--- base_model: allenai/scibert_scivocab_uncased tags: - generated_from_trainer model-index: - name: BioNLP13CG_SciBERT_NER results: [] --- # BioNLP13CG_SciBERT_NER This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1817 - Seqeval classification report: precision recall f1-score support Amino_acid 0.54 0.43 0.48 89 Anatomical_system 0.00 0.00 0.00 41 Cancer 0.84 0.84 0.84 3620 Cell 0.00 0.00 0.00 11 Cellular_component 0.00 0.00 0.00 7 Developing_anatomical_structure 0.00 0.00 0.00 37 Gene_or_gene_product 0.90 0.92 0.91 540 Immaterial_anatomical_entity 0.63 0.65 0.64 82 Multi-tissue_structure 0.63 0.71 0.67 144 Organ 0.00 0.00 0.00 56 Organism 0.86 0.17 0.28 36 Organism_subdivision 0.83 0.86 0.84 1086 Organism_substance 0.87 0.81 0.84 484 Pathological_formation 0.92 0.92 0.92 1430 Simple_chemical 0.58 0.72 0.64 304 Tissue 0.79 0.82 0.80 341 micro avg 0.84 0.82 0.83 8308 macro avg 0.52 0.49 0.49 8308 weighted avg 0.82 0.82 0.82 8308 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - 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 | Seqeval classification report | |:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | No log | 0.99 | 95 | 0.2278 | precision recall f1-score support Amino_acid 0.48 0.15 0.22 89 Anatomical_system 0.00 0.00 0.00 41 Cancer 0.81 0.80 0.80 3620 Cell 0.00 0.00 0.00 11 Cellular_component 0.00 0.00 0.00 7 Developing_anatomical_structure 0.00 0.00 0.00 37 Gene_or_gene_product 0.80 0.90 0.84 540 Immaterial_anatomical_entity 0.48 0.59 0.52 82 Multi-tissue_structure 0.62 0.45 0.52 144 Organ 0.00 0.00 0.00 56 Organism 0.00 0.00 0.00 36 Organism_subdivision 0.75 0.84 0.79 1086 Organism_substance 0.83 0.77 0.80 484 Pathological_formation 0.90 0.86 0.88 1430 Simple_chemical 0.53 0.69 0.60 304 Tissue 0.74 0.73 0.73 341 micro avg 0.79 0.78 0.78 8308 macro avg 0.43 0.42 0.42 8308 weighted avg 0.77 0.78 0.77 8308 | | No log | 2.0 | 191 | 0.1850 | precision recall f1-score support Amino_acid 0.52 0.40 0.46 89 Anatomical_system 0.00 0.00 0.00 41 Cancer 0.83 0.84 0.84 3620 Cell 0.00 0.00 0.00 11 Cellular_component 0.00 0.00 0.00 7 Developing_anatomical_structure 0.00 0.00 0.00 37 Gene_or_gene_product 0.89 0.92 0.90 540 Immaterial_anatomical_entity 0.56 0.65 0.60 82 Multi-tissue_structure 0.60 0.69 0.64 144 Organ 0.00 0.00 0.00 56 Organism 1.00 0.17 0.29 36 Organism_subdivision 0.80 0.87 0.83 1086 Organism_substance 0.87 0.79 0.83 484 Pathological_formation 0.91 0.93 0.92 1430 Simple_chemical 0.57 0.72 0.64 304 Tissue 0.77 0.79 0.78 341 micro avg 0.82 0.83 0.82 8308 macro avg 0.52 0.49 0.48 8308 weighted avg 0.81 0.83 0.82 8308 | | No log | 2.98 | 285 | 0.1817 | precision recall f1-score support Amino_acid 0.54 0.43 0.48 89 Anatomical_system 0.00 0.00 0.00 41 Cancer 0.84 0.84 0.84 3620 Cell 0.00 0.00 0.00 11 Cellular_component 0.00 0.00 0.00 7 Developing_anatomical_structure 0.00 0.00 0.00 37 Gene_or_gene_product 0.90 0.92 0.91 540 Immaterial_anatomical_entity 0.63 0.65 0.64 82 Multi-tissue_structure 0.63 0.71 0.67 144 Organ 0.00 0.00 0.00 56 Organism 0.86 0.17 0.28 36 Organism_subdivision 0.83 0.86 0.84 1086 Organism_substance 0.87 0.81 0.84 484 Pathological_formation 0.92 0.92 0.92 1430 Simple_chemical 0.58 0.72 0.64 304 Tissue 0.79 0.82 0.80 341 micro avg 0.84 0.82 0.83 8308 macro avg 0.52 0.49 0.49 8308 weighted avg 0.82 0.82 0.82 8308 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0