Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use KienLe21/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KienLe21/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KienLe21/my_awesome_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KienLe21/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("KienLe21/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KienLe21/my_awesome_model: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/KienLe21/my_awesome_model/resolve/main/training_args.bin
- Command line
-
hf download hf://KienLe21/my_awesome_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KienLe21/my_awesome_model/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 3c2e513a89557dca9f5366ea09eaf2d755624e5f89763722dad945c0f7bbf83f
- Size of remote file:
- 5.37 kB
- SHA256:
- c735ca7989c8ec8eb4192eb344a7b47b62b64cdf69ea99cf0a729f1f57a101b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.