Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Accelera/news_analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Accelera/news_analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Accelera/news_analyzer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Accelera/news_analyzer") model = AutoModelForSequenceClassification.from_pretrained("Accelera/news_analyzer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Accelera/news_analyzer: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/Accelera/news_analyzer/resolve/main/training_args.bin
- Command line
-
hf download hf://Accelera/news_analyzer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Accelera/news_analyzer/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 87a22c45ff19915a0b8526ef6a8eeecc01f98109a4a8a60831b618c8d4452a0e
- Size of remote file:
- 4.6 kB
- SHA256:
- d885c89a19b7cbf2221be1ad55a0eeaf3b1953474cebaa6b24ee1654c0c73437
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.