--- license: - apache-2.0 - bsd-3-clause tags: - summarization - summary - booksum - long-document - long-form - tglobal-xl - XL datasets: - kmfoda/booksum metrics: - rouge inference: false model-index: - name: pszemraj/long-t5-tglobal-xl-16384-book-summary results: - task: type: summarization name: Summarization dataset: name: multi_news type: multi_news config: default split: test metrics: - type: rouge value: 36.2043 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzRmMmUyOTVjMmJmZTRiZDcyYzY3MTQ1MmUyNDA5NjVhYzEzYzBiNzcxYTRhMDQ3OTlhMGZjYmJlNDM1M2NjYyIsInZlcnNpb24iOjF9._uArOQ1_0znXDPXMq7unA1OHB-XbgqzzKRbFRcVUzTUJdWk26LiSa2pEEVNNmJPg6Uo7CAvONmhpEswLvl9TAg - type: rouge value: 8.424 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzg0MzljYjVjYWQ3MmRkZDBlOGI5M2RiMGU0M2UwZGUzMDg2NTU0NjcwMTNiN2ZmODEzNTQ0MmEwNDA3NDA5MSIsInZlcnNpb24iOjF9.Dzj85ld6TjosQ8KyUdoadzicMLedEFrICC6Q-08O3qx28d9B9Uke1zw-VWabiuesPEDTRGbWuBgPA5vxYWUZAw - type: rouge value: 17.3721 name: ROUGE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDA3ZjZmODAwMTNlM2RlZmJlMDI5MGVkMGRkMTBjMTYzNDk5ZjFiNTY5MWE1MDUwNWI2MDE4ZDA2YWMwMmI2NCIsInZlcnNpb24iOjF9.MOV_nId0XAK1eMQssG5GN9DsitZaTrxl4jdCJnOg9EZ0-vAw227ln599YV5YfZ1OPJnWwek6rneqqyONiHn9AQ - type: rouge value: 32.3994 name: ROUGE-LSUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmY3MDMwOTZjNWI0YTk1MDgwMzJkYTFiN2U5YWU0Mzc0MWRiMzc1NzZlMDhjMWUwMmY2ODI2MjI5ODBkYWUxOSIsInZlcnNpb24iOjF9._BwGIZbcA4pUBkEAL0cW-JPPta0KSoGug4Z7vogHacUz-AEhIOI5ICUldZh0pt9OK67MpUSzpShJOu3rSt5YDQ - type: loss value: 2.0843334197998047 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWFhMmE5ZjA3ODM4YmVjMDMyMjk5YjNlMjA1MGMzOWY0NTRlYzk1YjZiMzQxMDMxOTMwMjFkNTdmNjM1NDcyMyIsInZlcnNpb24iOjF9.3wbXV4CIIgnfXAnnRztdOR12PwsWsEfiglQQ09K-C1EgW4gai4x9l-wTE2OZ7CTWkuk_tr4tL_uqOCXLZRMtCQ - type: gen_len value: 248.3572 name: gen_len verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWZhOGMwMDJjNGU2MzA2YzI1OWU1ZDY5N2NjZmM1YTA5NDg1MzUwNmU1YTBhNjQyNWYwYzA3OGNmODFjMmE2NSIsInZlcnNpb24iOjF9.Rc9u89zCdbFnjsnmq65l_JvCtUwOX_ZWapKJpTZ-rC8HxcUVfi2Ash2QfvvvxHH_YWhwklxxdnNa0HCm46qLAA --- # long-t5-tglobal-xl + BookSum Summarize long text and get a SparkNotes-esque summary of arbitrary topics! - Generalizes reasonably well to academic & narrative text. - This is the XL checkpoint, which **from a human-evaluation perspective, [produces even better summaries](https://long-t5-xl-book-summary-examples.netlify.app/)**. A simple example/use case with [the base model](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) on ASR is [here](https://longt5-booksum-example.netlify.app/). ## Cheeky Proof-of-Concept A summary of the [infamous navy seals copypasta](https://knowyourmeme.com/memes/navy-seal-copypasta): > In this chapter, the monster explains how he intends to exact revenge on "the little b****" who insulted him. He tells the kiddo that he is a highly trained and experienced killer who will use his arsenal of weapons--including his access to the internet--to exact justice on the little brat. While a somewhat crude example, try running this copypasta through other summarization models to see the difference in comprehension (_despite it not even being a "long" text!_) --- ## Description A fine-tuned version of [google/long-t5-tglobal-xl](https://huggingface.co/google/long-t5-tglobal-xl) on the `kmfoda/booksum` dataset. Read the paper by Guo et al. here: [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) ## How-To in Python > 🚧 `LLM.int8()` appears to be compatible with summarization and does not degrade the quality of the outputs; this is a crucial enabler for using this model on standard GPUs. A PR for this is in-progress [here](https://github.com/huggingface/transformers/pull/20341), and this model card will be updated with instructions once done :) 🚧 Install/update transformers `pip install -U transformers` Summarize text with pipeline: ```python import torch from transformers import pipeline summarizer = pipeline( "summarization", "pszemraj/long-t5-tglobal-xl-16384-book-summary", device=0 