Instructions to use aieng-lab/codet5p-220m_bug-issue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aieng-lab/codet5p-220m_bug-issue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aieng-lab/codet5p-220m_bug-issue")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aieng-lab/codet5p-220m_bug-issue") model = AutoModelForSequenceClassification.from_pretrained("aieng-lab/codet5p-220m_bug-issue") - Notebooks
- Google Colab
- Kaggle
File size: 800 Bytes
f20686b | 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 32 33 34 | {
"_name_or_path": "Salesforce/codet5p-220m",
"architectures": [
"T5ForSequenceClassification"
],
"bos_token_id": 1,
"classifier_dropout": 0.0,
"d_ff": 3072,
"d_kv": 64,
"d_model": 768,
"decoder_start_token_id": 0,
"dense_act_fn": "relu",
"dropout_rate": 0.1,
"eos_token_id": 2,
"feed_forward_proj": "relu",
"initializer_factor": 1.0,
"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 12,
"num_heads": 12,
"num_layers": 12,
"output_past": true,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"torch_dtype": "bfloat16",
"transformers_version": "4.48.3",
"use_cache": true,
"vocab_size": 32100
}
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