Instructions to use uclanlp/plbart-cs-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uclanlp/plbart-cs-java with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-cs-java") model = AutoModelForSeq2SeqLM.from_pretrained("uclanlp/plbart-cs-java") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation_dropout": 0.0, | |
| "activation_function": "gelu", | |
| "architectures": [ | |
| "PLBartForConditionalGeneration" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": 0.0, | |
| "d_model": 768, | |
| "decoder_attention_heads": 12, | |
| "decoder_ffn_dim": 3072, | |
| "decoder_layerdrop": 0.0, | |
| "decoder_layers": 6, | |
| "dropout": 0.1, | |
| "encoder_attention_heads": 12, | |
| "encoder_ffn_dim": 3072, | |
| "encoder_layerdrop": 0.0, | |
| "encoder_layers": 6, | |
| "eos_token_id": 2, | |
| "forced_eos_token_id": 2, | |
| "init_std": 0.02, | |
| "is_encoder_decoder": true, | |
| "max_position_embeddings": 1024, | |
| "model_type": "plbart", | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 1, | |
| "scale_embedding": true, | |
| "transformers_version": "4.13.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 50005 | |
| } | |