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--- |
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license: mit |
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base_model: 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-generic_ner_ontonotes-ner-2024_08_14 |
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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-generic_ner_ontonotes-ner-2024_08_14 |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0851 |
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- Precision: 0.8634 |
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- Recall: 0.8879 |
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- F1: 0.8755 |
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- Accuracy: 0.9783 |
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- O Precision: 0.9952 |
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- O Recall: 0.9917 |
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- O F1: 0.9934 |
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- B-cardinal Precision: 0.8585 |
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- B-cardinal Recall: 0.8994 |
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- B-cardinal F1: 0.8784 |
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- B-date Precision: 0.8627 |
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- B-date Recall: 0.8796 |
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- B-date F1: 0.8711 |
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- I-date Precision: 0.8742 |
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- I-date Recall: 0.9023 |
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- I-date F1: 0.8880 |
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- B-person Precision: 0.9204 |
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- B-person Recall: 0.9596 |
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- B-person F1: 0.9396 |
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- I-person Precision: 0.9452 |
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- I-person Recall: 0.9818 |
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- I-person F1: 0.9632 |
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- B-norp Precision: 0.8898 |
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- B-norp Recall: 0.9311 |
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- B-norp F1: 0.9100 |
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- B-gpe Precision: 0.9471 |
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- B-gpe Recall: 0.9395 |
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- B-gpe F1: 0.9433 |
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- I-gpe Precision: 0.9119 |
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- I-gpe Recall: 0.8846 |
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- I-gpe F1: 0.8980 |
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- B-law Precision: 0.5909 |
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- B-law Recall: 0.8667 |
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- B-law F1: 0.7027 |
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- I-law Precision: 0.5170 |
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- I-law Recall: 0.7982 |
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- I-law F1: 0.6276 |
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- B-org Precision: 0.9061 |
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- B-org Recall: 0.8716 |
