luluw/bart-large-cnn-finetuned

bart-large-finetuned-billsum

This model is a fine-tuned version of facebook/bart-large-cnn on a FiscalNote/Billsum dataset. It achieves the following results on the evaluation set:

• Loss: 1.1553
• Rouge1: 51.9605
• Rouge2: 36.2784
• Rougel: 44.1511
• Rougelsum: 47.1043
• Gen Len: 63.9903

Model description

More information needed

Intended uses & limitations

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Train Loss	Step	Val Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.4735	1000	1.3306	50.6543	33.9684	42.2550	45.4452	63.9983
1.3146	2000	1.2376	51.0888	34.9554	42.9847	45.8933	63.9903
1.1542	3000	1.1874	51.5755	35.6875	43.6806	46.5762	63.9800
1.0917	4000	1.1714	51.8612	36.1809	44.0608	47.0279	63.9870
1.0380	5000	1.1553	51.9605	36.2784	44.1511	47.1043	63.9903

from transformers import pipeline

summarizer = pipeline("summarization", model="luluw/bart-large-cnn-finetuned")

text = """ 
The paper "Attention is All You Need" revolutionized the field of natural language processing (NLP) by introducing the Transformer architecture, which relies solely on attention mechanisms to model long-range dependencies in sequential data. Prior to this, models like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) were the primary tools for sequence modeling, but they suffered from limitations such as difficulty in parallelization and the vanishing gradient problem. The Transformer, however, breaks free from these constraints by using a self-attention mechanism, which allows it to attend to different parts of a sequence simultaneously, leading to more efficient training and better performance on tasks such as machine translation, text summarization, and language modeling.
The core innovation of the Transformer model lies in its multi-head self-attention mechanism. Unlike RNNs that process sequences step-by-step, the Transformer processes the entire sequence at once by applying self-attention to every word or token. This allows each token to weigh the relevance of other tokens in the sequence, giving the model a global understanding of context. Multi-head attention refers to applying multiple attention layers in parallel, enabling the model to focus on different parts of the input sequence simultaneously. This enhances the model's ability to capture various relationships and nuances in the data.
The Transformer consists of an encoder-decoder structure. The encoder takes in the input sequence, computes self-attention to understand relationships between tokens, and generates a context-aware representation. The decoder, which also incorporates self-attention, generates the output sequence one token at a time by attending to both the previously generated tokens and the encoder's output. This architecture, coupled with position-wise feed-forward networks and layer normalization, makes the Transformer highly scalable and efficient.
Another significant contribution of the paper is the introduction of positional encoding. Since the Transformer lacks the inherent sequential nature of RNNs, it cannot infer the order of tokens from the architecture itself. To overcome this, the authors introduced positional encodings, which are added to the input embeddings to provide the model with information about the relative position of tokens. These encodings allow the model to maintain a sense of order in the data without explicitly processing tokens sequentially.
The original Transformer model proposed in Attention is All You Need had six layers each in both the encoder and decoder. Each layer consists of multi-head attention and feed-forward layers, with residual connections and normalization. The model was trained using the Adam optimizer and applied to machine translation tasks, where it demonstrated state-of-the-art performance, surpassing previous models like LSTMs and GRUs.
One of the key benefits of the Transformer is its ability to parallelize training, as it does not rely on sequential data processing like RNNs. This parallelism allows it to leverage modern GPU architectures effectively, leading to faster training times and the ability to scale to much larger datasets. Furthermore, Transformers handle long-range dependencies better than previous models because self-attention allows every token to interact with every other token in the sequence, regardless of their distance from each other.
"""

print(summarizer(text, max_new_tokens=128)[0]['generated_text'])
>> Attention is All You Need is a paper that revolutionized the field of natural language processing (NLP) by introducing the Transformer architecture, which relies solely on attention mechanisms to model long-range dependencies in sequential data. The Transformer consists of an encoder-decoder structure: the encoder takes in the input sequence, computes self-attention to understand relationships between tokens, and generates a context-aware representation; and the decoder generates the output sequence one token at a time by attending to both the previously generated tokens and encoder output.

Framework versions

• Transformers 4.44.2
• Pytorch 2.2.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1