Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use elopezlopez/distilbert-base-uncased_fold_2_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_2_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_2_binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elopezlopez/distilbert-base-uncased_fold_2_binary") model = AutoModelForSequenceClassification.from_pretrained("elopezlopez/distilbert-base-uncased_fold_2_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from elopezlopez/distilbert-base-uncased_fold_2_binary: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/elopezlopez/distilbert-base-uncased_fold_2_binary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://elopezlopez/distilbert-base-uncased_fold_2_binary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/elopezlopez/distilbert-base-uncased_fold_2_binary/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 1a1588ecff72f5a3c9601665423c251ff753b135143713aebf652c92b19ba140
- Size of remote file:
- 268 MB
- SHA256:
- 59e16abae7de6cf27a9f1ed4a2e45b8cb1f5719471d29f2c7c4e7e39b03b84f5
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