Instructions to use YakovElm/Hyperledger_10_BERT_Under_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Hyperledger_10_BERT_Under_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Hyperledger_10_BERT_Under_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Hyperledger_10_BERT_Under_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Hyperledger_10_BERT_Under_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Hyperledger_10_BERT_Under_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Hyperledger_10_BERT_Under_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Hyperledger_10_BERT_Under_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Hyperledger_10_BERT_Under_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- e0307d3733b5536dd1aa5012352f52256561b4cb9edc0958c33efee7df276226
- Size of remote file:
- 438 MB
- SHA256:
- de0d724cd3b136da16b6101bdfd4a0230576ac297e2f34e635223cc5c353948e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.