Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use elopezlopez/distilbert-base-uncased_fold_5_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elopezlopez/distilbert-base-uncased_fold_5_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elopezlopez/distilbert-base-uncased_fold_5_binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elopezlopez/distilbert-base-uncased_fold_5_binary") model = AutoModelForSequenceClassification.from_pretrained("elopezlopez/distilbert-base-uncased_fold_5_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from elopezlopez/distilbert-base-uncased_fold_5_binary: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/elopezlopez/distilbert-base-uncased_fold_5_binary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://elopezlopez/distilbert-base-uncased_fold_5_binary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/elopezlopez/distilbert-base-uncased_fold_5_binary/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 319f0f38a63aec89fafb1283c101dd6351099cd0562ebe995797e9d9b4841385
- Size of remote file:
- 268 MB
- SHA256:
- 25942b398c4e0f04bfd1fcbf2ddd3f54d4ad5bef20bc8b73b17548048d49b8a2
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