Instructions to use Samuela39/MySummarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Samuela39/MySummarizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Samuela39/MySummarizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Samuela39/MySummarizer") model = AutoModel.from_pretrained("Samuela39/MySummarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Samuela39/MySummarizer: direct link, hf CLI and curl.
- Browser
- Download file 1.48 kB
-
https://huggingface.co/Samuela39/MySummarizer/resolve/main/config.json
- Command line
-
hf download hf://Samuela39/MySummarizer/config.json
-
curl -L -o config.json https://huggingface.co/Samuela39/MySummarizer/resolve/main/config.json
1.48 kB
| { | |
| "_name_or_path": "./MyTSM", | |
| "architectures": [ | |
| "T5Model" | |
| ], | |
| "classifier_dropout": 0.0, | |
| "d_ff": 2048, | |
| "d_kv": 64, | |
| "d_model": 512, | |
| "decoder_start_token_id": 0, | |
| "dense_act_fn": "relu", | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 1, | |
| "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": 6, | |
| "num_heads": 8, | |
| "num_layers": 6, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "relative_attention_max_distance": 128, | |
| "relative_attention_num_buckets": 32, | |
| "task_specific_params": { | |
| "summarization": { | |
| "early_stopping": true, | |
| "length_penalty": 2.0, | |
| "max_length": 200, | |
| "min_length": 30, | |
| "no_repeat_ngram_size": 3, | |
| "num_beams": 4, | |
| "prefix": "summarize: " | |
| }, | |
| "translation_en_to_de": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to German: " | |
| }, | |
| "translation_en_to_fr": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to French: " | |
| }, | |
| "translation_en_to_ro": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to Romanian: " | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.38.2", | |
| "use_cache": true, | |
| "vocab_size": 32128 | |
| } | |