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:
- 4c3587f03c90386f5c109436735436a62788da4696285b74c1bcab09d64d103f
- Size of remote file:
- 18.3 MB
- SHA256:
- 51c292478d94ec3a01461bdfa82eb0885d262eb09e615679b2d69dedb6ad09e7
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