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--- |
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language: |
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- en |
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tags: |
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- summarization |
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datasets: |
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- ccdv/mediasum |
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metrics: |
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- rouge |
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model-index: |
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- name: ccdv/lsg-bart-base-4096-mediasum |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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**This model relies on a custom modeling file, you need to add trust_remote_code=True**\ |
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**See [\#13467](https://github.com/huggingface/transformers/pull/13467)** |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-4096-mediasum", trust_remote_code=True) |
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model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-mediasum", trust_remote_code=True) |
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text = "Replace by what you want." |
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pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=0) |
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generated_text = pipe( |
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text, |
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truncation=True, |
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max_length=64, |
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no_repeat_ngram_size=7, |
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num_beams=2, |
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early_stopping=True |
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) |
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``` |
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# ccdv/lsg-bart-base-4096-mediasum |
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This model is a fine-tuned version of [ccdv/lsg-bart-base-4096](https://huggingface.co/ccdv/lsg-bart-base-4096) on the [ccdv/mediasum roberta_prepended](https://huggingface.co/datasets/ccdv/mediasum) dataset. \ |
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It achieves the following results on the test set: |
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| Length | Sparse Type | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum | |
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|:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- | |
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| 4096 | Local | 256 | 0 | 768 | 35.16 | 18.13 | 31.54 | 32.20 | |
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| 4096 | Local | 128 | 0 | 384 | 34.16 | 17.61 | 30.75 | 31.41 | |
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| 4096 | Pooling | 128 | 4 | 644 | 34.52 | 17.71 | 31.01 | 31.67 | |
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| 4096 | Stride | 128 | 4 | 644 | 35.05 | 18.11 | 31.47 | 32.13 | |
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| 4096 | Block Stride | 128 | 4 | 644 | 34.72 | 17.81 | 31.13 | 31.82 | |
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| 4096 | Norm | 128 | 4 | 644 | 34.75 | 17.86 | 31.10 | 31.77 | |
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| 4096 | LSH | 128 | 4 | 644 | 34.54 | 17.81 | 31.05 | 31.71 | |
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With smaller block size (lower ressources): |
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| Length | Sparse Type | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum | |
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|:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- | |
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| 4096 | Local | 64 | 0 | 192 | 32.55 | 16.66 | 29.36 | 30.00 | |
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| 4096 | Local | 32 | 0 | 96 | 30.98 | 15.41 | 27.84 | 28.46 | |
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| 4096 | Pooling | 32 | 4 | 160 | 31.84 | 16.02 | 28.68 | 29.30 | |
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| 4096 | Stride | 32 | 4 | 160 | 32.67 | 16.68 | 29.47 | 30.10 | |
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| 4096 | Block Stride | 32 | 4 | 160 | 32.51 | 16.64 | 29.33 | 29.94 | |
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| 4096 | Norm | 32 | 4 | 160 | 32.44 | 16.48 | 29.20 | 29.79 | |
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| 4096 | LSH | 32 | 4 | 160 | 31.79 | 16.04 | 28.67 | 29.31 | |
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## Model description |
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The model relies on Local-Sparse-Global attention to handle long sequences: |
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The model has about ~145 millions parameters (6 encoder layers - 6 decoder layers). \ |
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The model is warm started from BART-base, converted to handle long sequences (encoder only) and fine tuned. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 6.0 |
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### Generate hyperparameters |
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The following hyperparameters were used during generation: |
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- dataset_name: ccdv/mediasum |
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- dataset_config_name: roberta_prepended |
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- eval_batch_size: 8 |
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- eval_samples: 10000 |
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- early_stopping: True |
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- ignore_pad_token_for_loss: True |
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- length_penalty: 2.0 |
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- max_length: 128 |
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- min_length: 3 |
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- num_beams: 5 |
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- no_repeat_ngram_size: None |
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- seed: 123 |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 2.1.0 |
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- Tokenizers 0.11.6 |
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