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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 934b9b998780953a56742a1df8fd83fb2189c5ea6f38a6f52f9400ebafcfc18f
- Size of remote file:
- 714 MB
- SHA256:
- 51316876d0d38d6ac3b97e93231ba1c196ee1bc6b6b12aa5bd9445cd33d9eff9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.