Instructions to use SEBIS/code_trans_t5_small_source_code_summarization_csharp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_source_code_summarization_csharp 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_source_code_summarization_csharp")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_csharp") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_csharp") - Notebooks
- Google Colab
- Kaggle
File size: 633 Bytes
21163c3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"architectures": [
"T5Model"
],
"d_ff": 2048,
"d_kv": 64,
"d_model": 512,
"decoder_start_token_id": 0,
"dropout_rate": 0.1,
"eos_token_id": 1,
"initializer_factor": 1.0,
"is_encoder_decoder": true,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 6,
"num_heads": 8,
"num_layers": 6,
"output_past": true,
"pad_token_id": 0,
"relative_attention_num_buckets": 32,
"task_specific_params": {
"summarization": {
"max_length": 512,
"num_beams": 4,
"prefix": "source code summarization csharp: "
}
},
"vocab_size": 32128
}
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