Instructions to use tr3cks/SentimentAnalysis_BETO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tr3cks/SentimentAnalysis_BETO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tr3cks/SentimentAnalysis_BETO")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tr3cks/SentimentAnalysis_BETO") model = AutoModelForSequenceClassification.from_pretrained("tr3cks/SentimentAnalysis_BETO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from tr3cks/SentimentAnalysis_BETO: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/tr3cks/SentimentAnalysis_BETO/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://tr3cks/SentimentAnalysis_BETO@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/tr3cks/SentimentAnalysis_BETO/resolve/refs%2Fpr%2F1/flax_model.msgpack
439 MB
- Xet hash:
- 7a00c55da78038c0470964b5cd0bb015ac5cfcb8d1a0788aac01954062a760bc
- Size of remote file:
- 439 MB
- SHA256:
- 63d3102494716d7c82bea96f0ac96cca999c2ebdd4d99d5a04d3decb7d35f797
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