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:
- 15a414d25c2385566d1fef4c7f765eb0a2a1e35f26da652f3577c5079ca48d13
- Size of remote file:
- 499 MB
- SHA256:
- 9eddd2c9307162bc74166df0a3e6fd04982de925eb7f54a9dcb9b8ce6460c783
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