Instructions to use aehrm/dtaec-type-normalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrm/dtaec-type-normalizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="aehrm/dtaec-type-normalizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("aehrm/dtaec-type-normalizer") model = AutoModelForSeq2SeqLM.from_pretrained("aehrm/dtaec-type-normalizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation_dropout": 0.0, | |
| "activation_function": "gelu", | |
| "architectures": [ | |
| "BartForConditionalGeneration" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "classifier_dropout": 0.0, | |
| "d_model": 256, | |
| "decoder_attention_heads": 4, | |
| "decoder_ffn_dim": 1024, | |
| "decoder_layerdrop": 0, | |
| "decoder_layers": 4, | |
| "decoder_start_token_id": 2, | |
| "dropout": 0.3, | |
| "encoder_attention_heads": 4, | |
| "encoder_ffn_dim": 1024, | |
| "encoder_layerdrop": 0, | |
| "encoder_layers": 4, | |
| "eos_token_id": 2, | |
| "forced_eos_token_id": 2, | |
| "init_std": 0.02, | |
| "is_encoder_decoder": true, | |
| "max_length": 100, | |
| "max_position_embeddings": 1024, | |
| "model_type": "bart", | |
| "num_beams": 4, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "scale_embedding": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.41.2", | |
| "unk_token_id": 3, | |
| "use_cache": true, | |
| "vocab_size": 122 | |
| } | |