Instructions to use YakovElm/Hyperledger_20_BERT_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Hyperledger_20_BERT_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Hyperledger_20_BERT_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Hyperledger_20_BERT_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Hyperledger_20_BERT_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Hyperledger_20_BERT_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Hyperledger_20_BERT_Over_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Hyperledger_20_BERT_Over_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Hyperledger_20_BERT_Over_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- fd932aa3a01529c103ddcdb740d084c5708186af04c9e66f52e9c3f3327cb5d0
- Size of remote file:
- 438 MB
- SHA256:
- 44e898fc62705ee9f5f0d4aae3ae3cd5c294b6c9a97452f933d2c84ba2f05b02
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