Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use misaza/Sentimiento-appmovilesXLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use misaza/Sentimiento-appmovilesXLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="misaza/Sentimiento-appmovilesXLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("misaza/Sentimiento-appmovilesXLM") model = AutoModelForSequenceClassification.from_pretrained("misaza/Sentimiento-appmovilesXLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from misaza/Sentimiento-appmovilesXLM: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/misaza/Sentimiento-appmovilesXLM/resolve/main/tokenizer.json
- Command line
-
hf download hf://misaza/Sentimiento-appmovilesXLM/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/misaza/Sentimiento-appmovilesXLM/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 91ebb7cab3164874e11a1c956d00a74f87b12b56090aaae408f5f29dc6fc0333
- Size of remote file:
- 17.1 MB
- SHA256:
- 349ddbc97c4742ad2a4f2da1ecf985a03c45bff47d712556b3a73db66512977a
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