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 training_args.bin from EMBEDDIA/sloberta-tweetsentiment: direct link, hf CLI and curl.
- Browser
- Download file 2.61 kB
-
https://huggingface.co/EMBEDDIA/sloberta-tweetsentiment/resolve/main/training_args.bin
- Command line
-
hf download hf://EMBEDDIA/sloberta-tweetsentiment/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EMBEDDIA/sloberta-tweetsentiment/resolve/main/training_args.bin
2.61 kB
- Xet hash:
- db3e571235907f378886f3b84cf34dfaa7f8121075a6e9c6bed652401c556cca
- Size of remote file:
- 2.61 kB
- SHA256:
- 1edc66c94aac94e6b4456b65693f2e26541996f14501b6ee5c3bf517f44df739
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.