Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use SG1123/boolq_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SG1123/boolq_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SG1123/boolq_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SG1123/boolq_model") model = AutoModelForSequenceClassification.from_pretrained("SG1123/boolq_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SG1123/boolq_model: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/SG1123/boolq_model/resolve/main/training_args.bin
- Command line
-
hf download hf://SG1123/boolq_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SG1123/boolq_model/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 1905c7ae3c9dcc35d45cfc9c8930ee8dc2c018e70d701aec96fa1148e2369d75
- Size of remote file:
- 4.54 kB
- SHA256:
- c2d5be7f01d060f4d4f77e106028fffd14c427e32800c6e33d704e10bcb7f901
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.