Instructions to use Martinlabla/bert_finetuning_test_mine_result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Martinlabla/bert_finetuning_test_mine_result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Martinlabla/bert_finetuning_test_mine_result")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Martinlabla/bert_finetuning_test_mine_result") model = AutoModelForMaskedLM.from_pretrained("Martinlabla/bert_finetuning_test_mine_result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Martinlabla/bert_finetuning_test_mine_result: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Martinlabla/bert_finetuning_test_mine_result/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Martinlabla/bert_finetuning_test_mine_result/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Martinlabla/bert_finetuning_test_mine_result/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 191b007e8352bd2d993d3329c593617048cced2466b7e1e5f7f5e24e583c2a23
- Size of remote file:
- 438 MB
- SHA256:
- 4c6d20ddbc531920b9eb774d274e2635b08364f9ba68e239830873028c60034d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.