Instructions to use YakovElm/IntelDAOS_5_BERT_Under_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS_5_BERT_Under_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS_5_BERT_Under_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS_5_BERT_Under_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS_5_BERT_Under_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS_5_BERT_Under_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS_5_BERT_Under_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS_5_BERT_Under_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS_5_BERT_Under_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- bad3b298fe2112f3779d78e17aaf37584e2cb2ca039c0d0de4c51bd6938e8836
- Size of remote file:
- 438 MB
- SHA256:
- 5c4c1c9d302d82ed51a75f8c7ed938fb51990b20b19bc0e32b552663c35e793f
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