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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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- f1 |
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model-index: |
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- name: distilbert-base-uncased_fine_tuned_title |
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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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# distilbert-base-uncased_fine_tuned_title |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2615 |
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- Accuracy: {'accuracy': 0.877634820695319} |
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- Recall: {'recall': 0.8474786132372805} |
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- Precision: {'precision': 0.8953502200023784} |
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- F1: {'f1': 0.8707569536806801} |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 1000 |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|:------------------------------:|:---------------------------------:|:--------------------------:| |
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| 0.3093 | 1.0 | 2284 | 0.3021 | {'accuracy': 0.8779085683000274} | {'recall': 0.8560333183250788} | {'precision': 0.8888499298737728} | {'f1': 0.8721330275229358} | |
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| 0.2459 | 2.0 | 4568 | 0.2909 | {'accuracy': 0.8894059676977827} | {'recall': 0.8513057181449797} | {'precision': 0.9153957879448076} | {'f1': 0.8821882654846612} | |
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| 0.1696 | 3.0 | 6852 | 0.3259 | {'accuracy': 0.8808102929099371} | {'recall': 0.8595227375056281} | {'precision': 0.8915353181552831} | {'f1': 0.875236403232277} | |
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| 0.1179 | 4.0 | 9136 | 0.4946 | {'accuracy': 0.8729811114152751} | {'recall': 0.8610986042323278} | {'precision': 0.8756868131868132} | {'f1': 0.8683314415437005} | |
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| 0.0775 | 5.0 | 11420 | 0.6547 | {'accuracy': 0.8708458800985491} | {'recall': 0.8041422782530392} | {'precision': 0.9202627850057967} | {'f1': 0.8582927854868745} | |
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| 0.0522 | 6.0 | 13704 | 0.6699 | {'accuracy': 0.8768683274021353} | {'recall': 0.8325078793336335} | {'precision': 0.9067058967757754} | {'f1': 0.8680241769849187} | |
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| 0.0406 | 7.0 | 15988 | 0.8149 | {'accuracy': 0.8739118532712838} | {'recall': 0.8330706888788834} | {'precision': 0.9002554433767181} | {'f1': 0.8653610055539316} | |
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| 0.0298 | 8.0 | 18272 | 0.8906 | {'accuracy': 0.8753353408157679} | {'recall': 0.8421882035119316} | {'precision': 0.8952973555103506} | {'f1': 0.8679310944840787} | |
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| 0.0217 | 9.0 | 20556 | 1.0192 | {'accuracy': 0.8754448398576512} | {'recall': 0.8624493471409275} | {'precision': 0.8791738382099827} | {'f1': 0.8707312915506562} | |
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| 0.017 | 10.0 | 22840 | 1.0550 | {'accuracy': 0.8758828360251848} | {'recall': 0.8556956325979289} | {'precision': 0.8852917200419238} | {'f1': 0.8702421155056951} | |
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| 0.0139 | 11.0 | 25124 | 1.0873 | {'accuracy': 0.8728716123733917} | {'recall': 0.8582845565060784} | {'precision': 0.8776473296500921} | {'f1': 0.8678579558388345} | |
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| 0.0114 | 12.0 | 27408 | 1.1506 | {'accuracy': 0.8716123733917328} | {'recall': 0.8628995947771274} | {'precision': 0.8718298646650745} | {'f1': 0.8673417435085139} | |
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| 0.0061 | 13.0 | 29692 | 1.2574 | {'accuracy': 0.8696961401587736} | {'recall': 0.874943719045475} | {'precision': 0.8596549435965495} | {'f1': 0.8672319535869686} | |
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| 0.0035 | 14.0 | 31976 | 1.2490 | {'accuracy': 0.8784560635094443} | {'recall': 0.85006753714543} | {'precision': 0.8947867298578199} | {'f1': 0.8718540752713001} | |
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| 0.0028 | 15.0 | 34260 | 1.2615 | {'accuracy': 0.877634820695319} | {'recall': 0.8474786132372805} | {'precision': 0.8953502200023784} | {'f1': 0.8707569536806801} | |
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### Framework versions |
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- Transformers 4.21.0 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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