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- B-org F1: 0.8885 |
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- I-org Precision: 0.9212 |
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- I-org Recall: 0.9075 |
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- I-org F1: 0.9143 |
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- B-percent Precision: 0.9321 |
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- B-percent Recall: 0.8996 |
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- B-percent F1: 0.9156 |
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- I-percent Precision: 0.8822 |
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- I-percent Recall: 0.9887 |
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- I-percent F1: 0.9324 |
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- B-ordinal Precision: 0.8356 |
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- B-ordinal Recall: 0.8356 |
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- B-ordinal F1: 0.8356 |
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- B-money Precision: 0.9051 |
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- B-money Recall: 0.9304 |
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- B-money F1: 0.9176 |
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- I-money Precision: 0.9372 |
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- I-money Recall: 0.9753 |
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- I-money F1: 0.9558 |
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- B-work Of Art Precision: 0.5354 |
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- B-work Of Art Recall: 0.6355 |
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- B-work Of Art F1: 0.5812 |
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- I-work Of Art Precision: 0.5849 |
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- I-work Of Art Recall: 0.6998 |
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- I-work Of Art F1: 0.6372 |
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- B-fac Precision: 0.4833 |
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- B-fac Recall: 0.6312 |
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- B-fac F1: 0.5474 |
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- B-time Precision: 0.7782 |
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- B-time Recall: 0.8299 |
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- B-time F1: 0.8032 |
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- I-cardinal Precision: 0.7683 |
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- I-cardinal Recall: 0.8892 |
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- I-cardinal F1: 0.8243 |
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- B-loc Precision: 0.8206 |
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- B-loc Recall: 0.7530 |
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- B-loc F1: 0.7854 |
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- B-quantity Precision: 0.8731 |
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- B-quantity Recall: 0.9 |
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- B-quantity F1: 0.8864 |
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- I-quantity Precision: 0.8889 |
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- I-quantity Recall: 0.9706 |
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- I-quantity F1: 0.9279 |
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- I-norp Precision: 0.6792 |
