Instructions to use YakovElm/Hyperledger_15_BERT_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Hyperledger_15_BERT_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Hyperledger_15_BERT_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Hyperledger_15_BERT_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Hyperledger_15_BERT_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Hyperledger_15_BERT_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Hyperledger_15_BERT_Over_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Hyperledger_15_BERT_Over_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Hyperledger_15_BERT_Over_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 183eb884763d1f68bfc6f760e465848bd3ec64106d3c9c4c6e738770528fc991
- Size of remote file:
- 438 MB
- SHA256:
- 4bda24ab23bcb0d7f6dcbb425c0a0919e7966da0dc37061dc607e85a7a5fa97d
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