Model Card for semaj83/scibert_finetuned_ctmatch

This model can be used for classifying "<topic> [SEP] <clinical trial document>" pairs into 3 classes, 0, 1, 2, or not relevant, partially relevant, and relevant.

Model Details

Fine-tuned from 'allenai/scibert_scivocab_uncased' on triples of labelled topic, documents, relevance labels. These triples were processed using ctproc, collated from the openly available TREC22 Precision Medicine and CSIRO datasets here: https://huggingface.co/datasets/semaj83/ctmatch_classification

Model Description

Transformer model with linear sequence classification head, trained with cross-entropy on ~30k triples and evaluated using f1.

Developed by: James Kelly
Model type: SequenceClassification
Language(s) (NLP): English
License: MIT
Finetuned from model: allenai/scibert_scivocab_uncased

Model Sources

Repository: https://github.com/semajyllek/ctmatch
Paper [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use

ctmatch IR pipeline for matching large set of clinical trials documents to text topic.

Bias, Risks, and Limitations

Please see dataset sources for information on patient descriptions (topics), constructed by medical professionals for these datasets. No personal health information about real individuals is contained in the related dataset. Links in dataset location on hub.

The claissifier model performs much better on deciding if a pair is 0 - not relevant, than differentiating between 1, partially relevant, and 2, relevant, though this is still an important clinical task.

How to Get Started with the Model

Use the code below to get started with the model.


from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("semaj83/scibert_finetuned_ctmatch")

model = AutoModelForSequenceClassification.from_pretrained("semaj83/scibert_finetuned_ctmatch")

Training Details

see notebook in ctmatch repo.

Training Data

https://huggingface.co/datasets/semaj83/ctmatch

Preprocessing

If using ctmatch labelled dataset, using the tokenizer alone is sufficient. If using raw topic and/or clinical trial documents, you may need to use ctproc or another method to extract relevant fields and preprocess text.

Training Hyperparameters

max_sequence_length=512 batch_size=8 padding='max_length' truncation=True learning_rate=2e-5 train_epochs=5 weight_decay=0.01 warmup_steps=500 seed=42 splits={"train":0.8, "val":0.1} use_trainer=True fp16=True early_stopping=True

Evaluation

sklearn classifier table on random test split:


                precision    recall  f1-score   support

           0       0.88      0.93      0.90      5430
           1       0.56      0.56      0.56      1331
           2       0.65      0.49      0.56      1178

    accuracy                           0.80      7939
   macro avg       0.70      0.66      0.67      7939
weighted avg       0.79      0.80      0.79      7939

Model Card Authors

James Kelly

Model Card Contact

[email protected]

semaj83
/

scibert_finetuned_ctmatch