Seungjun/textSummaryV1.0

How to use the model

# Load tokenizer and model
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Seungjun/textSummaryV1.0")
model = TFAutoModelForSeq2SeqLM.from_pretrained("Seungjun/textSummaryV1.0")

# Get the original text - text you want to summarize
original = """
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.
"""

# Now summarize the original text using pipline method
from transformers import pipeline

summarizer = pipeline("summarization", model=model, tokenizer=tokenizer, framework="tf")
summarizer(
    original,
    min_length=20,
    max_length=1024,
)

Your max_length is set to 1024, but you input_length is only 269. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=134)
[{'summary_text': 'Neural networks are a type of machine learning that is inspired by the human brain. They are made up of a node layer, a hidden layer, and an output layer. They are used to learn and improve their accuracy. They can take minutes versus hours to identify and identify.'}]
[ ]

textSummaryV10

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: 1.6512
• Validation Loss: 1.5292
• Train Rougel: tf.Tensor(0.27060625, 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

Train Loss	Validation Loss	Train Rougel	Epoch
1.9373	1.6561	tf.Tensor(0.25774935, shape=(), dtype=float32)	0
1.7678	1.5957	tf.Tensor(0.2631886, shape=(), dtype=float32)	1
1.7149	1.5662	tf.Tensor(0.26651797, shape=(), dtype=float32)	2
1.6796	1.5473	tf.Tensor(0.268827, shape=(), dtype=float32)	3
1.6512	1.5292	tf.Tensor(0.27060625, shape=(), dtype=float32)	4

Framework versions

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