Instructions to use mllm-dev/gpt2_f_experiment_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_5") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 84894a4b024c422311acc0bbe43395931910ec763f96f75c2e926291205c3d92
- Size of remote file:
- 996 MB
- SHA256:
- 46b2a8cb2ee9fd60ff9d899e5d49440a81e4721542133062a6539c22e6ea7bd3
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