Instructions to use cppmai/finetune_bart-base2_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cppmai/finetune_bart-base2_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cppmai/finetune_bart-base2_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cppmai/finetune_bart-base2_classification") model = AutoModelForSequenceClassification.from_pretrained("cppmai/finetune_bart-base2_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/gpfs/home/pchau/intern-text-summarization/pretrain_bart/finetune-classification/output_bart-base-2", | |
| "activation_dropout": 0.1, | |
| "activation_function": "gelu", | |
| "add_bias_logits": false, | |
| "add_final_layer_norm": false, | |
| "architectures": [ | |
| "BartForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": 0, | |
| "classif_dropout": 0.1, | |
| "classifier_dropout": 0.0, | |
| "d_model": 768, | |
| "decoder_attention_heads": 12, | |
| "decoder_ffn_dim": 3072, | |
| "decoder_layerdrop": 0.0, | |
| "decoder_layers": 6, | |
| "decoder_start_token_id": 2, | |
| "dropout": 0.1, | |
| "early_stopping": true, | |
| "encoder_attention_heads": 12, | |
| "encoder_ffn_dim": 3072, | |
| "encoder_layerdrop": 0.0, | |
| "encoder_layers": 6, | |
| "eos_token_id": 2, | |
| "forced_bos_token_id": 0, | |
| "forced_eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "id2label": { | |
| "0": "advertisement", | |
| "1": "budget", | |
| "2": "email", | |
| "3": "file_folder", | |
| "4": "form", | |
| "5": "handwritten", | |
| "6": "invoice", | |
| "7": "letter", | |
| "8": "memo", | |
| "9": "news_article", | |
| "10": "presentation", | |
| "11": "publication", | |
| "12": "questionnaire", | |
| "13": "report", | |
| "14": "resume", | |
| "15": "specification" | |
| }, | |
| "init_std": 0.02, | |
| "is_encoder_decoder": true, | |
| "label2id": { | |
| "advertisement": 0, | |
| "budget": 1, | |
| "email": 2, | |
| "file_folder": 3, | |
| "form": 4, | |
| "handwritten": 5, | |
| "invoice": 6, | |
| "letter": 7, | |
| "memo": 8, | |
| "news_article": 9, | |
| "presentation": 10, | |
| "publication": 11, | |
| "questionnaire": 12, | |
| "report": 13, | |
| "resume": 14, | |
| "specification": 15 | |
| }, | |
| "max_position_embeddings": 1024, | |
| "model_type": "bart", | |
| "no_repeat_ngram_size": 3, | |
| "normalize_before": false, | |
| "normalize_embedding": true, | |
| "num_beams": 4, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 1, | |
| "problem_type": "single_label_classification", | |
| "scale_embedding": false, | |
| "task_specific_params": { | |
| "summarization": { | |
| "length_penalty": 1.0, | |
| "max_length": 128, | |
| "min_length": 12, | |
| "num_beams": 4 | |
| }, | |
| "summarization_cnn": { | |
| "length_penalty": 2.0, | |
| "max_length": 142, | |
| "min_length": 56, | |
| "num_beams": 4 | |
| }, | |
| "summarization_xsum": { | |
| "length_penalty": 1.0, | |
| "max_length": 62, | |
| "min_length": 11, | |
| "num_beams": 6 | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.41.2", | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } | |