metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ner_classifier_v2
results: []
ner_classifier_v2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3338
- F1: 0.8406
Model description
The DeepNeural NER-II model is designed to identify multiple enitities e.g. people, objects, organization etc. in textual medical documents. This clinical support model is one of many to be released, and is a crucial aspect of clinical support systems.
Intended uses & limitations
The model is meant to be used for research and development purposes by Data Scientists, ML & Software Engineers for the development of NER applications capable of identifying enitities in medical EHR systems to augment patient health processing.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.1708 | 1.0 | 834 | 0.2817 | 0.8212 |
| 0.1305 | 2.0 | 1668 | 0.2822 | 0.8354 |
| 0.07 | 3.0 | 2502 | 0.3338 | 0.8406 |
Loading the model
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline('token-classification', model="DeepNeural/ner_classifier_v2")
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained('DeepNeural/ner_classifier_v2')
model = AutoModelForTokenClassification.from_pretrained('DeepNeural/ner_classifier_v2')
Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1