Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use petermutwiri/distilbert-base-uncased-Distilbert-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petermutwiri/distilbert-base-uncased-Distilbert-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="petermutwiri/distilbert-base-uncased-Distilbert-Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("petermutwiri/distilbert-base-uncased-Distilbert-Model") model = AutoModelForSequenceClassification.from_pretrained("petermutwiri/distilbert-base-uncased-Distilbert-Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from petermutwiri/distilbert-base-uncased-Distilbert-Model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/petermutwiri/distilbert-base-uncased-Distilbert-Model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://petermutwiri/distilbert-base-uncased-Distilbert-Model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/petermutwiri/distilbert-base-uncased-Distilbert-Model/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 6ef9d388a1ec84ad5e7d55166e15b936a72b717f197ad846aee2bca6500733d8
- Size of remote file:
- 268 MB
- SHA256:
- f7004710ae6b298de212dd261f741700d58bcce0ee77b4b471c3ef0ca353e2fb
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