Summarization
PEFT
Safetensors
Transformers
encoder-decoder
text2text-generation
spanish
lora
seq2seq
beto
Instructions to use SenaSoft/chdv-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SenaSoft/chdv-summarization with PEFT:
Task type is invalid.
- Transformers
How to use SenaSoft/chdv-summarization with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="SenaSoft/chdv-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SenaSoft/chdv-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("SenaSoft/chdv-summarization") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "EncoderDecoderModel" | |
| ], | |
| "decoder": { | |
| "_name_or_path": "dccuchile/bert-base-spanish-wwm-uncased", | |
| "add_cross_attention": true, | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": true, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31002 | |
| }, | |
| "decoder_start_token_id": 4, | |
| "dtype": "float32", | |
| "encoder": { | |
| "_name_or_path": "dccuchile/bert-base-spanish-wwm-uncased", | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31002 | |
| }, | |
| "eos_token_id": 5, | |
| "is_encoder_decoder": true, | |
| "model_type": "encoder-decoder", | |
| "pad_token_id": 1, | |
| "transformers_version": "4.56.2", | |
| "vocab_size": 31002 | |
| } | |