metadata
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SciBERT_AsymmetricLoss_25K_bs64_P4_N1
results: []
SciBERT_AsymmetricLoss_25K_bs64_P4_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: 30.2502
- Accuracy: 0.9871
- Precision: 0.4247
- Recall: 0.8998
- F1: 0.5770
- Hamming: 0.0129
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 |
|---|---|---|---|---|---|---|---|---|
| 36.6287 | 0.16 | 5000 | 34.9978 | 0.9852 | 0.3863 | 0.8728 | 0.5355 | 0.0148 |
| 33.8929 | 0.32 | 10000 | 32.4942 | 0.9857 | 0.3958 | 0.8901 | 0.5480 | 0.0143 |
| 32.5419 | 0.47 | 15000 | 31.3170 | 0.9867 | 0.4162 | 0.8941 | 0.5680 | 0.0133 |
| 31.565 | 0.63 | 20000 | 30.6092 | 0.9869 | 0.4201 | 0.8975 | 0.5723 | 0.0131 |
| 31.105 | 0.79 | 25000 | 30.2502 | 0.9871 | 0.4247 | 0.8998 | 0.5770 | 0.0129 |
Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1