 0.8094 | 0.9809 | |
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| 54.6743 | 2.9108 | 5000 | 56.1128 | 0.8083 | 0.8231 | 0.8156 | 0.9809 | |
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| 53.4043 | 3.2019 | 5500 | 55.4426 | 0.8135 | 0.8080 | 0.8107 | 0.9806 | |
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| 52.4045 | 3.4929 | 6000 | 54.4679 | 0.7964 | 0.8325 | 0.8141 | 0.9809 | |
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| 51.6726 | 3.7840 | 6500 | 53.7073 | 0.8099 | 0.8306 | 0.8201 | 0.9813 | |
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| 50.959 | 4.0751 | 7000 | 53.1030 | 0.8108 | 0.8272 | 0.8189 | 0.9812 | |
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| 50.2364 | 4.3662 | 7500 | 52.5818 | 0.8055 | 0.8342 | 0.8196 | 0.9815 | |
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| 49.7482 | 4.6573 | 8000 | 52.1174 | 0.8119 | 0.8306 | 0.8211 | 0.9816 | |
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| 49.2511 | 4.9483 | 8500 | 51.6036 | 0.8157 | 0.8214 | 0.8185 | 0.9812 | |
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| 48.5192 | 5.2394 | 9000 | 51.1405 | 0.8103 | 0.8285 | 0.8193 | 0.9810 | |
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| 48.1685 | 5.5305 | 9500 | 50.7789 | 0.8189 | 0.8300 | 0.8244 | 0.9818 | |
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| 47.9619 | 5.8216 | 10000 | 50.5119 | 0.8044 | 0.8344 | 0.8191 | 0.9813 | |
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| 47.4978 | 6.1126 | 10500 | 50.1726 | 0.817 | 0.8251 | 0.8210 | 0.9818 | |
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| 47.1816 | 6.4037 | 11000 | 49.9418 | 0.8162 | 0.8260 | 0.8211 | 0.9819 | |
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| 46.9279 | 6.6948 | 11500 | 49.6736 | 0.8223 | 0.8290 | 0.8256 | 0.9818 | |
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| 46.5873 | 6.9859 | 12000 | 49.4774 | 0.8228 | 0.8303 | 0.8266 | 0.9821 | |
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| 46.3493 | 7.2770 | 12500 | 49.1917 | 0.8194 | 0.8270 | 0.8232 | 0.9817 | |
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| 46.133 | 7.5680 | 13000 | 48.9379 | 0.8227 | 0.8370 | 0.8298 | 0.9821 | |
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| 46.0883 | 7.8591 | 13500 | 48.9742 | 0.8248 | 0.8254 | 0.8251 | 0.9819 | |
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| 45.812 | 8.1502 | 14000 | 48.6892 | 0.8200 | 0.8332 | 0.8265 | 0.9818 | |
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| 45.6582 | 8.4413 | 14500 | 48.5991 | 0.8153 | 0.8339 | 0.8245 | 0.9820 | |
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| 45.6454 | 8.7324 | 15000 | 48.5257 | 0.8204 | 0.8290 | 0.8247 | 0.9819 | |
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| 45.4808 | 9.0234 | 15500 | 48.4097 | 0.8107 | 0.8270 | 0.8188 | 0.9815 | |
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| 45.3486 | 9.3145 | 16000 | 48.4108 | 0.8191 | 0.8290 | 0.8240 | 0.9819 | |
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| 45.2876 | 9.6056 | 16500 | 48.3039 | 0.8148 | 0.8326 | 0.8236 | 0.9819 | |
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| 45.3167 | 9.8967 | 17000 | 48.3210 | 0.8129 | 0.8285 | 0.8206 | 0.9813 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
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