Instructions to use YakovElm/Apache_10_BERT_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Apache_10_BERT_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Apache_10_BERT_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Apache_10_BERT_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Apache_10_BERT_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/Apache_10_BERT_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Apache_10_BERT_Over_Sampling/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/Apache_10_BERT_Over_Sampling/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/Apache_10_BERT_Over_Sampling/resolve/main/tf_model.h5
438 MB
- Xet hash:
- ac62f9b93344ba978dac761f3f09f24ea95ccfd71f1cc846623e5c661328a9af
- Size of remote file:
- 438 MB
- SHA256:
- 95e398889d21b5d7e06b88b3b4d551e0ad681b7a05e50aa4f8e168ef82549c83
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