Instructions to use CamiloVega/Tweet-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CamiloVega/Tweet-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CamiloVega/Tweet-Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CamiloVega/Tweet-Classifier") model = AutoModelForSequenceClassification.from_pretrained("CamiloVega/Tweet-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-12/optimizer.pt from CamiloVega/Tweet-Classifier: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/CamiloVega/Tweet-Classifier/resolve/main/checkpoint-12/optimizer.pt
- Command line
-
hf download hf://CamiloVega/Tweet-Classifier/checkpoint-12/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/CamiloVega/Tweet-Classifier/resolve/main/checkpoint-12/optimizer.pt
876 MB
- Xet hash:
- 375c58af433b347447905b87b4b521f931f56bf9868781ba6827f62b26818e94
- Size of remote file:
- 876 MB
- SHA256:
- 44aa1fe4707693443a04338f9b6c4571dbe2c4d513157556fc0f27792a2ab262
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