Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use mathildeparlo/ben_base_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mathildeparlo/ben_base_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mathildeparlo/ben_base_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mathildeparlo/ben_base_model") model = AutoModelForSequenceClassification.from_pretrained("mathildeparlo/ben_base_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mathildeparlo/ben_base_model: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/mathildeparlo/ben_base_model/resolve/main/training_args.bin
- Command line
-
hf download hf://mathildeparlo/ben_base_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mathildeparlo/ben_base_model/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- 1f68110faea1426ec5181f4889fadcde463fdc78421b994203c53a5ff95949bc
- Size of remote file:
- 4.47 kB
- SHA256:
- 7ab231ecb8a819f9139dbbec2c1076ad5669ca6c34d73d8c30e8a70222bfc620
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