<h1>Model description</h1>

This is a fine-tuned BioBERT model for extracting reported outcomes (i.e. those for which results are presented) from articles reporting clinical trials.
This is the second version of the model; the original model development was reported in:

Anna Koroleva, Sanjay Kamath, Patrick Paroubek. Extracting primary and reported outcomes from articles reporting randomized controlled trials using pre-trained deep language representations. Preprint: https://easychair.org/publications/preprint/qpml

The original work was conducted within the scope of the Assisted authoring for avoiding inadequate claims in scientific reporting PhD project of the Methods for Research on Research (MiRoR, http://miror-ejd.eu/) program.

Model creator: Anna Koroleva


<h1>Intended uses & limitations</h1>

The model is intended to be used for extracting reported outcomes from texts of clinical trials.

The main limitation is that the model was trained on a fairly small sample of data annotated by a single annotator. Annotating more data or involvig more annotators was not possiblw within the PhD project.


<h1>How to use</h1>

The model should be used with the BioBERT tokeniser. A sample code for getting model predictions is below:
```
  import numpy as np
  from transformers import AutoTokenizer
  from transformers import AutoModelForTokenClassification

  tokenizer = AutoTokenizer.from_pretrained('dmis-lab/biobert-v1.1')
  model = AutoModelForTokenClassification.from_pretrained(r'aakorolyova/reported_outcome_extraction')
  text = """Compared with placebo plus chemotherapy, pembrolizumab plus chemotherapy improved overall survival in patients with previously untreated, advanced oesophageal squamous cell carcinoma and PD-L1 CPS of 10 or more, and overall survival and progression-free survival in patients with oesophageal squamous cell carcinoma, PD-L1 CPS of 10 or more, and in all randomised patients regardless of histology, and had a manageable safety profile in the total as-treated population."""
  encoded_input = tokenizer(text, padding=True, truncation=True, max_length=2000, return_tensors='pt')
  output = model(**encoded_input)['logits']
  output = np.argmax(output.detach().numpy(), axis=2)
  print(output)
```

Some more useful functions can be found in or Github repository: https://github.com/aakorolyova/DeSpin-2.0


<h1>Training data</h1>

Training data can be found in https://github.com/aakorolyova/DeSpin-2.0/tree/main/data/Reported_Outcomes

<h1>Training procedure</h1>
The model was fine-tuned using Huggingface Trainer API. Training scripts can be found in https://github.com/aakorolyova/DeSpin-2.0

<h1>Evaluation</h1>
Precision: 65.57%

Recall: 74.77%

F1: 69.87%
{}
transformers pytorch bert token-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 

<h1>Model description</h1>

This is a fine-tuned BioBERT model for extracting reported outcomes (i.e. those for which results are presented) from articles reporting clinical trials.
This is the second version of the model; the original model development was reported in:

Anna Koroleva, Sanjay Kamath, Patrick Paroubek. Extracting primary and reported outcomes from articles reporting randomized controlled trials using pre-trained deep language representations. Preprint: URL

The original work was conducted within the scope of the Assisted authoring for avoiding inadequate claims in scientific reporting PhD project of the Methods for Research on Research (MiRoR, URL program.

Model creator: Anna Koroleva


<h1>Intended uses & limitations</h1>

The model is intended to be used for extracting reported outcomes from texts of clinical trials.

The main limitation is that the model was trained on a fairly small sample of data annotated by a single annotator. Annotating more data or involvig more annotators was not possiblw within the PhD project.


<h1>How to use</h1>

The model should be used with the BioBERT tokeniser. A sample code for getting model predictions is below:


Some more useful functions can be found in or Github repository: URL


<h1>Training data</h1>

Training data can be found in URL

<h1>Training procedure</h1>
The model was fine-tuned using Huggingface Trainer API. Training scripts can be found in URL

<h1>Evaluation</h1>
Precision: 65.57%

Recall: 74.77%

F1: 69.87%
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 
