Instructions to use Simih/distilbert-base-uncased-finetuned-fin_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simih/distilbert-base-uncased-finetuned-fin_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Simih/distilbert-base-uncased-finetuned-fin_ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Simih/distilbert-base-uncased-finetuned-fin_ner") model = AutoModelForTokenClassification.from_pretrained("Simih/distilbert-base-uncased-finetuned-fin_ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2249b8b112653d53bef2037ae4412085fc4cdf023269507fc53efa83ac4ddd87
- Size of remote file:
- 5.37 kB
- SHA256:
- 233bfd7fc768378e6dae6b1088d0a2b2cfa491dfa9fe95b02e700e425c8d739e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.