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BioNLP13CG_SciBERT_NER

judithrosell / BioNLP13CG_SciBERT_NER

Run on huggingface.co
huggingface.co
Total runs: 10
24-hour runs: 0
7-day runs: 1
30-day runs: 6
Model's Last Updated: December 18 2023
token-classification

Introduction of BioNLP13CG_SciBERT_NER

Model Details of BioNLP13CG_SciBERT_NER

BioNLP13CG_SciBERT_NER

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: 0.1817

  • 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

Runs of judithrosell BioNLP13CG_SciBERT_NER on huggingface.co

10
Total runs
0
24-hour runs
2
3-day runs
1
7-day runs
6
30-day runs

More Information About BioNLP13CG_SciBERT_NER huggingface.co Model

BioNLP13CG_SciBERT_NER huggingface.co

BioNLP13CG_SciBERT_NER huggingface.co is an AI model on huggingface.co that provides BioNLP13CG_SciBERT_NER's model effect (), which can be used instantly with this judithrosell BioNLP13CG_SciBERT_NER model. huggingface.co supports a free trial of the BioNLP13CG_SciBERT_NER model, and also provides paid use of the BioNLP13CG_SciBERT_NER. Support call BioNLP13CG_SciBERT_NER model through api, including Node.js, Python, http.

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judithrosell BioNLP13CG_SciBERT_NER online free

BioNLP13CG_SciBERT_NER huggingface.co is an online trial and call api platform, which integrates BioNLP13CG_SciBERT_NER's modeling effects, including api services, and provides a free online trial of BioNLP13CG_SciBERT_NER, you can try BioNLP13CG_SciBERT_NER online for free by clicking the link below.

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https://huggingface.co/judithrosell/BioNLP13CG_SciBERT_NER

BioNLP13CG_SciBERT_NER install

BioNLP13CG_SciBERT_NER is an open source model from GitHub that offers a free installation service, and any user can find BioNLP13CG_SciBERT_NER on GitHub to install. At the same time, huggingface.co provides the effect of BioNLP13CG_SciBERT_NER install, users can directly use BioNLP13CG_SciBERT_NER installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

BioNLP13CG_SciBERT_NER install url in huggingface.co:

https://huggingface.co/judithrosell/BioNLP13CG_SciBERT_NER

Url of BioNLP13CG_SciBERT_NER

BioNLP13CG_SciBERT_NER huggingface.co Url

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judithrosell
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