Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use hyun402/bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hyun402/bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hyun402/bert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hyun402/bert_model") model = AutoModelForSequenceClassification.from_pretrained("hyun402/bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hyun402/bert_model: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/hyun402/bert_model/resolve/main/training_args.bin
- Command line
-
hf download hf://hyun402/bert_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hyun402/bert_model/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 969e3870c58dfaf560d7cac460cc7d23d4ff871e57b31c8b4e3d4e46d0776159
- Size of remote file:
- 5.3 kB
- SHA256:
- 1ff8ee3d134d28eb7b481823ed8d3095cf446ba816d1bca105e82c20f83f9cd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.