Instructions to use mllm-dev/gpt2_f_experiment_1_1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_1_1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_1_1000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_1_1000") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_1_1000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5688edd025965435c7153b9965734d78a0af01eb97e049e3360de8ade72d2581
- Size of remote file:
- 4.54 kB
- SHA256:
- 71fb8add506dc39cd210a3591968d6412d4a9b16ebba0406d38e14b534d6e376
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