Text Classification
Transformers
Safetensors
PyTorch
Persian
bert
sentiment-analysis
parsbert
persian
text-embeddings-inference
Instructions to use MTE313/NEXARA_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MTE313/NEXARA_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MTE313/NEXARA_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MTE313/NEXARA_Model") model = AutoModelForSequenceClassification.from_pretrained("MTE313/NEXARA_Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a8adcde13af2130f1fff849e971ecd700e329b7855d5eaf4cbe6a56ab49c69d
- Size of remote file:
- 5.2 kB
- SHA256:
- 7f3c6dce657c880eb5e2c473c9716b8f84c71147efa50afcac461fd3e1b87773
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.