Instructions to use NLPScholars/Roberta-Earning-Call-Transcript-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLPScholars/Roberta-Earning-Call-Transcript-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NLPScholars/Roberta-Earning-Call-Transcript-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NLPScholars/Roberta-Earning-Call-Transcript-Classification") model = AutoModelForSequenceClassification.from_pretrained("NLPScholars/Roberta-Earning-Call-Transcript-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7579278b30eef1b3e920d50b51555439bd64f51e3c5e0661a5c3c3140c32f7d5
- Size of remote file:
- 499 MB
- SHA256:
- 7e576054cd7500c0dbbf0fd1a8f9eed8654a4dee51cfc1f088bd1f053e2cf0f3
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