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- I-norp Recall: 0.5373 |
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- I-norp F1: 0.6000 |
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- I-loc Precision: 0.7721 |
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- I-loc Recall: 0.7692 |
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- I-loc F1: 0.7706 |
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- B-product Precision: 0.5447 |
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- B-product Recall: 0.6979 |
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- B-product F1: 0.6119 |
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- I-time Precision: 0.7694 |
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- I-time Recall: 0.8766 |
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- I-time F1: 0.8195 |
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- B-event Precision: 0.7308 |
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- B-event Recall: 0.5733 |
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- B-event F1: 0.6425 |
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- I-event Precision: 0.7951 |
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- I-event Recall: 0.6198 |
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- I-event F1: 0.6966 |
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- I-fac Precision: 0.6463 |
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- I-fac Recall: 0.6909 |
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- I-fac F1: 0.6678 |
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- B-language Precision: 0.8387 |
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- B-language Recall: 0.65 |
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- B-language F1: 0.7324 |
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- I-product Precision: 0.8480 |
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- I-product Recall: 0.8192 |
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- I-product F1: 0.8333 |
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- I-ordinal Precision: 1.0 |
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- I-ordinal Recall: 0.0 |
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- I-ordinal F1: 0.0 |
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- I-language Precision: 1.0 |
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- I-language Recall: 1.0 |
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- I-language F1: 1.0 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | O Precision | O Recall | O F1 | B-cardinal Precision | B-cardinal Recall | B-cardinal F1 | B-date Precision | B-date Recall | B-date F1 | I-date Precision | I-date Recall | I-date F1 | B-person Precision | B-person Recall | B-person F1 | I-person Precision | I-person Recall | I-person F1 | B-norp Precision | B-norp Recall | B-norp F1 | B-gpe Precision | B-gpe Recall | B-gpe F1 | I-gpe Precision | I-gpe Recall | I-gpe F1 | B-law Precision | B-law Recall | B-law F1 | I-law Precision | I-law Recall | I-law F1 | B-org Precision | B-org Recall | B-org F1 | I-org Precision | I-org Recall | I-org F1 | B-percent Precision | B-percent Recall | B-percent F1 | I-percent Precision | I-percent Recall | I-percent F1 | B-ordinal Precision | B-ordinal Recall | B-ordinal F1 | B-money Precision | B-money Recall | B-money F1 | I-money Precision | I-money Recall | I-money F1 | B-work Of Art Precision | B-work Of