Instructions to use gustavecortal/roberta_emo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gustavecortal/roberta_emo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gustavecortal/roberta_emo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gustavecortal/roberta_emo") model = AutoModelForSequenceClassification.from_pretrained("gustavecortal/roberta_emo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Evaluation results for gustavecortal/roberta_emo model as a base model for other tasks
As part of a research effort to identify high quality models in Huggingface that can serve as base models for further finetuning, we evaluated this by finetuning on 36 datasets. The model ranks 2nd among all tested models for the roberta-base architecture as of 18/01/2023. To share this information with others in your model card, please add the following evaluation results to your README.md page. For more information please see https://ibm.github.io/model-recycling/ or contact me. Best regards, Elad Venezian eladv@il.ibm.com IBM Research AI836f3cb
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update model card README.md
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