Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use elopezlopez/distilbert-base-uncased_fold_7_binary_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elopezlopez/distilbert-base-uncased_fold_7_binary_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elopezlopez/distilbert-base-uncased_fold_7_binary_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elopezlopez/distilbert-base-uncased_fold_7_binary_v1") model = AutoModelForSequenceClassification.from_pretrained("elopezlopez/distilbert-base-uncased_fold_7_binary_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from elopezlopez/distilbert-base-uncased_fold_7_binary_v1: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/elopezlopez/distilbert-base-uncased_fold_7_binary_v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://elopezlopez/distilbert-base-uncased_fold_7_binary_v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/elopezlopez/distilbert-base-uncased_fold_7_binary_v1/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- a5b5b174c86ccba910fd2e19dfbef9856bcea1cd322871d05e12c5559f0c77d0
- Size of remote file:
- 268 MB
- SHA256:
- 1386eb041ac089640be2f0d78dcb19b18b90409e0fd27371ff43b0c522f1474a
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