Text Classification
Transformers
Safetensors
German
bert
science
ner
def_extraction
definitions
Eval Results (legacy)
text-embeddings-inference
Instructions to use samirmsallem/gbert-base-definition_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samirmsallem/gbert-base-definition_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="samirmsallem/gbert-base-definition_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("samirmsallem/gbert-base-definition_classification") model = AutoModelForSequenceClassification.from_pretrained("samirmsallem/gbert-base-definition_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from samirmsallem/gbert-base-definition_classification: direct link, hf CLI and curl.
- Browser
- Download file 729 kB
-
https://huggingface.co/samirmsallem/gbert-base-definition_classification/resolve/main/tokenizer.json
- Command line
-
hf download hf://samirmsallem/gbert-base-definition_classification/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/samirmsallem/gbert-base-definition_classification/resolve/main/tokenizer.json
729 kB
File too large to display, you can check the raw version instead.