Instructions to use Centic-AI/sentiment_analysis_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Centic-AI/sentiment_analysis_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Centic-AI/sentiment_analysis_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Centic-AI/sentiment_analysis_model") model = AutoModelForSequenceClassification.from_pretrained("Centic-AI/sentiment_analysis_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Centic-AI/sentiment_analysis_model: direct link, hf CLI and curl.
- Browser
- Download file 540 MB
-
https://huggingface.co/Centic-AI/sentiment_analysis_model/resolve/main/tf_model.h5
- Command line
-
hf download hf://Centic-AI/sentiment_analysis_model/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Centic-AI/sentiment_analysis_model/resolve/main/tf_model.h5
540 MB
- Xet hash:
- da260701b544cc62457ea64e4b5da7d569acf5150a3e59f8e70f4e3928aa8195
- Size of remote file:
- 540 MB
- SHA256:
- e4748ff371d77f66adaada68d15cb69e105f579bd6b5992e887211e141e10e72
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.