Instructions to use YakovElm/IntelDAOS_5_BERT_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS_5_BERT_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS_5_BERT_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS_5_BERT_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS_5_BERT_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS_5_BERT_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS_5_BERT_Over_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS_5_BERT_Over_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS_5_BERT_Over_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- ae9d8d8ac053d29c6b7b4c608892105656301187a065960253c0293ff2f407e7
- Size of remote file:
- 438 MB
- SHA256:
- c6c069b927d72ba3c1282ec96681256db896cb68374c7434c2770a642814adfa
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