Training complete !

README.md

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+---
+base_model: UBC-NLP/AraT5v2-base-1024
+tags:
+- summarization
+- Arat5v2
+- abstractive summarization
+- ar
+- xlsum
+- generated_from_trainer
+datasets:
+- xlsum
+model-index:
+- name: AraT5v2-base-1024-finetune-ar-xlsum
+  results: []
+---
+
+<!-- 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. -->
+
+# AraT5v2-base-1024-finetune-ar-xlsum
+
+This model is a fine-tuned version of [UBC-NLP/AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) on the xlsum dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.7983
+- Rouge-1: 33.4
+- Rouge-2: 16.14
+- Rouge-l: 29.31
+- Gen Len: 18.63
+- Bertscore: 74.57
+
+## 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: 0.0005
+- train_batch_size: 12
+- eval_batch_size: 12
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 192
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 250
+- num_epochs: 10
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
+| 6.1614        | 1.0   | 195  | 3.9898          | 28.51   | 12.02   | 24.64   | 18.87   | 72.64     |
+| 4.5342        | 2.0   | 390  | 3.9048          | 29.5    | 13.01   | 25.85   | 18.53   | 73.34     |
+| 4.2029        | 3.0   | 585  | 3.8162          | 31.64   | 14.33   | 27.54   | 18.57   | 73.88     |
+| 3.9689        | 4.0   | 781  | 3.7949          | 31.87   | 14.56   | 27.9    | 18.55   | 74.04     |
+| 3.8278        | 5.0   | 976  | 3.7702          | 31.85   | 14.58   | 27.74   | 18.74   | 73.96     |
+| 3.6921        | 6.0   | 1171 | 3.7775          | 32.27   | 14.95   | 28.16   | 18.78   | 74.23     |
+| 3.5632        | 7.0   | 1367 | 3.7751          | 32.54   | 15.04   | 28.4    | 18.72   | 74.36     |
+| 3.493         | 8.0   | 1562 | 3.7815          | 32.35   | 14.95   | 28.24   | 18.71   | 74.32     |
+| 3.4189        | 9.0   | 1757 | 3.7908          | 32.39   | 14.99   | 28.32   | 18.73   | 74.32     |
+| 3.3492        | 9.98  | 1950 | 3.7983          | 32.6    | 15.19   | 28.5    | 18.72   | 74.35     |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

generation_config.json

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+{
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.33.1"
+}