Instructions to use am-azadi/NLP_HuggingFace_Task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use am-azadi/NLP_HuggingFace_Task with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="am-azadi/NLP_HuggingFace_Task")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("am-azadi/NLP_HuggingFace_Task") model = AutoModelForSequenceClassification.from_pretrained("am-azadi/NLP_HuggingFace_Task", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7b5aa05ed187a9cf8a880b99c3ec83327d1c6a780e44270ba7874edfcf62f2f
- Size of remote file:
- 433 MB
- SHA256:
- 478a59e966cdc1edca4cb3805148ecc2c2c5342f8b27008723acbca350abd74c
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