metadata
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SciBERT_AsymmetricLoss_25K_bs64_P1_N1
results: []
SciBERT_AsymmetricLoss_25K_bs64_P1_N1
This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 67.0896
- Accuracy: 0.9945
- Precision: 0.7586
- Recall: 0.6438
- F1: 0.6965
- 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 |
|---|---|---|---|---|---|---|---|---|
| 83.6475 | 0.16 | 5000 | 79.3653 | 0.9938 | 0.7361 | 0.5667 | 0.6404 | 0.0062 |
| 75.8712 | 0.32 | 10000 | 72.7250 | 0.9942 | 0.7513 | 0.6068 | 0.6714 | 0.0058 |
| 72.4202 | 0.47 | 15000 | 69.4174 | 0.9944 | 0.7568 | 0.6237 | 0.6838 | 0.0056 |
| 70.0693 | 0.63 | 20000 | 67.8098 | 0.9945 | 0.7561 | 0.6385 | 0.6923 | 0.0055 |
| 68.9765 | 0.79 | 25000 | 67.0896 | 0.9945 | 0.7586 | 0.6438 | 0.6965 | 0.0055 |
Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1