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 training_args.bin from petermutwiri/distilbert-base-uncased-Distilbert-Model: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/petermutwiri/distilbert-base-uncased-Distilbert-Model/resolve/main/training_args.bin
- Command line
-
hf download hf://petermutwiri/distilbert-base-uncased-Distilbert-Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/petermutwiri/distilbert-base-uncased-Distilbert-Model/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 52a54bc3419c891351ac91bdb8576cff03209accafbd99959f074bb67abd7666
- Size of remote file:
- 4.09 kB
- SHA256:
- 4e3deb7fc4b54b519872803cf11ae271af2db2eef007bce7126d1e1077b23a38
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.