README.md

metadata

library_name: transformers
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: BioMedRoBERTa-finetuned-ner-pablo-just-classifier
    results: []

BioMedRoBERTa-finetuned-ner-pablo-just-classifier

This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1150
• Precision: 0.6869
• Recall: 0.7076
• F1: 0.6971
• Accuracy: 0.9677

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: 0.1
• train_batch_size: 128
• eval_batch_size: 128
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 512
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine_with_restarts
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.9655	14	0.3729	0.4205	0.6119	0.4985	0.9430
No log	2.0	29	0.2544	0.5272	0.6683	0.5894	0.9574
No log	2.9655	43	0.2117	0.5702	0.6884	0.6238	0.9604
No log	4.0	58	0.1747	0.5934	0.7001	0.6424	0.9628
No log	4.9655	72	0.1420	0.6280	0.6827	0.6542	0.9642
No log	6.0	87	0.1287	0.6639	0.7033	0.6830	0.9667
No log	6.9655	101	0.1309	0.6471	0.7009	0.6729	0.9654
No log	8.0	116	0.1260	0.6349	0.7199	0.6748	0.9652
No log	8.9655	130	0.1159	0.6621	0.7118	0.6860	0.9670
No log	9.6552	140	0.1150	0.6869	0.7076	0.6971	0.9677

Framework versions

• Transformers 4.44.1
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1