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What does model do and how to use it

Just provide an title to the model and it will generate a whole article about it.

# Install transformers library
!pip install transformers

# Load tokenizer and model
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TFAutoModelForSeq2SeqLM
model_name = "Seungjun/articleGeneratorV1.0"
tokenizer = AutoTokenizer.from_pretrained("t5-small")
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)

# Get the article for a given title
from transformers import pipeline
summarizer = pipeline("summarization", model=model, tokenizer=tokenizer, framework="tf")
summarizer(
    "Steve Jobs", # title
    min_length=500,
    max_length=1024,
)

Result:

Current limitation of the model

It generate aot of lies. 99% of the word generated by this model is not true.

articleGeneratorV1.0

This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 3.9568
• Validation Loss: 3.6096
• Train Rougel: tf.Tensor(0.08172019, shape=(), dtype=float32)
• Epoch: 4

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Framework versions

• Transformers 4.27.4
• TensorFlow 2.12.0
• Datasets 2.11.0
• Tokenizers 0.13.3