Art Recall | B-work Of Art F1 | I-work Of Art Precision | I-work Of Art Recall | I-work Of Art F1 | B-fac Precision | B-fac Recall | B-fac F1 | B-time Precision | B-time Recall | B-time F1 | I-cardinal Precision | I-cardinal Recall | I-cardinal F1 | B-loc Precision | B-loc Recall | B-loc F1 | B-quantity Precision | B-quantity Recall | B-quantity F1 | I-quantity Precision | I-quantity Recall | I-quantity F1 | I-norp Precision | I-norp Recall | I-norp F1 | I-loc Precision | I-loc Recall | I-loc F1 | B-product Precision | B-product Recall | B-product F1 | I-time Precision | I-time Recall | I-time F1 | B-event Precision | B-event Recall | B-event F1 | I-event Precision | I-event Recall | I-event F1 | I-fac Precision | I-fac Recall | I-fac F1 | B-language Precision | B-language Recall | B-language F1 | I-product Precision | I-product Recall | I-product F1 | I-ordinal Precision | I-ordinal Recall | I-ordinal F1 | I-language Precision | I-language Recall | I-language F1 | |
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| 0.43 | 0.3332 | 1248 | 0.1141 | 0.7842 | 0.8162 | 0.7999 | 0.9680 | 0.9940 | 0.9886 | 0.9913 | 0.8180 | 0.8605 | 0.8387 | 0.8343 | 0.8244 | 0.8294 | 0.8103 | 0.9023 | 0.8538 | 0.8776 | 0.9537 | 0.9141 | 0.9234 | 0.9693 | 0.9458 | 0.8485 | 0.9146 | 0.8803 | 0.9480 | 0.8583 | 0.9009 | 0.8588 | 0.7931 | 0.8246 | 1.0 | 0.0 | 0.0 | 0.3333 | 0.6754 | 0.4464 | 0.8220 | 0.8229 | 0.8224 | 0.8696 | 0.8734 | 0.8715 | 0.8902 | 0.9563 | 0.9221 | 0.9170 | 0.9170 | 0.9170 | 0.825 | 0.7911 | 0.8077 | 0.7884 | 0.8719 | 0.8280 | 0.9129 | 0.9593 | 0.9355 | 0.3367 | 0.1542 | 0.2115 | 0.4216 | 0.6862 | 0.5223 | 0.4031 | 0.325 | 0.3599 | 0.6897 | 0.4149 | 0.5181 | 0.6894 | 0.9125 | 0.7854 | 0.4729 | 0.6128 | 0.5339 | 0.8872 | 0.9077 | 0.8973 | 0.8442 | 0.9559 | 0.8966 | 0.75 | 0.0448 | 0.0845 | 0.5819 | 0.6374 | 0.6084 | 0.2260 | 0.6875 | 0.3402 | 0.7253 | 0.7437 | 0.7344 | 0.6857 | 0.2069 | 0.3179 | 0.5475 | 0.6832 | 0.6078 | 0.4843 | 0.6727 | 0.5632 | 1.0 | 0.025 | 0.0488 | 0.5455 | 0.1695 | 0.2586 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.105 | 0.6663 | 2496 | 0.0947 | 0.8324 | 0.8524 | 0.8423 | 0.9745 | 0.9920 | 0.9929 | 0.9924 | 0.8393 | 0.8623 | 0.8507 | 0.8746 | 0.8149 | 0.8437 | 0.8787 | 0.8613 | 0.8699 | 0.9319 | 0.9185 | 0.9252 | 0.9546 | 0.9673 | 0.9609 | 0.8904 | 0.9124 | 0.9012 | 0.8901 | 0.9615 | 0.9244 | 0.8396 | 0.9025 | 0.8699 | 0.6 | 0.4 | 0.48 | 0.4565 | 0.5526 | 0.5 | 0.8549 | 0.8488 | 0.8519 | 0.9132 | 0.8534 | 0.8823 | 0.9579 | 0.8952 | 0.9255 | 0.9203 | 0.9585 | 0.9390 | 0.8270 | 0.8185 | 0.8227 | 0.8616 | 0.9192 | 0.8895 | 0.9241 | 0.9738 | 0.9483 | 0.5193 | 0.5654 | 0.5414 | 0.5861 | 0.6682 | 0.6245 | 0.4261 | 0.4688 | 0.4464 | 0.7593 | 0.6805 | 0.7177 | 0.8645 | 0.8367 | 0.8504 | 0.8719 | 0.5396 | 0.6667 | 0.8769 | 0.8769 | 0.8769 | 0.8629 | 0.9485 | 0.9037 | 0.7179 | 0.4179 | 0.5283 | 0.8259 | 0.6081 | 0.7004 | 0.4889 | 0.6875 | 0.5714 | 0.74 | 0.8196 | 0.7778 | 0.7566 | 0.4957 | 0.5990 | 0.7438 | 0.6556 | 0.6969 | 0.6076 | 0.6364 | 0.6217 | 0.5246 | 0.8 | 0.6337 | 0.8489 | 0.6667 | 0.7468 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0911 | 0.9995 | 3744 | 0.0930 | 0.8390 | 0.8678 | 0.8531 | 0.9746 | 0.9946 | 0.9902 | 0.9924 | 0.8656 | 0.8358 | 0.8505 | 0.8726 | 0.8452 | 0.8587 | 0.8527 | 0.9116 | 0.8812 | 0.8653 | 0.9684 | 0.9140 | 0.9111 | 0.9855 | 0.9468 | 0.8994 | 0.9176 | 0.9084 | 0.9277 | 0.9374 | 0.9325 | 0.8923 | 0.8696 | 0.8808 | 0.5667 | 0.7556 | 0.6476 | 0.4024 | 0.5789 | 0.4748 | 0.8818 | 0.8469 | 0.8640 | 0.8967 | 