# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("EMBO/SourceData_RolesMulti_v1_0_0_PubMedBERT_base")
model = AutoModelForTokenClassification.from_pretrained("EMBO/SourceData_RolesMulti_v1_0_0_PubMedBERT_base")Quick Links
SourceData_RolesMulti_v1_0_0_PubMedBERT_base
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract on the source_data dataset. It achieves the following results on the evaluation set:
- Loss: 0.0083
- Accuracy Score: 0.9978
- Precision: 0.9495
- Recall: 0.9608
- F1: 0.9551
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: 0.0001
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Use adafactor and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0052 | 1.0 | 864 | 0.0083 | 0.9978 | 0.9495 | 0.9608 | 0.9551 |
Framework versions
- Transformers 4.46.3
- Pytorch 1.13.1+cu117
- Datasets 3.1.0
- Tokenizers 0.20.3
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Evaluation results
- Precision on source_datavalidation set self-reported0.950
- Recall on source_datavalidation set self-reported0.961
- F1 on source_datavalidation set self-reported0.955
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EMBO/SourceData_RolesMulti_v1_0_0_PubMedBERT_base")