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 pytorch_model.bin 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/pytorch_model.bin
- Command line
-
hf download hf://leslie/bert_finetuning_test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/leslie/bert_finetuning_test/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 67f51fc65f8c0f7071549a3fa034ce2563c472dc0d5416e34d0a738f78dacd11
- Size of remote file:
- 438 MB
- SHA256:
- e1ca9a76d5cdc2b456a27e476fcf0bd227f80695d1a1dc0bee3e6cc6695ddb8a
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