Text Classification
Transformers
PyTorch
TensorBoard
English
mobilebert
Generated from Trainer
Eval Results (legacy)
Instructions to use Alireza1044/mobilebert_QNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alireza1044/mobilebert_QNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Alireza1044/mobilebert_QNLI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Alireza1044/mobilebert_QNLI") model = AutoModelForSequenceClassification.from_pretrained("Alireza1044/mobilebert_QNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Alireza1044/mobilebert_QNLI: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/Alireza1044/mobilebert_QNLI/resolve/main/training_args.bin
- Command line
-
hf download hf://Alireza1044/mobilebert_QNLI/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Alireza1044/mobilebert_QNLI/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- f4ffedd1befdb37f047187cb48bf007a3b8bcd937c4a278c18fc59e800340325
- Size of remote file:
- 3.25 kB
- SHA256:
- 130b3c58bb4bc01a11e445af3a929dfd62831e7c35cffa9ceb6d76f165e483a7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.