Instructions to use tk3879110/bert_finetuning_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tk3879110/bert_finetuning_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tk3879110/bert_finetuning_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tk3879110/bert_finetuning_test") model = AutoModelForSequenceClassification.from_pretrained("tk3879110/bert_finetuning_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from tk3879110/bert_finetuning_test: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tk3879110/bert_finetuning_test/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://tk3879110/bert_finetuning_test/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/tk3879110/bert_finetuning_test/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- bb5fd72d64d89a613374ec0b591f14e1d1e4ad3fe19110f182be3c2528737c23
- Size of remote file:
- 438 MB
- SHA256:
- 0612b685da7e9b61bb2d814dd6d5b38c4fa7f9974abe4a58787fc543a18707ec
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