Instructions to use YakovElm/IntelDAOS_10_BERT_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS_10_BERT_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS_10_BERT_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS_10_BERT_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS_10_BERT_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS_10_BERT_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS_10_BERT_Over_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS_10_BERT_Over_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS_10_BERT_Over_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- a95f13d6bf9858c18d5b6e6f92f8dbe9ca753d36b3a271c1d8bd338cc3fc8935
- Size of remote file:
- 438 MB
- SHA256:
- bb26531de9d6895cd8f3b082b805ef29f955fd021b848fb53647f4b29d13399a
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