metadata
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SciBERT_JNLPBA_NER_new
results: []
SciBERT_JNLPBA_NER_new
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: 0.1515
- Precision: 0.9549
- Recall: 0.9516
- F1: 0.9532
- Accuracy: 0.9511
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1681 | 1.0 | 1164 | 0.1500 | 0.9540 | 0.9493 | 0.9516 | 0.9492 |
| 0.1281 | 2.0 | 2328 | 0.1519 | 0.9537 | 0.9497 | 0.9517 | 0.9492 |
| 0.1019 | 3.0 | 3492 | 0.1515 | 0.9549 | 0.9516 | 0.9532 | 0.9511 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0