Initial commit

README.md

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+This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
+
+# Setup
+```bash
+git clone https://github.com/vuiseng9/transformers
+cd transformers
+git checkout pegasus-v4p13 && git reset --hard 41eeb07
+# installation, set summarization dependency
+# . . .
+```
+
+# Train
+```bash
+#!/usr/bin/env bash
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+
+NEPOCH=10
+RUNID=pegasus-billsum-${NEPOCH}eph-run1
+OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus/${RUNID}
+mkdir -p $OUTDIR
+
+nohup python run_summarization.py \
+    --model_name_or_path google/pegasus-large \
+    --dataset_name billsum \
+    --do_train \
+    --adafactor \
+    --learning_rate 2e-4 \
+    --label_smoothing_factor 0.1 \
+    --num_train_epochs $NEPOCH \
+    --per_device_train_batch_size 2 \
+    --do_eval \
+    --per_device_eval_batch_size 2 \
+    --num_beams 8 \
+    --max_source_length 1024 \
+    --max_target_length 256 \
+    --evaluation_strategy steps \
+    --eval_steps 1000 \
+    --save_strategy steps \
+    --save_steps 2000 \
+    --logging_steps 1 \
+    --overwrite_output_dir \
+    --run_name $RUNID \
+    --output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
+```
+
+# Eval
+```bash
+#!/usr/bin/env bash
+
+export CUDA_VISIBLE_DEVICES=3
+
+DT=$(date +%F_%H-%M)
+RUNID=pegasus-billsum-${DT}
+OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus-test/${RUNID}
+mkdir -p $OUTDIR
+
+nohup python run_summarization.py \
+    --model_name_or_path vuiseng9/pegasus-billsum \
+    --dataset_name billsum \
+    --max_source_length 1024 \
+    --max_target_length 256 \
+    --do_predict \
+    --per_device_eval_batch_size 8 \
+    --predict_with_generate \
+    --num_beams 8 \
+    --overwrite_output_dir \
+    --run_name $RUNID \
+    --output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
+```
+
+Although fine-tuning is carried out for 10 epochs, this model is the checkpoint (@12000 steps, 6.6epoch, 210mins) with lowest eval loss during training. Test/predict with this checkpoint should give results below.
+
+```
+***** predict metrics *****
+  predict_gen_len            =   179.7363
+  predict_loss               =     1.2452
+  predict_rouge1             =    56.8657
+  predict_rouge2             =    38.6531
+  predict_rougeL             =    44.8399
+  predict_rougeLsum          =    51.6266
+  predict_runtime            = 1:19:28.20
+  predict_samples            =       3269
+  predict_samples_per_second =      0.686
+  predict_steps_per_second   =      0.086
+```

config.json

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+{
+  "_name_or_path": "google/pegasus-large",
+  "activation_dropout": 0.1,
+  "activation_function": "relu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": true,
+  "architectures": [
+    "PegasusForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "classif_dropout": 0.0,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 16,
+  "decoder_start_token_id": 0,
+  "dropout": 0.1,
+  "encoder_attention_heads": 16,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 16,
+  "eos_token_id": 1,
+  "extra_pos_embeddings": 1,
+  "force_bos_token_to_be_generated": false,
+  "forced_eos_token_id": 1,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "length_penalty": 0.8,
+  "max_length": 256,
+  "max_position_embeddings": 1024,
+  "model_type": "pegasus",
+  "normalize_before": true,
+  "normalize_embedding": false,
+  "num_beams": 8,
+  "num_hidden_layers": 16,
+  "pad_token_id": 0,
+  "scale_embedding": true,
+  "static_position_embeddings": true,
+  "task_specific_params": {
+    "summarization_aeslc": {
+      "length_penalty": 0.6,
+      "max_length": 32,
+      "max_position_embeddings": 512
+    },
+    "summarization_arxiv": {
+      "length_penalty": 0.8,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_big_patent": {
+      "length_penalty": 0.7,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_billsum": {
+      "length_penalty": 0.6,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_cnn_dailymail": {
+      "length_penalty": 0.8,
+      "max_length": 128,
+      "max_position_embeddings": 1024
+    },
+    "summarization_gigaword": {
+      "length_penalty": 0.6,
+      "max_length": 32,
+      "max_position_embeddings": 128
+    },
+    "summarization_large": {
+      "length_penalty": 0.8,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_multi_news": {
+      "length_penalty": 0.8,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_newsroom": {
+      "length_penalty": 0.8,
+      "max_length": 128,
+      "max_position_embeddings": 512
+    },
+    "summarization_pubmed": {
+      "length_penalty": 0.8,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_reddit_tifu": {
+      "length_penalty": 0.6,
+      "max_length": 128,
+      "max_position_embeddings": 512
+    },
+    "summarization_wikihow": {
+      "length_penalty": 0.6,
+      "max_length": 256,
+      "max_position_embeddings": 512
+    },
+    "summarization_xsum": {
+      "length_penalty": 0.8,
+      "max_length": 64,
+      "max_position_embeddings": 512
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.13.0",
+  "use_cache": true,
+  "vocab_size": 96103
+}

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special_tokens_map.json

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tokenizer_config.json

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