djsull/aha_sentence_classification

1---2library_name: transformers3license: apache-2.04base_model: skt/A.X-Encoder-base5tags:6- generated_from_trainer7metrics:8- accuracy9model-index:10- name: aha_sentence_classification11  results: []12---13 14<!-- This model card has been generated automatically according to the information the Trainer had access to. You15should probably proofread and complete it, then remove this comment. -->16 17# aha_sentence_classification18 19This model is a fine-tuned version of [skt/A.X-Encoder-base](https://huggingface.co/skt/A.X-Encoder-base) on an unknown dataset.20It achieves the following results on the evaluation set:21- Loss: 0.845422- Accuracy: 0.690023- F1 Micro: 0.690024- F1 Macro: 0.650325- Precision Macro: 0.607826- Recall Macro: 0.722127 28## Model description29 30More information needed31 32## Intended uses & limitations33 34More information needed35 36## Training and evaluation data37 38More information needed39 40## Training procedure41 42### Training hyperparameters43 44The following hyperparameters were used during training:45- learning_rate: 2e-0546- train_batch_size: 6447- eval_batch_size: 6448- seed: 4249- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments50- lr_scheduler_type: cosine51- lr_scheduler_warmup_ratio: 0.152- num_epochs: 2553 54### Training results55 56| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Macro | Recall Macro |57|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:------------:|58| 0.9702        | 0.5949 | 1000  | 1.1520          | 0.5590   | 0.5590   | 0.5444   | 0.5142          | 0.6791       |59| 0.7293        | 1.1898 | 2000  | 1.0469          | 0.5992   | 0.5992   | 0.5966   | 0.5599          | 0.7238       |60| 0.7779        | 1.7847 | 3000  | 0.9977          | 0.6278   | 0.6278   | 0.5964   | 0.5646          | 0.7274       |61| 0.5545        | 2.3795 | 4000  | 0.9847          | 0.6290   | 0.6290   | 0.6208   | 0.5849          | 0.7236       |62| 0.5692        | 2.9744 | 5000  | 0.8454          | 0.6900   | 0.6900   | 0.6503   | 0.6078          | 0.7221       |63| 0.3962        | 3.5693 | 6000  | 1.0074          | 0.6488   | 0.6488   | 0.6316   | 0.6093          | 0.7081       |64| 0.1624        | 4.1642 | 7000  | 1.1059          | 0.6732   | 0.6732   | 0.6533   | 0.6322          | 0.6930       |65| 0.1816        | 4.7591 | 8000  | 1.1277          | 0.6872   | 0.6872   | 0.6513   | 0.6429          | 0.6690       |66| 0.0934        | 5.3540 | 9000  | 1.4084          | 0.6882   | 0.6882   | 0.6468   | 0.6380          | 0.6649       |67| 0.0882        | 5.9488 | 10000 | 1.4941          | 0.6918   | 0.6918   | 0.6450   | 0.6428          | 0.6606       |68 69 70### Framework versions71 72- Transformers 4.56.173- Pytorch 2.7.0+cu12674- Tokenizers 0.22.075