Instructions to use mp6kv/ACTS_feedback1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mp6kv/ACTS_feedback1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mp6kv/ACTS_feedback1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mp6kv/ACTS_feedback1") model = AutoModelForSequenceClassification.from_pretrained("mp6kv/ACTS_feedback1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87318bc15105c5dd790a29047a32f9fe579ee310fc2a4cf97b80ca39fbca85e2
- Size of remote file:
- 3.06 kB
- SHA256:
- 9eb9ab9900907b95e710dc8dffb116843d5f09ba516ee62d511dea63d3f04758
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