Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use dhananjay2912/deberta_aci_bench_medical_section_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dhananjay2912/deberta_aci_bench_medical_section_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dhananjay2912/deberta_aci_bench_medical_section_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dhananjay2912/deberta_aci_bench_medical_section_classifier") model = AutoModelForSequenceClassification.from_pretrained("dhananjay2912/deberta_aci_bench_medical_section_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dhananjay2912/deberta_aci_bench_medical_section_classifier: direct link, hf CLI and curl.
- Browser
- Download file 524 Bytes
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https://huggingface.co/dhananjay2912/deberta_aci_bench_medical_section_classifier/resolve/main/README.md
- Command line
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hf download hf://dhananjay2912/deberta_aci_bench_medical_section_classifier/README.md
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curl -L -o README.md https://huggingface.co/dhananjay2912/deberta_aci_bench_medical_section_classifier/resolve/main/README.md
524 Bytes
metadata
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
datasets:
- autotrain-9c20u-twasm/autotrain-data
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.4913766384124756
f1_macro: 0.28164367547346275
f1_micro: 0.64
f1_weighted: 0.5917376665887304
precision_macro: 0.2705775014459225
precision_micro: 0.64
precision_weighted: 0.5802396761133604
recall_macro: 0.3324350649350649
recall_micro: 0.64
recall_weighted: 0.64
accuracy: 0.64