metadata
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SciBERT_BioNLP13CG_NER_new
results: []
SciBERT_BioNLP13CG_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: 0.1760
- Precision: 0.9560
- Recall: 0.9595
- F1: 0.9577
- Accuracy: 0.9560
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 191 | 0.1970 | 0.9469 | 0.9507 | 0.9488 | 0.9474 |
| No log | 2.0 | 382 | 0.1724 | 0.9544 | 0.9584 | 0.9564 | 0.9551 |
| 0.2509 | 3.0 | 573 | 0.1760 | 0.9560 | 0.9595 | 0.9577 | 0.9560 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0