Text Classification
Transformers
Safetensors
English
bert
pharmacovigilance
drug-safety
adverse-drug-reactions
clinical-nlp
biobert
drug-causality
ade-corpus
medical-nlp
text-embeddings-inference
Instructions to use PrashantRGore/drug-causality-bert-v2-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PrashantRGore/drug-causality-bert-v2-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PrashantRGore/drug-causality-bert-v2-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PrashantRGore/drug-causality-bert-v2-model") model = AutoModelForSequenceClassification.from_pretrained("PrashantRGore/drug-causality-bert-v2-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from PrashantRGore/drug-causality-bert-v2-model: direct link, hf CLI and curl.
- Browser
- Download file 679 kB
-
https://huggingface.co/PrashantRGore/drug-causality-bert-v2-model/resolve/main/tokenizer.json
- Command line
-
hf download hf://PrashantRGore/drug-causality-bert-v2-model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/PrashantRGore/drug-causality-bert-v2-model/resolve/main/tokenizer.json
679 kB
File too large to display, you can check the raw version instead.