Instructions to use yseop/SMM4H2024_Task1_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yseop/SMM4H2024_Task1_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="yseop/SMM4H2024_Task1_roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("yseop/SMM4H2024_Task1_roberta") model = AutoModelForTokenClassification.from_pretrained("yseop/SMM4H2024_Task1_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 536 Bytes
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license: afl-3.0
language:
- en
library_name: transformers
pipeline_tag: token-classification
---
# SMM4H-2024 Task 1: Adverse Drug Events Detection
## Overview
This is a NER model created by fine-tuning [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on [SMM4H 2024 Task 1](https://healthlanguageprocessing.org/smm4h-2024/) corpus.
## Results
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|---|---:|
|F1-Norm|40|
|P-Norm|39.6|
|R-Norm|40.4|
|F1-NER|47.2|
|P-NER|47|
|R-NER|47.5|
|F1-Norm-Unseen|29.5|
|P-Norm-Unseen|23.2|
|R-Norm-Unseen|40.6| |