Text Classification
Transformers
ONNX
Safetensors
gpt2
healthcare
column-normalization
distilgpt2
Eval Results (legacy)
Instructions to use tsilva/clinical-field-mapper-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsilva/clinical-field-mapper-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tsilva/clinical-field-mapper-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tsilva/clinical-field-mapper-classification") model = AutoModelForSequenceClassification.from_pretrained("tsilva/clinical-field-mapper-classification") - Notebooks
- Google Colab
- Kaggle
Add ONNX variant of tsilva/clinical-field-mapper-classification
Browse files- onnx/model.onnx +1 -1
onnx/model.onnx
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