Instructions to use pourmand1376/NER_Farsi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pourmand1376/NER_Farsi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pourmand1376/NER_Farsi")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pourmand1376/NER_Farsi") model = AutoModelForTokenClassification.from_pretrained("pourmand1376/NER_Farsi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pourmand1376/NER_Farsi: direct link, hf CLI and curl.
- Browser
- Download file 471 MB
-
https://huggingface.co/pourmand1376/NER_Farsi/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pourmand1376/NER_Farsi/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pourmand1376/NER_Farsi/resolve/main/pytorch_model.bin
471 MB
- Xet hash:
- 67607b4e45e49b8d788966ec9e7208b314dbe072ef900f4a793a9d9d7c3f7436
- Size of remote file:
- 471 MB
- SHA256:
- 8ed171936b80f1be8dcb7d2620e63001d43d0d32d67fcb89948f08a5dedd8ed6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.