Instructions to use devansvd/bert-model-test-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devansvd/bert-model-test-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devansvd/bert-model-test-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devansvd/bert-model-test-2") model = AutoModelForTokenClassification.from_pretrained("devansvd/bert-model-test-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from devansvd/bert-model-test-2: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/devansvd/bert-model-test-2/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://devansvd/bert-model-test-2@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/devansvd/bert-model-test-2/resolve/refs%2Fpr%2F1/training_args.bin
1.78 kB
- Xet hash:
- 64e83e22f903fcd198aab7725352ff9de483720eb6cb8e8e602af5c139cc58f5
- Size of remote file:
- 1.78 kB
- SHA256:
- 707cd7aba270ab82e43c51d8ce84fa2ed841eda971fa15d1a0a791db736f367e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.