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--- |
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license: mit |
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base_model: ai-forever/ruElectra-medium |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: rubert-electra-srl |
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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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# rubert-electra-srl |
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This model is a fine-tuned version of [ai-forever/ruElectra-medium](https://huggingface.co/ai-forever/ruElectra-medium) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0448 |
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- Addressee Precision: 0.9583 |
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- Addressee Recall: 1.0 |
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- Addressee F1: 0.9787 |
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- Addressee Number: 23 |
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- Benefactive Precision: 0.0 |
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- Benefactive Recall: 0.0 |
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- Benefactive F1: 0.0 |
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- Benefactive Number: 2 |
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- Causator Precision: 0.9773 |
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- Causator Recall: 0.9773 |
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- Causator F1: 0.9773 |
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- Causator Number: 44 |
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- Cause Precision: 0.9259 |
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- Cause Recall: 0.7143 |
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- Cause F1: 0.8065 |
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- Cause Number: 35 |
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- Contrsubject Precision: 1.0 |
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- Contrsubject Recall: 0.9429 |
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- Contrsubject F1: 0.9706 |
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- Contrsubject Number: 35 |
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- Deliberative Precision: 0.9231 |
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- Deliberative Recall: 1.0 |
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- Deliberative F1: 0.9600 |
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- Deliberative Number: 24 |
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- Destinative Precision: 1.0 |
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- Destinative Recall: 1.0 |
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- Destinative F1: 1.0 |
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- Destinative Number: 7 |
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- Directivefinal Precision: 1.0 |
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- Directivefinal Recall: 1.0 |
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- Directivefinal F1: 1.0 |
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- Directivefinal Number: 1 |
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- Experiencer Precision: 0.9030 |
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- Experiencer Recall: 0.9441 |
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- Experiencer F1: 0.9231 |
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- Experiencer Number: 286 |
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- Instrument Precision: 0.9 |
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- Instrument Recall: 0.9 |
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- Instrument F1: 0.9 |
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- Instrument Number: 10 |
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- Object Precision: 0.9484 |
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- Object Recall: 0.9519 |
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- Object F1: 0.9502 |
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- Object Number: 541 |
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- Overall Precision: 0.9369 |
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- Overall Recall: 0.9425 |
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- Overall F1: 0.9397 |
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- Overall Accuracy: 0.9883 |
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- Limitative F1: 0.0 |
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- Limitative Number: 0.0 |
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- Limitative Precision: 0.0 |
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- Limitative Recall: 0.0 |
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- Directiveinitial F1: 0.0 |
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- Directiveinitial Number: 0.0 |
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- Directiveinitial Precision: 0.0 |
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- Directiveinitial Recall: 0.0 |
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- Mediative F1: 0.0 |
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- Mediative Number: 0.0 |
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- Mediative Precision: 0.0 |
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- Mediative Recall: 0.0 |
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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: 0.00016666401556632117 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 708526 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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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_ratio: 0.21 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Addressee Precision | Addressee Recall | Addressee F1 | Addressee Number | Benefactive Precision | Benefactive Recall | Benefactive F1 | Benefactive Number | Causator Precision | Causator Recall | Causator F1 | Causator Number | Cause Precision | Cause Recall | Cause F1 | Cause Number | Contrsubject Precision | Contrsubject Recall | Contrsubject F1 | Contrsubject Number | Deliberative Precision | Deliberative Recall | Deliberative F1 | Deliberative Number | Destinative Precision | Destinative Recall | Destinative F1 | Destinative Number | Directivefinal Precision | Directivefinal Recall | Directivefinal F1 | Directivefinal Number | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Instrument Precision | Instrument Recall | Instrument F1 | Instrument Number | Object Precision | Object Recall | Object F1 | Object Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Limitative F1 | Limitative Number | Limitative Precision | Limitative Recall | Directiveinitial F1 | Directiveinitial Number | Directiveinitial Precision | Directiveinitial Recall | Mediative F1 | Mediative Number | Mediative Precision | Mediative Recall | |
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| 0.1548 | 1.0 | 1471 | 0.1755 | 0.6667 | 0.5217 | 0.5854 | 23 | 0.0 | 0.0 | 0.0 | 2 | 0.5714 | 0.8182 | 0.6729 | 44 | 0.5217 | 0.3429 | 0.4138 | 35 | 0.4103 | 0.4571 | 0.4324 | 35 | 0.0 | 0.0 | 0.0 | 24 | 0.0 | 0.0 | 0.0 | 7 | 0.0 | 0.0 | 0.0 | 1 | 0.8645 | 0.8252 | 0.8444 | 286 | 0.0 | 0.0 | 0.0 | 10 | 0.7711 | 0.8965 | 0.8291 | 541 | 0.7627 | 0.7907 | 0.7764 | 0.9582 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.1209 | 2.0 | 2942 | 0.0797 | 0.9130 | 0.9130 | 0.9130 | 23 | 0.0 | 0.0 | 0.0 | 2 | 0.9348 | 0.9773 | 0.9556 | 44 | 0.8462 | 0.6286 | 0.7213 | 35 | 0.8889 | 0.9143 | 0.9014 | 35 | 0.75 | 0.875 | 0.8077 | 24 | 1.0 | 0.4286 | 0.6 | 7 | 0.0 | 0.0 | 0.0 | 1 | 0.8993 | 0.8741 | 0.8865 | 286 | 0.875 | 0.7 | 0.7778 | 10 | 0.9336 | 0.9094 | 0.9213 | 541 | 0.9138 | 0.8839 | 0.8986 | 0.9808 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.0559 | 3.0 | 4413 | 0.0448 | 0.9583 | 1.0 | 0.9787 | 23 | 0.0 | 0.0 | 0.0 | 2 | 0.9773 | 0.9773 | 0.9773 | 44 | 0.9259 | 0.7143 | 0.8065 | 35 | 1.0 | 0.9429 | 0.9706 | 35 | 0.9231 | 1.0 | 0.9600 | 24 | 1.0 | 1.0 | 1.0 | 7 | 1.0 | 1.0 | 1.0 | 1 | 0.9030 | 0.9441 | 0.9231 | 286 | 0.9 | 0.9 | 0.9 | 10 | 0.9484 | 0.9519 | 0.9502 | 541 | 0.9369 | 0.9425 | 0.9397 | 0.9883 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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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 2.20.0 |
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- Tokenizers 0.19.1 |
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