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")# 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
- Xet hash:
- c80f49cba1cb537c3fd833cf5e2078c9e1b32d240e1dfa7a30fbf9fb0545dbd0
- Size of remote file:
- 5.78 kB
- SHA256:
- ca6a57bb3665602515473f6c3e6aa96cb5505d7b8642beb6c8604c4a00aec451
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