Instructions to use aekupor/model_utterance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aekupor/model_utterance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aekupor/model_utterance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aekupor/model_utterance") model = AutoModelForSequenceClassification.from_pretrained("aekupor/model_utterance") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1031c7f6fc9a28557f8f7021de53b7a3041e55d975ac37fabd6a377a9863cd9
- Size of remote file:
- 499 MB
- SHA256:
- 8ba1fcd9cd1ccf8636c01436ef0ba4a0b736244197e7936e2acc6be587d51197
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