Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use tsessk/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsessk/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tsessk/content")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tsessk/content") model = AutoModelForSequenceClassification.from_pretrained("tsessk/content") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd4a974c1be061859b4d3a41f53c01a1747d8c686848ce5f80f147555976b1a9
- Size of remote file:
- 5.37 kB
- SHA256:
- 658041dc11e911485e660d4f7beeed60a61f93182f7e328985adb3ff21eccd7b
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