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:
- 5be6b96ce36b2e1b20ac55539ed22bb05d588ae5e04b472d1d7740f1768e6a51
- Size of remote file:
- 499 MB
- SHA256:
- 77c9b5d92aeda03f3e9e451fccebd3ea6522ce6f1ac8c77bcbc6a7861a58818e
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