Instructions to use samanehs/gpt2_imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use samanehs/gpt2_imdb with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://samanehs/gpt2_imdb") - Keras
How to use samanehs/gpt2_imdb with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://samanehs/gpt2_imdb") - Notebooks
- Google Colab
- Kaggle
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"module": "keras_nlp.src.models.gpt2.gpt2_causal_lm",
"class_name": "GPT2CausalLM",
"config": {
"backbone": {
"module": "keras_nlp.src.models.gpt2.gpt2_backbone",
"class_name": "GPT2Backbone",
"config": {
"name": "gpt2_backbone",
"trainable": true,
"vocabulary_size": 50257,
"num_layers": 12,
"num_heads": 12,
"hidden_dim": 768,
"intermediate_dim": 3072,
"dropout": 0.1,
"max_sequence_length": 1024
},
"registered_name": "keras_nlp>GPT2Backbone"
},
"preprocessor": {
"module": "keras_nlp.src.models.gpt2.gpt2_causal_lm_preprocessor",
"class_name": "GPT2CausalLMPreprocessor",
"config": {
"name": "gpt2_causal_lm_preprocessor",
"trainable": true,
"dtype": "float32",
"tokenizer": {
"module": "keras_nlp.src.models.gpt2.gpt2_tokenizer",
"class_name": "GPT2Tokenizer",
"config": {
"name": "gpt2_tokenizer",
"trainable": true,
"dtype": "int32",
"sequence_length": null,
"add_prefix_space": false
},
"registered_name": "keras_nlp>GPT2Tokenizer"
},
"sequence_length": 1024,
"add_start_token": true,
"add_end_token": true
},
"registered_name": "keras_nlp>GPT2CausalLMPreprocessor"
},
"name": "gpt2_causal_lm"
},
"registered_name": "keras_nlp>GPT2CausalLM"
} |