Instructions to use ania3000/fatjbert-morph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ania3000/fatjbert-morph with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ania3000/fatjbert-morph")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ania3000/fatjbert-morph") model = AutoModelForTokenClassification.from_pretrained("ania3000/fatjbert-morph", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ania3000/fatjbert-morph: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/ania3000/fatjbert-morph/resolve/main/training_args.bin
- Command line
-
hf download hf://ania3000/fatjbert-morph/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ania3000/fatjbert-morph/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- 884b8dd5fc25b4cbaceb64b35ad783afdfa2abd6e54ba3ac64b2bb54690d9d5b
- Size of remote file:
- 5.84 kB
- SHA256:
- 3fd24d3aabc0245e2d52ec29223382b13364324c2b92d05d62f28b9d8129dc8c
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