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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - xlsum
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+ model-index:
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+ - name: AraBART-summ
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # AraBART-summ
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+
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+ This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBART) on the xlsum dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.339382
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 2.784000 | 1.0 | 9380 | 2.382031 |
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+ | 2.495400 | 2.0 | 18760 | 2.341753 |
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+ | 2.222300 | 3.0 | 28140 | 2.339382 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.25.1
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+ - Pytorch 1.13.0+cu116
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+ - Datasets 2.7.1
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+ - Tokenizers 0.13.2
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+ {
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+ "_name_or_path": "moussaKam/AraBART",
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+ "activation_dropout": 0.1,
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+ "activation_function": "gelu",
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+ "add_final_layer_norm": true,
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+ "architectures": [
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+ "MBartForConditionalGeneration"
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+ ],
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+ "early_stopping": true,
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+ "encoder_layerdrop": 0.0,
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+ "encoder_layers": 6,
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+ "model_type": "mbart",
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+ "no_repeat_ngram_size": 3,
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+ "normalize_before": true,
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+ "num_hidden_layers": 6,
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+ "tokenizer_class": "BarthezTokenizer",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.25.1",
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+ "use_cache": true,
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+ "vocab_size": 50002
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+ }
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