Instructions to use SEBIS/code_trans_t5_small_program_synthese_multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_program_synthese_multitask with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="SEBIS/code_trans_t5_small_program_synthese_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_multitask") - Notebooks
- Google Colab
- Kaggle
Add TF weights
Browse filesValidated by the `pt_to_tf` CLI. Max crossload hidden state difference=1.431e-06; Max converted hidden state difference=1.431e-06.
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0c3133826badace8137e6b0e564c6fedea1ba2e0a11d96eaaf30584c924b383
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size 242297600
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