Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use yuqinA/model_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yuqinA/model_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yuqinA/model_trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yuqinA/model_trained") model = AutoModelForSequenceClassification.from_pretrained("yuqinA/model_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from yuqinA/model_trained: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/yuqinA/model_trained/resolve/main/training_args.bin
- Command line
-
hf download hf://yuqinA/model_trained/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/yuqinA/model_trained/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 68a848d724b40073212e59a1a1f6462d6d914f1bd54a7bbbd2ac4b9313183e3e
- Size of remote file:
- 5.3 kB
- SHA256:
- 435c75f640be6f0040dc2e52d1c256b41eb490f6690896a4d59a3b32c2f1104d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.