Instructions to use mllm-dev/gpt2_f_experiment_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_3") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64b7a35cef10bacaa62ab7373db63d224a89d98f2edabcc0eceb75aeb981d149
- Size of remote file:
- 996 MB
- SHA256:
- df3c93669a2f5441574b1143ed6351f18cdcd0ffa8d32b53e83694e8f18bbfe3
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