Instructions to use lightbansal/metadata_postprocess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lightbansal/metadata_postprocess 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="lightbansal/metadata_postprocess")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lightbansal/metadata_postprocess") model = AutoModelForSeq2SeqLM.from_pretrained("lightbansal/metadata_postprocess", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from lightbansal/metadata_postprocess: direct link, hf CLI and curl.
- Browser
- Download file 3.11 kB
-
https://huggingface.co/lightbansal/metadata_postprocess/resolve/refs%2Fpr%2F1/config.json
- Command line
-
hf download hf://lightbansal/metadata_postprocess@refs/pr/1/config.json
-
curl -L -o config.json https://huggingface.co/lightbansal/metadata_postprocess/resolve/refs%2Fpr%2F1/config.json
3.11 kB
| { | |
| "_name_or_path": "AutoTrain", | |
| "activation_dropout": 0.1, | |
| "activation_function": "relu", | |
| "add_bias_logits": false, | |
| "add_final_layer_norm": true, | |
| "architectures": [ | |
| "PegasusForConditionalGeneration" | |
| ], | |
| "attention_dropout": 0.1, | |
| "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": 16, | |
| "decoder_start_token_id": 0, | |
| "dropout": 0.1, | |
| "encoder_attention_heads": 16, | |
| "encoder_ffn_dim": 4096, | |
| "encoder_layerdrop": 0.0, | |
| "encoder_layers": 16, | |
| "eos_token_id": 1, | |
| "extra_pos_embeddings": 1, | |
| "force_bos_token_to_be_generated": false, | |
| "forced_eos_token_id": 1, | |
| "gradient_checkpointing": false, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1", | |
| "2": "LABEL_2" | |
| }, | |
| "init_std": 0.02, | |
| "is_encoder_decoder": true, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_2": 2 | |
| }, | |
| "length_penalty": 0.8, | |
| "max_length": 256, | |
| "max_position_embeddings": 1024, | |
| "model_type": "pegasus", | |
| "normalize_before": true, | |
| "normalize_embedding": false, | |
| "num_beams": 8, | |
| "num_hidden_layers": 16, | |
| "pad_token_id": 0, | |
| "scale_embedding": true, | |
| "static_position_embeddings": true, | |
| "task_specific_params": { | |
| "summarization_aeslc": { | |
| "length_penalty": 0.6, | |
| "max_length": 32, | |
| "max_position_embeddings": 512 | |
| }, | |
| "summarization_arxiv": { | |
| "length_penalty": 0.8, | |
| "max_length": 256, | |
| "max_position_embeddings": 1024 | |
| }, | |
| "summarization_big_patent": { | |
| "length_penalty": 0.7, | |
| "max_length": 256, | |
| "max_position_embeddings": 1024 | |
| }, | |
| "summarization_billsum": { | |
| "length_penalty": 0.6, | |
| "max_length": 256, | |
| "max_position_embeddings": 1024 | |
| }, | |
| "summarization_cnn_dailymail": { | |
| "length_penalty": 0.8, | |
| "max_length": 128, | |
| "max_position_embeddings": 1024 | |
| }, | |
| "summarization_gigaword": { | |
| "length_penalty": 0.6, | |
| "max_length": 32, | |
| "max_position_embeddings": 128 | |
| }, | |
| "summarization_large": { | |
| "length_penalty": 0.8, | |
| "max_length": 256, | |
| "max_position_embeddings": 1024 | |
| }, | |
| "summarization_multi_news": { | |
| "length_penalty": 0.8, | |
| "max_length": 256, | |
| "max_position_embeddings": 1024 | |
| }, | |
| "summarization_newsroom": { | |
| "length_penalty": 0.8, | |
| "max_length": 128, | |
| "max_position_embeddings": 512 | |
| }, | |
| "summarization_pubmed": { | |
| "length_penalty": 0.8, | |
| "max_length": 256, | |
| "max_position_embeddings": 1024 | |
| }, | |
| "summarization_reddit_tifu": { | |
| "length_penalty": 0.6, | |
| "max_length": 128, | |
| "max_position_embeddings": 512 | |
| }, | |
| "summarization_wikihow": { | |
| "length_penalty": 0.6, | |
| "max_length": 256, | |
| "max_position_embeddings": 512 | |
| }, | |
| "summarization_xsum": { | |
| "length_penalty": 0.8, | |
| "max_length": 64, | |
| "max_position_embeddings": 512 | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.20.0", | |
| "use_cache": true, | |
| "vocab_size": 96103 | |
| } | |