Text Classification
Transformers
TensorBoard
Safetensors
French
English
deberta-v2
Generated from Trainer
name
person
company
text-embeddings-inference
Instructions to use ele-sage/mdeberta-v3-base-name-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ele-sage/mdeberta-v3-base-name-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ele-sage/mdeberta-v3-base-name-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ele-sage/mdeberta-v3-base-name-classifier") model = AutoModelForSequenceClassification.from_pretrained("ele-sage/mdeberta-v3-base-name-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ele-sage/mdeberta-v3-base-name-classifier: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/ele-sage/mdeberta-v3-base-name-classifier/resolve/main/tokenizer.json
- Command line
-
hf download hf://ele-sage/mdeberta-v3-base-name-classifier/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ele-sage/mdeberta-v3-base-name-classifier/resolve/main/tokenizer.json
16.4 MB
- Xet hash:
- 91a848b836cde9c15dc5bf24d77c83a0200c7839aff08479594e06149f6ffbdc
- Size of remote file:
- 16.4 MB
- SHA256:
- 9ddbe35b768b22d6cd0d61a0a88ea3a188b7c00bbb7b825f9f247cf0b79c2365
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