if torch.cuda.is_available() else -1, ) long_text = "Here is a lot of text I don't want to read. Replace me" result = summarizer(long_text) print(result[0]["summary_text"]) ``` Pass [other parameters related to beam search textgen](https://huggingface.co/blog/how-to-generate) when calling `summarizer` to get even higher quality results. --- ## About ### Intended uses & limitations While this model seems to improve upon factual consistency, **do not take summaries to be foolproof and check things that seem odd**. Specifically: negation statements (i.e., model says: _This thing does not have [ATTRIBUTE]_ where instead it should have said _This thing has a lot of [ATTRIBUTE]_). - I'm sure someone will write a paper on this eventually (if there isn't one already), but you can usually fact-check this by comparing a specific claim to what the surrounding sentences imply. ### Training and evaluation data `kmfoda/booksum` dataset on HuggingFace - read [the original paper here](https://arxiv.org/abs/2105.08209). - **Initial fine-tuning** only used input text with 12288 tokens input or less and 1024 tokens output or less (_i.e. rows with longer were dropped before training_) for memory reasons. Per brief analysis, summaries in the 12288-16384 range in this dataset are in the **small** minority - In addition, this initial training combined the training and validation sets and trained on these in aggregate to increase the functional dataset size. **Therefore, take the validation set results with a grain of salt; primary metrics should be (always) the test set.** - **final phases of fine-tuning** used the standard conventions of 16384 input/1024 output keeping everything (truncating longer sequences). This did not appear to change the loss/performance much. ### Eval results Official results with the [model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator) will be computed and posted here. **Please read the note above as due to training methods, validation set performance looks better than the test set results will be**. The model achieves the following results on the evaluation set: - eval_loss: 1.2756 - eval_rouge1: 41.8013 - eval_rouge2: 12.0895 - eval_rougeL: 21.6007 - eval_rougeLsum: 39.5382 - eval_gen_len: 387.2945 - eval_runtime: 13908.4995 - eval_samples_per_second: 0.107 - eval_steps_per_second: 0.027 ``` ***** predict/test metrics (initial) ***** predict_gen_len = 506.4368 predict_loss = 2.028 predict_rouge1 = 36.8815 predict_rouge2 = 8.0625 predict_rougeL = 17.6161 predict_rougeLsum = 34.9068 predict_runtime = 2:04:14.37 predict_samples = 1431 predict_samples_per_second = 0.192 predict_steps_per_second = 0.048 ``` \* evaluating big model not as easy as it seems. Doing a bit more investigating --- ## FAQ ### How can I run inference with this on CPU? lol ### How to run inference over a very long (30k+ tokens) document in batches? See `summarize.py` in [the code for my hf space Document Summarization](https://huggingface.co/spaces/pszemraj/document-summarization/blob/main/summarize.py) :) You can also use the same code to split a document into batches of 4096, etc., and run over those with the model. This is useful in situations where CUDA memory is limited. ### How to fine-tune further? See [train with a script](https://huggingface.co/docs/transformers/run_scripts) and [the summarization scripts](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) --- ## Training procedure ### Updates Updates to this model/model card will be posted here as relevant. The model seems fairly converged; if updates/improvements are possible using the `BookSum` dataset, this repo will be updated. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0006 - train_batch_size: 1 - eval_batch_size: 1 - seed: 10350 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 1.0 \*_Prior training sessions used roughly similar parameters (learning rates were higher); multiple sessions were required as this takes eons to train._ ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1 ---