Instructions to use leslie/bert_finetuning_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leslie/bert_finetuning_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leslie/bert_finetuning_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leslie/bert_finetuning_test") model = AutoModelForSequenceClassification.from_pretrained("leslie/bert_finetuning_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from leslie/bert_finetuning_test: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/leslie/bert_finetuning_test/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://leslie/bert_finetuning_test/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/leslie/bert_finetuning_test/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- af3505f1b10fa512566303f945874d3b6db0b770a62447dad25e850f8fc1841f
- Size of remote file:
- 438 MB
- SHA256:
- 136647a32808ea7e14db7f9b2eb9e75fadfd0a9ba929c4d11eeb4c7320f0d384
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