Instructions to use EMBEDDIA/sloberta-tweetsentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBEDDIA/sloberta-tweetsentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EMBEDDIA/sloberta-tweetsentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/sloberta-tweetsentiment") model = AutoModelForSequenceClassification.from_pretrained("EMBEDDIA/sloberta-tweetsentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EMBEDDIA/sloberta-tweetsentiment: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/EMBEDDIA/sloberta-tweetsentiment/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EMBEDDIA/sloberta-tweetsentiment/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EMBEDDIA/sloberta-tweetsentiment/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 1fc26a848be29bc0d71178247fc6688ebc12786012fe8334f27b1299a6326669
- Size of remote file:
- 443 MB
- SHA256:
- 1f78b2e786344099970ca6316e6faee8b6c69f4bbf607dce13c00ca6f4cad51f
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