README.md

metadata

library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
  - token-classification
  - generated_from_trainer
datasets:
  - Rodrigo1771/drugtemist-it-fasttext-75-ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: Rodrigo1771/drugtemist-it-fasttext-75-ner
          type: Rodrigo1771/drugtemist-it-fasttext-75-ner
          config: DrugTEMIST Italian NER
          split: validation
          args: DrugTEMIST Italian NER
        metrics:
          - name: Precision
            type: precision
            value: 0.9169054441260746
          - name: Recall
            type: recall
            value: 0.9293320425943853
          - name: F1
            type: f1
            value: 0.9230769230769231
          - name: Accuracy
            type: accuracy
            value: 0.9986302259153467

output

This model is a fine-tuned version of IVN-RIN/bioBIT on the Rodrigo1771/drugtemist-it-fasttext-75-ner dataset. It achieves the following results on the evaluation set:

• Loss: 0.0094
• Precision: 0.9169
• Recall: 0.9293
• F1: 0.9231
• Accuracy: 0.9986

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.9990	485	0.0043	0.9146	0.9119	0.9132	0.9984
0.0127	2.0	971	0.0054	0.9162	0.9100	0.9131	0.9985
0.0033	2.9990	1456	0.0059	0.9210	0.9138	0.9174	0.9986
0.0016	4.0	1942	0.0062	0.9019	0.9255	0.9135	0.9986
0.001	4.9990	2427	0.0064	0.9181	0.9226	0.9203	0.9986
0.0006	6.0	2913	0.0081	0.9202	0.9158	0.9180	0.9986
0.0004	6.9990	3398	0.0085	0.9181	0.9226	0.9203	0.9986
0.0003	8.0	3884	0.0087	0.9215	0.9206	0.9211	0.9986
0.0002	8.9990	4369	0.0090	0.9093	0.9322	0.9207	0.9985
0.0001	9.9897	4850	0.0094	0.9169	0.9293	0.9231	0.9986

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1