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 pytorch_model.bin from mathildeparlo/ben_base_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/mathildeparlo/ben_base_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mathildeparlo/ben_base_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mathildeparlo/ben_base_model/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- e8a907e857c5673f4e0ea88bd5d033653b77b845d07b5e7ffeb7ec053bdf6e32
- Size of remote file:
- 268 MB
- SHA256:
- 768754e87c874ea1e413ee0d3208c03333db53a63822c4282b8af461706f03fe
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