Upload 6 files
Browse files- README.md +74 -0
- config.json +49 -0
- generation_config.json +13 -0
- special_tokens_map.json +1 -0
- spiece.model +3 -0
- tokenizer_config.json +1 -0
README.md
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---
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language: en
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tags:
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- summarization
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---
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### Pegasus Models
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See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
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Original TF 1 code [here](https://github.com/google-research/pegasus)
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Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
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Maintained by: [@sshleifer](https://twitter.com/sam_shleifer)
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Task: Summarization
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The following is copied from the authors' README.
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# Mixed & Stochastic Checkpoints
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We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table.
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| dataset | C4 | HugeNews | Mixed & Stochastic|
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| ---- | ---- | ---- | ----|
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| xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64|
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| cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30|
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| newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18|
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| multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95|
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| gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76|
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| wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *|
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| reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94|
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| big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *|
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| arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67|
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| pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25|
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| aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51|
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| billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59|
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The "Mixed & Stochastic" model has the following changes:
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- trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
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- trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
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- the model uniformly sample a gap sentence ratio between 15% and 45%.
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- importance sentences are sampled using a 20% uniform noise to importance scores.
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- the sentencepiece tokenizer is updated to be able to encode newline character.
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(*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data:
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- wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information.
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- we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS.
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The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper):
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trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
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trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
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the model uniformly sample a gap sentence ratio between 15% and 45%.
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importance sentences are sampled using a 20% uniform noise to importance scores.
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the sentencepiece tokenizer is updated to be able to encode newline character.
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Citation
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```
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@misc{zhang2019pegasus,
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title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization},
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author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu},
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year={2019},
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eprint={1912.08777},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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config.json
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{
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"activation_dropout": 0.1,
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"activation_function": "relu",
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"add_bias_logits": false,
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"add_final_layer_norm": true,
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"architectures": [
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"PegasusForConditionalGeneration"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 0,
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"classif_dropout": 0.0,
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"d_model": 1024,
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"decoder_attention_heads": 16,
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"decoder_ffn_dim": 4096,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 16,
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"dropout": 0.1,
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"encoder_attention_heads": 16,
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"encoder_ffn_dim": 4096,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 16,
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"eos_token_id": 1,
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"extra_pos_embeddings": 1,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2"
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},
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_2": 2
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},
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"length_penalty": 0.8,
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"max_length": 128,
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"max_position_embeddings": 1024,
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"min_length": 32,
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"model_type": "pegasus",
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"normalize_before": true,
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"normalize_embedding": false,
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"num_beams": 8,
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"num_hidden_layers": 16,
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"pad_token_id": 0,
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"scale_embedding": true,
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"static_position_embeddings": true,
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"vocab_size": 96103
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"forced_eos_token_id": 1,
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"length_penalty": 0.8,
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"max_length": 128,
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"min_length": 32,
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"num_beams": 8,
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"pad_token_id": 0,
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"transformers_version": "4.27.0.dev0"
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}
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special_tokens_map.json
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{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
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spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:0015189ef36359283fec8b93cf6d9ce51bca37eb1101defc68a53b394913b96c
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size 1912529
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tokenizer_config.json
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{"model_max_length": 1024, "special_tokens_map_file": null, "full_tokenizer_file": null}
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