Instructions to use abletobetable/text_feature_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abletobetable/text_feature_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abletobetable/text_feature_extractor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abletobetable/text_feature_extractor") model = AutoModelForSequenceClassification.from_pretrained("abletobetable/text_feature_extractor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45fb808a0e071cbdf9eeb4ef63379e2ae17854c45528ca9651f394c4ef019074
- Size of remote file:
- 3.52 kB
- SHA256:
- 8e94b6cda56bd94550703fa35b872b8a76d131a3ab9f8a23a96529243c6bbed8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.