Instructions to use miguelvictor/python-fromzero-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miguelvictor/python-fromzero-t5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("miguelvictor/python-fromzero-t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("miguelvictor/python-fromzero-t5-base") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": ["T5ForConditionalGeneration"], | |
| "bos_token_id": 2, | |
| "d_ff": 2048, | |
| "d_kv": 64, | |
| "d_model": 768, | |
| "decoder_start_token_id": 0, | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 3, | |
| "feed_forward_proj": "gated-gelu", | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "layer_norm_epsilon": 1e-6, | |
| "model_type": "t5", | |
| "n_positions": 512, | |
| "num_decoder_layers": 6, | |
| "num_heads": 12, | |
| "num_layers": 6, | |
| "pad_token_id": 0, | |
| "relative_attention_num_buckets": 32, | |
| "tokenizer": "default", | |
| "transformers_version": "4.5.1", | |
| "vocab_size": 16000 | |
| } | |