Instructions to use mllm-dev/gpt2_f_experiment_3_drug_data_new_run 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_drug_data_new_run 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_drug_data_new_run")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_3_drug_data_new_run") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_3_drug_data_new_run") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d19ec7f219d30d9309112ff70a715bed658abc8fda88249954e0e84a94f2b08
- Size of remote file:
- 996 MB
- SHA256:
- fda09667e78326146d40bb90c4380aa94d7bb6d2848bd5920c82e84ca824c97e
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