Training complete!

README.md

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+---
+license: mit
+base_model: facebook/bart-large-xsum
+tags:
+- generated_from_trainer
+datasets:
+- samsum
+metrics:
+- rouge
+model-index:
+- name: bart-samsum
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: samsum
+      type: samsum
+      config: samsum
+      split: validation
+      args: samsum
+    metrics:
+    - name: Rouge1
+      type: rouge
+      value: 0.547
+---
+
+<!-- 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. -->
+
+# bart-samsum
+
+This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3852
+- Rouge1: 0.547
+- Rouge2: 0.2837
+- Rougel: 0.4462
+- Rougelsum: 0.4454
+- Gen Len: 29.72
+
+## 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: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
+| 1.5201        | 0.27  | 500  | 1.4589          | 0.5276 | 0.2694 | 0.4246 | 0.424     | 33.5067 |
+| 1.3757        | 0.54  | 1000 | 1.5105          | 0.506  | 0.2566 | 0.415  | 0.4146    | 29.76   |
+| 1.3496        | 0.81  | 1500 | 1.4039          | 0.5365 | 0.2759 | 0.4233 | 0.4221    | 29.8    |
+| 1.094         | 1.09  | 2000 | 1.4119          | 0.5407 | 0.2827 | 0.4293 | 0.4288    | 29.84   |
+| 1.1488        | 1.36  | 2500 | 1.3680          | 0.5275 | 0.2637 | 0.423  | 0.4224    | 26.92   |
+| 1.1222        | 1.63  | 3000 | 1.2875          | 0.5369 | 0.2844 | 0.4473 | 0.4463    | 29.2267 |
+| 1.1092        | 1.9   | 3500 | 1.3968          | 0.533  | 0.2818 | 0.4354 | 0.4363    | 30.0667 |
+| 0.8509        | 2.17  | 4000 | 1.3682          | 0.5306 | 0.2874 | 0.4327 | 0.4331    | 29.1467 |
+| 0.9565        | 2.44  | 4500 | 1.3450          | 0.5466 | 0.2782 | 0.4419 | 0.4409    | 29.2133 |
+| 0.8496        | 2.72  | 5000 | 1.3768          | 0.5366 | 0.2807 | 0.4359 | 0.4351    | 30.7733 |
+| 0.8397        | 2.99  | 5500 | 1.3852          | 0.547  | 0.2837 | 0.4462 | 0.4454    | 29.72   |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 0,
+  "decoder_start_token_id": 2,
+  "early_stopping": true,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "max_length": 62,
+  "min_length": 11,
+  "no_repeat_ngram_size": 3,
+  "num_beams": 6,
+  "pad_token_id": 1,
+  "transformers_version": "4.33.1"
+}

pytorch_model.bin

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