Text Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
sagemaker
roberta-bne
TextClassification
SentimentAnalysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use edumunozsala/roberta_bne_sentiment_analysis_es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edumunozsala/roberta_bne_sentiment_analysis_es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edumunozsala/roberta_bne_sentiment_analysis_es")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edumunozsala/roberta_bne_sentiment_analysis_es") model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/roberta_bne_sentiment_analysis_es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from edumunozsala/roberta_bne_sentiment_analysis_es: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/edumunozsala/roberta_bne_sentiment_analysis_es/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://edumunozsala/roberta_bne_sentiment_analysis_es/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/edumunozsala/roberta_bne_sentiment_analysis_es/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 8297b2d4f96dc7a2b21a1a0f3a2187677cf78a347e12b217a017f672faa465e9
- Size of remote file:
- 499 MB
- SHA256:
- 931d3bb575e1cba4d1b869764c6c660b4e10bf2776e84072ddf12f4fdb656eda
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