Instructions to use YakovElm/Qt_5_BERT_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Qt_5_BERT_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Qt_5_BERT_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Qt_5_BERT_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Qt_5_BERT_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Qt_5_BERT_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Qt_5_BERT_Over_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Qt_5_BERT_Over_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Qt_5_BERT_Over_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 7a0ced3b295344d54384cee66dda8e57dad61c3348de7ed98f3ccae111a88966
- Size of remote file:
- 438 MB
- SHA256:
- 88f405e14d248f1852d6fbbe92c91cfc1d17f4d7052d5483c0cd0e145780c161
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