Instructions to use YakovElm/Hyperledger_15_BERT_Under_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Hyperledger_15_BERT_Under_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Hyperledger_15_BERT_Under_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Hyperledger_15_BERT_Under_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Hyperledger_15_BERT_Under_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Hyperledger_15_BERT_Under_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Hyperledger_15_BERT_Under_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Hyperledger_15_BERT_Under_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Hyperledger_15_BERT_Under_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 683651c34666604d84373fbfa767a2ff06198597f69898b2546f08d6d890e980
- Size of remote file:
- 438 MB
- SHA256:
- 953a4b36d345bbc9a870d85e5107f417301e1567c749bb5d0963fe21ddfdc9fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.