--- base_model: allenai/scibert_scivocab_uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: SciBERT_twowayloss_25K_bs64 results: [] --- # SciBERT_twowayloss_25K_bs64 This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0158 - Accuracy: 0.9945 - Precision: 0.7948 - Recall: 0.5830 - F1: 0.6727 - Hamming: 0.0055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 25000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| | 0.0332 | 0.16 | 5000 | 0.0283 | 0.9921 | 0.8249 | 0.2410 | 0.3730 | 0.0079 | | 0.0195 | 0.32 | 10000 | 0.0186 | 0.9939 | 0.7964 | 0.4983 | 0.6131 | 0.0061 | | 0.0173 | 0.47 | 15000 | 0.0168 | 0.9943 | 0.7936 | 0.5587 | 0.6557 | 0.0057 | | 0.0165 | 0.63 | 20000 | 0.0161 | 0.9944 | 0.7949 | 0.5782 | 0.6694 | 0.0056 | | 0.0161 | 0.79 | 25000 | 0.0158 | 0.9945 | 0.7948 | 0.5830 | 0.6727 | 0.0055 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.14.1