Instructions to use YakovElm/IntelDAOS_20_BERT_Under_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS_20_BERT_Under_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS_20_BERT_Under_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS_20_BERT_Under_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS_20_BERT_Under_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS_20_BERT_Under_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS_20_BERT_Under_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS_20_BERT_Under_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS_20_BERT_Under_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 5a00dbf0f496b4932b555ed94e2f06798433a290462e2111130d3ec76d349e40
- Size of remote file:
- 438 MB
- SHA256:
- cea2e6db93973ee733059edffcbb4fb548e6fa667a175e15b09d711fdd52c53d
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