metadata
library_name: transformers
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
model-index:
- name: scibert-acm-multilabel
results: []
scibert-acm-multilabel
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: 0.0238
- F1 Average Per Sample: 0.4069
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Average Per Sample |
|---|---|---|---|---|
| 0.0565 | 1.0 | 5000 | 0.0291 | 0.2295 |
| 0.0513 | 2.0 | 10000 | 0.0251 | 0.3690 |
| 0.0459 | 3.0 | 15000 | 0.0240 | 0.3994 |
| 0.0443 | 4.0 | 20000 | 0.0238 | 0.4069 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2