Instructions to use HooshvareLab/bert-fa-base-uncased-ner-peyma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-base-uncased-ner-peyma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HooshvareLab/bert-fa-base-uncased-ner-peyma")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-ner-peyma") model = AutoModelForTokenClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-ner-peyma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from HooshvareLab/bert-fa-base-uncased-ner-peyma: direct link, hf CLI and curl.
- Browser
- Download file 1.58 kB
-
https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-peyma/resolve/main/training_args.bin
- Command line
-
hf download hf://HooshvareLab/bert-fa-base-uncased-ner-peyma/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-peyma/resolve/main/training_args.bin
1.58 kB
- Xet hash:
- 7b64767e1f0982eb5d5d0b207e07fff62b093ba0bd28accf974b7e52370098d9
- Size of remote file:
- 1.58 kB
- SHA256:
- ee84c88920b196f08fce171b9908dce5ed72bbca9cfdfbd4bca0ffad054072b2
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