Instructions to use Grey01/bart_billsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use Grey01/bart_billsum with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://Grey01/bart_billsum") - Keras
How to use Grey01/bart_billsum with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Grey01/bart_billsum") - Notebooks
- Google Colab
- Kaggle
File size: 2,352 Bytes
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"module": "keras_nlp.src.models.bart.bart_seq_2_seq_lm",
"class_name": "BartSeq2SeqLM",
"config": {
"backbone": {
"module": "keras_nlp.src.models.bart.bart_backbone",
"class_name": "BartBackbone",
"config": {
"name": "bart_backbone",
"trainable": true,
"vocabulary_size": 50265,
"num_layers": 6,
"num_heads": 12,
"hidden_dim": 768,
"intermediate_dim": 3072,
"dropout": 0.1,
"max_sequence_length": 1024
},
"registered_name": "keras_nlp>BartBackbone"
},
"preprocessor": {
"module": "keras_nlp.src.models.bart.bart_seq_2_seq_lm_preprocessor",
"class_name": "BartSeq2SeqLMPreprocessor",
"config": {
"name": "bart_seq2_seq_lm_preprocessor",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "mixed_float16"
},
"registered_name": null
},
"tokenizer": {
"module": "keras_nlp.src.models.bart.bart_tokenizer",
"class_name": "BartTokenizer",
"config": {
"name": "bart_tokenizer",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "int32"
},
"registered_name": null
},
"sequence_length": null,
"add_prefix_space": false
},
"registered_name": "keras_nlp>BartTokenizer"
},
"encoder_sequence_length": 512,
"decoder_sequence_length": 128
},
"registered_name": "keras_nlp>BartSeq2SeqLMPreprocessor"
},
"name": "bart_seq2_seq_lm"
},
"registered_name": "keras_nlp>BartSeq2SeqLM"
} |