Instructions to use YakovElm/Hyperledger_5_BERT_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Hyperledger_5_BERT_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Hyperledger_5_BERT_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Hyperledger_5_BERT_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Hyperledger_5_BERT_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Hyperledger_5_BERT_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Hyperledger_5_BERT_Over_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Hyperledger_5_BERT_Over_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Hyperledger_5_BERT_Over_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 8eff816fa9e2a5bfb4e5b4f20ee337f9b0ef4ff9552be1fef366d68d15108105
- Size of remote file:
- 438 MB
- SHA256:
- 0b05e1616c683e4780dc0c76c003712aa4a5a1338d1607ce43005b941b432263
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.