Instructions to use sbcBI/sentiment_analysis_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sbcBI/sentiment_analysis_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sbcBI/sentiment_analysis_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sbcBI/sentiment_analysis_model") model = AutoModelForSequenceClassification.from_pretrained("sbcBI/sentiment_analysis_model") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the BERT model as inputs.
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## Model description [
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This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for
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further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification.
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the BERT model as inputs.
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## Model description [sbcBI/sentiment_analysis]
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This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for
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further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification.
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