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README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- xsum
+metrics:
+- rouge
+model-index:
+- name: large
+  results:
+  - task:
+      name: Summarization
+      type: summarization
+    dataset:
+      name: xsum
+      type: xsum
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Rouge1
+      type: rouge
+      value: 39.7125
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# large
+
+This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the xsum dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Rouge1: 39.7125
+- Rouge2: 16.3418
+- Rougel: 31.7246
+- Rougelsum: 31.7214
+- Gen Len: 26.7397
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- num_epochs: 3.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.27.0.dev0
+- Pytorch 1.9.0+cu111
+- Datasets 2.5.1
+- Tokenizers 0.12.1

all_results.json

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+    "eval_loss": NaN,
+    "eval_rouge1": 39.7125,
+    "eval_rouge2": 16.3418,
+    "eval_rougeL": 31.7246,
+    "eval_rougeLsum": 31.7214,
+    "eval_runtime": 1507.6277,
+    "eval_samples": 11327,
+    "eval_samples_per_second": 7.513,
+    "eval_steps_per_second": 0.47,
+    "train_loss": 0.0,
+    "train_runtime": 56397.6668,
+    "train_samples": 204017,
+    "train_samples_per_second": 10.852,
+    "train_steps_per_second": 0.17
+}

config.json

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+  "_name_or_path": "google/flan-t5-large",
+  "architectures": [
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+  ],
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+  "d_kv": 64,
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+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 24,
+  "num_heads": 16,
+  "num_layers": 24,
+  "output_past": true,
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+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.27.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

eval_results.json

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+    "eval_gen_len": 26.739736911803654,
+    "eval_loss": NaN,
+    "eval_rouge1": 39.7125,
+    "eval_rouge2": 16.3418,
+    "eval_rougeL": 31.7246,
+    "eval_rougeLsum": 31.7214,
+    "eval_runtime": 1507.6277,
+    "eval_samples": 11327,
+    "eval_samples_per_second": 7.513,
+    "eval_steps_per_second": 0.47
+}

generation_config.json

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+  "transformers_version": "4.27.0.dev0"
+}

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