Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/distilbert-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/distilbert-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/distilbert-classification-10ksamples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/distilbert-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/distilbert-classification-10ksamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jayavibhav/distilbert-classification-10ksamples: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/jayavibhav/distilbert-classification-10ksamples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jayavibhav/distilbert-classification-10ksamples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jayavibhav/distilbert-classification-10ksamples/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 09ac4fe519b4b8c1a454f253fe1bb04840a51ebad9b93c117fa8217c002950df
- Size of remote file:
- 268 MB
- SHA256:
- e56f03ef59512d7ba629c3b909e14e86c6e44dcd68588bdfd5d950e3cb2cb8d2
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