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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: ner_classifier_v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ner_classifier_v2 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3338 |
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- F1: 0.8406 |
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## Model description |
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The DeepNeural NER-II model is designed to identify multiple enitities e.g. people, objects, organization etc. in textual medical documents. |
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This clinical support model is one of many to be released, and is a crucial aspect of clinical support systems. |
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## Intended uses & limitations |
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The model is meant to be used for research and development purposes by Data Scientists, ML & Software Engineers for the development of |
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NER applications capable of identifying enitities in medical EHR systems to augment patient health processing. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.1708 | 1.0 | 834 | 0.2817 | 0.8212 | |
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| 0.1305 | 2.0 | 1668 | 0.2822 | 0.8354 | |
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| 0.07 | 3.0 | 2502 | 0.3338 | 0.8406 | |
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### Loading the model |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline('token-classification', model="DeepNeural/ner_classifier_v2") |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained('DeepNeural/ner_classifier_v2') |
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model = AutoModelForTokenClassification.from_pretrained('DeepNeural/ner_classifier_v2') |
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``` |
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### Making predictions |
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1. Preparing the model |
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```python |
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#Creating an easy tags function |
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#Custom configured model needs improvement, let's train it |
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def tag_text(text, tags, model, tokenizer) -> pd.DataFrame: |
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#Get tokens with special characters |
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tokens = tokenizer(text).tokens() |
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#Encode the sequence into IDs |
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input_ids = tokenizer(text, return_tensors="pt").input_ids.to(device) |
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#Get predictions as a distribution over 7 classes |
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outputs = model(input_ids)[0] |
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#Take argmax to get most likely class per token |
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predictions = torch.argmax(outputs, dim=2) |
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#Convert to DataFrame |
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preds = [ner_tags.names[p] for p in predictions[0].cpu().numpy()] |
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return pd.DataFrame([tokens, preds], index=["Tokens", "Tags"]) |
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``` |
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2. Example for making predictions |
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```python |
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#Testing the model |
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dummy_text = "DeepNeural is an organization seeking to revolutionize healthcare" |
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tag_text(dummy_text, ner_tags, trainer.model, tokenizer) |
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``` |
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### Framework versions |
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- Transformers 4.56.2 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.1 |
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