File size: 10,304 Bytes
d4200be |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
model-index:
- name: BioNLP13CG_SciBERT_NER
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
|