| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_keras_callback |
| model-index: |
| - name: articleGeneratorV1.0 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should |
| probably proofread and complete it, then remove this comment. --> |
|
|
| # 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. |
|
|
| ```python |
| # Install transformers library |
| !pip install transformers |
| ``` |
|
|
| ```python |
| # 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) |
| ``` |
|
|
| ```python |
| # 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](https://huggingface.co/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 |
| |
| | Train Loss | Validation Loss | Train Rougel | Epoch | |
| |:----------:|:---------------:|:-----------------------------------------------:|:-----:| |
| | 4.9218 | 4.0315 | tf.Tensor(0.08038119, shape=(), dtype=float32) | 0 | |
| | 4.2887 | 3.8366 | tf.Tensor(0.08103053, shape=(), dtype=float32) | 1 | |
| | 4.1269 | 3.7328 | tf.Tensor(0.081041485, shape=(), dtype=float32) | 2 | |
| | 4.0276 | 3.6614 | tf.Tensor(0.081364945, shape=(), dtype=float32) | 3 | |
| | 3.9568 | 3.6096 | tf.Tensor(0.08172019, shape=(), dtype=float32) | 4 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.27.4 |
| - TensorFlow 2.12.0 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
| |
