Instructions to use ad6398/gupshup_e2e_pegasus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ad6398/gupshup_e2e_pegasus with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ad6398/gupshup_e2e_pegasus") model = AutoModelForSeq2SeqLM.from_pretrained("ad6398/gupshup_e2e_pegasus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ad6398/gupshup_e2e_pegasus: direct link, hf CLI and curl.
- Browser
- Download file 1.77 kB
-
https://huggingface.co/ad6398/gupshup_e2e_pegasus/resolve/main/config.json
- Command line
-
hf download hf://ad6398/gupshup_e2e_pegasus/config.json
-
curl -L -o config.json https://huggingface.co/ad6398/gupshup_e2e_pegasus/resolve/main/config.json
1.77 kB
| { | |
| "_name_or_path": "/content/drive/My Drive/Data_summarization/student_mbart/student_mbart_untrained/", | |
| "_num_labels": 3, | |
| "activation_dropout": 0.0, | |
| "activation_function": "gelu", | |
| "add_bias_logits": false, | |
| "add_final_layer_norm": true, | |
| "architectures": [ | |
| "MBartForConditionalGeneration" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 0, | |
| "classif_dropout": 0.0, | |
| "classifier_dropout": 0.0, | |
| "d_model": 1024, | |
| "decoder_attention_heads": 16, | |
| "decoder_ffn_dim": 4096, | |
| "decoder_layerdrop": 0.0, | |
| "decoder_layers": 6, | |
| "decoder_start_token_id": 250004, | |
| "do_blenderbot_90_layernorm": false, | |
| "dropout": 0.1, | |
| "encoder_attention_heads": 16, | |
| "encoder_ffn_dim": 4096, | |
| "encoder_layerdrop": 0.0, | |
| "encoder_layers": 12, | |
| "eos_token_id": 2, | |
| "extra_pos_embeddings": 2, | |
| "force_bos_token_to_be_generated": false, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1", | |
| "2": "LABEL_2" | |
| }, | |
| "init_metadata": { | |
| "copied_decoder_layers": [ | |
| 0, | |
| 2, | |
| 4, | |
| 7, | |
| 9, | |
| 11 | |
| ], | |
| "copied_encoder_layers": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| 4, | |
| 5, | |
| 6, | |
| 7, | |
| 8, | |
| 9, | |
| 10, | |
| 11 | |
| ], | |
| "teacher_type": "mbart" | |
| }, | |
| "init_std": 0.02, | |
| "is_encoder_decoder": true, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_2": 2 | |
| }, | |
| "max_length": 1024, | |
| "max_position_embeddings": 1024, | |
| "model_type": "mbart", | |
| "normalize_before": true, | |
| "normalize_embedding": true, | |
| "num_beams": 5, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "scale_embedding": true, | |
| "static_position_embeddings": false, | |
| "task_specific_params": { | |
| "translation_en_to_ro": { | |
| "decoder_start_token_id": 250020 | |
| } | |
| }, | |
| "vocab_size": 250027 | |
| } | |