Instructions to use saadamin2k13/byT5_ft_semantic_parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saadamin2k13/byT5_ft_semantic_parser with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("saadamin2k13/byT5_ft_semantic_parser") model = AutoModelForSeq2SeqLM.from_pretrained("saadamin2k13/byT5_ft_semantic_parser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from saadamin2k13/byT5_ft_semantic_parser: direct link, hf CLI and curl.
- Browser
- Download file 756 Bytes
-
https://huggingface.co/saadamin2k13/byT5_ft_semantic_parser/resolve/main/config.json
- Command line
-
hf download hf://saadamin2k13/byT5_ft_semantic_parser/config.json
-
curl -L -o config.json https://huggingface.co/saadamin2k13/byT5_ft_semantic_parser/resolve/main/config.json
756 Bytes
| { | |
| "_name_or_path": "google/byt5-base", | |
| "architectures": [ | |
| "T5ForConditionalGeneration" | |
| ], | |
| "d_ff": 3968, | |
| "d_kv": 64, | |
| "d_model": 1536, | |
| "decoder_start_token_id": 0, | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 1, | |
| "feed_forward_proj": "gated-gelu", | |
| "gradient_checkpointing": false, | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "layer_norm_epsilon": 1e-06, | |
| "max_length": 512, | |
| "model_type": "t5", | |
| "num_decoder_layers": 6, | |
| "num_heads": 12, | |
| "num_layers": 18, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "relative_attention_num_buckets": 32, | |
| "tie_word_embeddings": false, | |
| "tokenizer_class": "ByT5Tokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.14.1", | |
| "use_cache": true, | |
| "vocab_size": 384 | |
| } | |