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 pytorch_model.bin from tr3cks/SentimentAnalysis_BETO: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/tr3cks/SentimentAnalysis_BETO/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://tr3cks/SentimentAnalysis_BETO@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tr3cks/SentimentAnalysis_BETO/resolve/refs%2Fpr%2F1/pytorch_model.bin
440 MB
- Xet hash:
- 559ff2d3a8acdde69c43e37c292db6442ce4fe57cc5a852a673302a6027a0624
- Size of remote file:
- 440 MB
- SHA256:
- 89b31aabf7e2f1a31204bffd8d727c94eddef37f70dade194ca65cf5892d6706
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