0.8967 | 0.8967 | 0.9327 | 0.9083 | 0.9204 | 0.8680 | 0.9925 | 0.9261 | 0.8854 | 0.7671 | 0.8220 | 0.8770 | 0.9331 | 0.9042 | 0.9266 | 0.9724 | 0.9489 | 0.4681 | 0.6168 | 0.5323 | 0.5297 | 0.6637 | 0.5892 | 0.5475 | 0.6125 | 0.5782 | 0.72 | 0.7469 | 0.7332 | 0.7570 | 0.8717 | 0.8103 | 0.8699 | 0.6524 | 0.7456 | 0.8188 | 0.8692 | 0.8433 | 0.8248 | 0.9522 | 0.8840 | 0.6364 | 0.5224 | 0.5738 | 0.8806 | 0.6484 | 0.7468 | 0.5138 | 0.5833 | 0.5463 | 0.7733 | 0.8418 | 0.8061 | 0.7319 | 0.4353 | 0.5459 | 0.7147 | 0.6556 | 0.6839 | 0.6151 | 0.6218 | 0.6184 | 0.7429 | 0.65 | 0.6933 | 0.7669 | 0.7062 | 0.7353 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0687 | 1.3326 | 4992 | 0.0859 | 0.8593 | 0.8739 | 0.8665 | 0.9773 | 0.9938 | 0.9923 | 0.9930 | 0.8537 | 0.8959 | 0.8742 | 0.8901 | 0.8505 | 0.8699 | 0.8793 | 0.8914 | 0.8853 | 0.9347 | 0.9473 | 0.9410 | 0.9343 | 0.9822 | 0.9577 | 0.9099 | 0.9154 | 0.9126 | 0.9510 | 0.9254 | 0.9380 | 0.8698 | 0.8816 | 0.8757 | 0.6383 | 0.6667 | 0.6522 | 0.6562 | 0.5526 | 0.6 | 0.8763 | 0.8787 | 0.8775 | 0.8960 | 0.9126 | 0.9042 | 0.9298 | 0.9258 | 0.9278 | 0.8931 | 0.9774 | 0.9333 | 0.8322 | 0.8151 | 0.8235 | 0.9126 | 0.9304 | 0.9214 | 0.9372 | 0.9753 | 0.9558 | 0.5642 | 0.5748 | 0.5694 | 0.5895 | 0.6614 | 0.6234 | 0.4837 | 0.5563 | 0.5174 | 0.7592 | 0.7718 | 0.7654 | 0.7927 | 0.8805 | 0.8343 | 0.7432 | 0.75 | 0.7466 | 0.8551 | 0.9077 | 0.8806 | 0.8961 | 0.9191 | 0.9074 | 0.75 | 0.4925 | 0.5946 | 0.7807 | 0.7692 | 0.7749 | 0.5285 | 0.6771 | 0.5936 | 0.7326 | 0.8671 | 0.7942 | 0.7314 | 0.5517 | 0.6290 | 0.7697 | 0.6722 | 0.7176 | 0.6778 | 0.6655 | 0.6716 | 0.7368 | 0.7 | 0.7179 | 0.8531 | 0.6893 | 0.7625 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0638 | 1.6658 | 6240 | 0.0855 | 0.8512 | 0.8825 | 0.8665 | 0.9769 | 0.9947 | 0.9912 | 0.9930 | 0.8476 | 0.8985 | 0.8723 | 0.8660 | 0.8737 | 0.8698 | 0.8713 | 0.8919 | 0.8815 | 0.9208 | 0.9577 | 0.9389 | 0.9413 | 0.9786 | 0.9596 | 0.9022 | 0.9258 | 0.9139 | 0.9359 | 0.9535 | 0.9446 | 0.9280 | 0.8891 | 0.9081 | 0.5818 | 0.7111 | 0.64 | 0.5780 | 0.5526 | 0.5650 | 0.8955 | 0.8685 | 0.8818 | 0.9273 | 0.8887 | 0.9076 | 0.9185 | 0.9345 | 0.9264 | 0.9231 | 0.9509 | 0.9368 | 0.8530 | 0.8151 | 0.8336 | 0.9187 | 0.9443 | 0.9313 | 0.9579 | 0.9593 | 0.9586 | 0.4462 | 0.6776 | 0.5380 | 0.4534 | 0.7472 | 0.5644 | 0.4554 | 0.6375 | 0.5312 | 0.7578 | 0.8050 | 0.7807 | 0.7677 | 0.8863 | 0.8227 | 0.8517 | 0.6829 | 0.7580 | 0.8369 | 0.9077 | 0.8708 | 0.8854 | 0.9375 | 0.9107 | 0.8919 | 0.4925 | 0.6346 | 0.8918 | 0.6337 | 0.7409 | 0.5349 | 0.7188 | 0.6133 | 0.7778 | 0.8418 | 0.8085 | 0.7590 | 0.5431 | 0.6332 | 0.7812 | 0.6198 | 0.6912 | 0.5287 | 0.6691 | 0.5907 | 0.8214 | 0.575 | 0.6765 | 0.8447 | 0.7684 | 0.8047 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.059 | 1.9989 | 7488 | 0.0828 | 0.8576 | 0.8837 | 0.8705 | 0.9778 | 0.9945 | 0.9919 | 0.9932 | 0.8352 | 0.9038 | 0.8682 | 0.8651 | 0.8749 | 0.8699 | 0.8784 | 0.8875 | 0.8829 | 0.9139 | 0.9589 | 0.9359 | 0.9361 | 0.9826 | 0.9588 | 0.8975 | 0.9311 | 0.9140 | 0.9323 | 0.9481 | 0.9401 | 0.8806 | 0.9175 | 0.8987 | 0.6 | 0.6667 | 0.6316 | 0.576 | 0.6316 | 0.6025 | 0.8991 | 0.8673 | 0.8829 | 0.9162 | 0.9005 | 0.9083 | 0.9330 | 0.9127 | 0.9227 | 0.8966 | 0.9811 | 0.9369 | 0.7907 | 0.8151 | 0.8027 | 0.8939 | 0.9387 | 0.9158 | 0.9448 | 0.9709 | 0.9577 | 0.5774 | 0.6449 | 0.6093 | 0.6834 | 0.6772 | 0.6803 | 0.5024 | 0.6438 | 0.5644 | 0.7870 | 0.7510 | 0.7686 | 0.7655 | 0.8659 | 0.8126 | 0.8710 | 0.6585 | 0.75 | 0.8992 | 0.8923 | 0.8958 | 0.8969 | 0.9596 | 