electra-small-discriminator-zeroshot-v1.1-none
This model is a fine-tuned version of google/electra-small-discriminator on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3747
- F1 Macro: 0.4125
- F1 Micro: 0.4620
- Accuracy Balanced: 0.4701
- Accuracy: 0.4620
- Precision Macro: 0.5162
- Recall Macro: 0.4701
- Precision Micro: 0.4620
- Recall Micro: 0.4620
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Datasets | mnli_m | mnli_mm | fevernli | anli_r1 | anli_r2 | anli_r3 | wanli | lingnli | wellformedquery | rottentomatoes | amazonpolarity | imdb | yelpreviews | hatexplain | massive | banking77 | emotiondair | emocontext | empathetic | agnews | yahootopics | biasframes_sex | biasframes_offensive | biasframes_intent | financialphrasebank | appreviews | hateoffensive | trueteacher | spam | wikitoxic_toxicaggregated | wikitoxic_obscene | wikitoxic_identityhate | wikitoxic_threat | wikitoxic_insult | manifesto | capsotu |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Accuracy | 0.853 | 0.861 | 0.838 | 0.583 | 0.592 | 0.588 | 0.709 | 0.787 | 0.603 | 0.75 | 0.863 | 0.808 | 0.879 | 0.432 | 0.497 | 0.391 | 0.546 | 0.607 | 0.234 | 0.801 | 0.562 | 0.77 | 0.639 | 0.628 | 0.629 | 0.861 | 0.37 | 0.502 | 0.814 | 0.744 | 0.798 | 0.786 | 0.767 | 0.778 | 0.096 | 0.462 |
Inference text/sec (A100, batch=64) | 4180.0 | 4161.0 | 2824.0 | 3233.0 | 3243.0 | 3239.0 | 4494.0 | 4288.0 | 5222.0 | 4396.0 | 2563.0 | 888.0 | 1035.0 | 4326.0 | 5447.0 | 5221.0 | 4871.0 | 4971.0 | 2852.0 | 3946.0 | 1585.0 | 4274.0 | 4097.0 | 4109.0 | 4229.0 | 3468.0 | 4476.0 | 1198.0 | 4514.0 | 1360.0 | 1267.0 | 1287.0 | 1232.0 | 1314.0 | 3936.0 | 4116.0 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 80085
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
---|---|---|---|---|---|---|---|---|---|---|---|
0.4765 | 0.32 | 5000 | 0.5300 | 0.7326 | 0.7528 | 0.7329 | 0.7528 | 0.7322 | 0.7329 | 0.7528 | 0.7528 |
0.4408 | 0.65 | 10000 | 0.5099 | 0.7402 | 0.765 | 0.7359 | 0.765 | 0.7463 | 0.7359 | 0.765 | 0.765 |
0.4169 | 0.97 | 15000 | 0.4976 | 0.7473 | 0.7702 | 0.7439 | 0.7702 | 0.7517 | 0.7439 | 0.7702 | 0.7702 |
0.387 | 1.3 | 20000 | 0.4943 | 0.7525 | 0.7742 | 0.7498 | 0.7742 | 0.7559 | 0.7498 | 0.7742 | 0.7742 |
0.3905 | 1.62 | 25000 | 0.4931 | 0.7522 | 0.775 | 0.7484 | 0.775 | 0.7572 | 0.7484 | 0.775 | 0.775 |
0.4001 | 1.95 | 30000 | 0.4924 | 0.7544 | 0.7752 | 0.7524 | 0.7752 | 0.7568 | 0.7524 | 0.7752 | 0.7752 |
0.3995 | 2.27 | 35000 | 0.4900 | 0.7543 | 0.7758 | 0.7517 | 0.7758 | 0.7576 | 0.7517 | 0.7758 | 0.7758 |
0.3981 | 2.6 | 40000 | 0.4906 | 0.7529 | 0.7742 | 0.7504 | 0.7742 | 0.7558 | 0.7504 | 0.7742 | 0.7742 |
0.4232 | 2.92 | 45000 | 0.4904 | 0.7544 | 0.776 | 0.7516 | 0.776 | 0.7579 | 0.7516 | 0.776 | 0.776 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
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