0.9272 | 0.8571 | 0.5373 | 0.6606 | 0.8517 | 0.6520 | 0.7386 | 0.5610 | 0.7188 | 0.6301 | 0.8185 | 0.8133 | 0.8159 | 0.7396 | 0.6121 | 0.6698 | 0.8033 | 0.6749 | 0.7335 | 0.5574 | 0.7418 | 0.6365 | 0.9259 | 0.625 | 0.7463 | 0.8333 | 0.7627 | 0.7965 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0469 | 2.3321 | 8736 | 0.0843 | 0.8674 | 0.8854 | 0.8763 | 0.9785 | 0.9944 | 0.9921 | 0.9932 | 0.8763 | 0.8817 | 0.8790 | 0.8636 | 0.8826 | 0.8730 | 0.8583 | 0.9116 | 0.8842 | 0.9310 | 0.9556 | 0.9432 | 0.9548 | 0.9818 | 0.9681 | 0.8977 | 0.9266 | 0.9119 | 0.9468 | 0.9398 | 0.9433 | 0.9367 | 0.8876 | 0.9115 | 0.6 | 0.8 | 0.6857 | 0.6083 | 0.6404 | 0.6239 | 0.9025 | 0.8724 | 0.8872 | 0.9201 | 0.9043 | 0.9121 | 0.9361 | 0.8952 | 0.9152 | 0.8680 | 0.9925 | 0.9261 | 0.8339 | 0.8253 | 0.8296 | 0.9066 | 0.9192 | 0.9129 | 0.9385 | 0.9767 | 0.9573 | 0.5744 | 0.6495 | 0.6096 | 0.6268 | 0.6975 | 0.6603 | 0.4928 | 0.6375 | 0.5559 | 0.8 | 0.7801 | 0.7899 | 0.7943 | 0.9009 | 0.8443 | 0.7980 | 0.7226 | 0.7584 | 0.8992 | 0.8923 | 0.8958 | 0.8859 | 0.9706 | 0.9263 | 0.8182 | 0.5373 | 0.6486 | 0.8016 | 0.7399 | 0.7695 | 0.5185 | 0.7292 | 0.6061 | 0.7604 | 0.8639 | 0.8089 | 0.7273 | 0.6207 | 0.6698 | 0.7753 | 0.6749 | 0.7216 | 0.6168 | 0.72 | 0.6644 | 0.9259 | 0.625 | 0.7463 | 0.8114 | 0.8023 | 0.8068 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0439 | 2.6652 | 9984 | 0.0868 | 0.8635 | 0.8846 | 0.8739 | 0.9781 | 0.9953 | 0.9914 | 0.9934 | 0.8517 | 0.8923 | 0.8716 | 0.8660 | 0.8778 | 0.8719 | 0.8604 | 0.9097 | 0.8844 | 0.9214 | 0.9623 | 0.9414 | 0.9377 | 0.9842 | 0.9604 | 0.8797 | 0.9258 | 0.9022 | 0.9402 | 0.9323 | 0.9362 | 0.8920 | 0.9040 | 0.8980 | 0.6066 | 0.8222 | 0.6981 | 0.4802 | 0.7456 | 0.5842 | 0.9044 | 0.8693 | 0.8865 | 0.9143 | 0.9122 | 0.9133 | 0.9321 | 0.8996 | 0.9156 | 0.8763 | 0.9887 | 0.9291 | 0.8385 | 0.8356 | 0.8370 | 0.9046 | 0.9248 | 0.9146 | 0.9321 | 0.9782 | 0.9546 | 0.6207 | 0.5888 | 0.6043 | 0.6463 | 0.6930 | 0.6688 | 0.5575 | 0.6062 | 0.5808 | 0.7787 | 0.8174 | 0.7976 | 0.7440 | 0.9067 | 0.8173 | 0.7508 | 0.7530 | 0.7519 | 0.8712 | 0.8846 | 0.8779 | 0.88 | 0.9706 | 0.9231 | 0.6792 | 0.5373 | 0.6000 | 0.7948 | 0.7802 | 0.7874 | 0.5378 | 0.6667 | 0.5953 | 0.7744 | 0.8797 | 0.8237 | 0.7366 | 0.5905 | 0.6555 | 0.7912 | 0.6474 | 0.7121 | 0.6809 | 0.6982 | 0.6894 | 0.6744 | 0.725 | 0.6988 | 0.7967 | 0.8192 | 0.8078 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
|
| 0.0435 | 2.9984 | 11232 | 0.0851 | 0.8634 | 0.8879 | 0.8755 | 0.9783 | 0.9952 | 0.9917 | 0.9934 | 0.8585 | 0.8994 | 0.8784 | 0.8627 | 0.8796 | 0.8711 | 0.8742 | 0.9023 | 0.8880 | 0.9204 | 0.9596 | 0.9396 | 0.9452 | 0.9818 | 0.9632 | 0.8898 | 0.9311 | 0.9100 | 0.9471 | 0.9395 | 0.9433 | 0.9119 | 0.8846 | 0.8980 | 0.5909 | 0.8667 | 0.7027 | 0.5170 | 0.7982 | 0.6276 | 0.9061 | 0.8716 | 0.8885 | 0.9212 | 0.9075 | 0.9143 | 0.9321 | 0.8996 | 0.9156 | 0.8822 | 0.9887 | 0.9324 | 0.8356 | 0.8356 | 0.8356 | 0.9051 | 0.9304 | 0.9176 | 0.9372 | 0.9753 | 0.9558 | 0.5354 | 0.6355 | 0.5812 | 0.5849 | 0.6998 | 0.6372 | 0.4833 | 0.6312 | 0.5474 | 0.7782 | 0.8299 | 0.8032 | 0.7683 | 0.8892 | 0.8243 | 0.8206 | 0.7530 | 0.7854 | 0.8731 | 0.9 | 0.8864 | 0.8889 | 0.9706 | 0.9279 | 0.6792 | 0.5373 | 0.6000 | 0.7721 | 0.7692 | 0.7706 | 0.5447 | 0.6979 | 0.6119 | 0.7694 | 0.8766 | 0.8195 | 0.7308 | 0.5733 | 0.6425 | 0.7951 | 0.6198 | 0.6966 | 0.6463 | 0.6909 | 0.6678 | 0.8387 | 0.65 | 0.7324 | 0.8480 | 0.8192 | 0.8333 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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