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bart_samsum

Safetensors
bart
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  1. README.md +13 -12
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. training_args.bin +1 -1
README.md CHANGED
@@ -17,12 +17,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4704
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- - Rouge1: 54.8232
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- - Rouge2: 30.1114
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- - Rougel: 45.2666
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- - Rougelsum: 50.7533
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- - Gen Len: 30.3399
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  ## Model description
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@@ -48,23 +48,24 @@ The following hyperparameters were used during training:
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 8
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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: 4
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  - mixed_precision_training: Native AMP
 
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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- | 1.3807 | 0.9997 | 1841 | 1.5203 | 52.4158 | 27.5034 | 42.8274 | 48.0361 | 31.4664 |
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- | 1.077 | 2.0 | 3683 | 1.5038 | 53.5277 | 28.5946 | 44.2315 | 49.5696 | 30.768 |
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- | 0.831 | 2.9997 | 5524 | 1.5362 | 52.9008 | 27.7041 | 43.5637 | 48.3921 | 29.9243 |
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- | 0.6919 | 3.9989 | 7364 | 1.6272 | 52.8716 | 27.9183 | 43.8019 | 48.6547 | 30.2002 |
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  ### Framework versions
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  - Transformers 4.42.4
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- - Pytorch 2.3.1+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
 
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  This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.6994
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+ - Rouge1: 54.5529
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+ - Rouge2: 30.0179
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+ - Rougel: 45.3837
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+ - Rougelsum: 50.4176
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+ - Gen Len: 28.967
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  ## Model description
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 8
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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  - num_epochs: 4
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  - mixed_precision_training: Native AMP
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+ - label_smoothing_factor: 0.1
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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+ | 2.7327 | 0.9997 | 1841 | 2.7677 | 52.2923 | 27.6237 | 43.1558 | 48.08 | 30.4005 |
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+ | 2.4597 | 2.0 | 3683 | 2.7286 | 53.4085 | 28.7235 | 44.5737 | 49.3042 | 29.3004 |
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+ | 2.2042 | 2.9997 | 5524 | 2.7436 | 53.6036 | 28.857 | 44.7337 | 49.2789 | 28.4188 |
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+ | 2.1096 | 3.9989 | 7364 | 2.7886 | 53.0547 | 28.3597 | 44.0648 | 48.804 | 29.5165 |
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  ### Framework versions
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  - Transformers 4.42.4
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+ - Pytorch 2.4.0+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
config.json CHANGED
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  "decoder_layerdrop": 0.0,
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  "decoder_layers": 12,
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  "decoder_start_token_id": 2,
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- "dropout": 0.1,
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  "early_stopping": true,
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  "encoder_attention_heads": 16,
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  "encoder_ffn_dim": 4096,
 
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  "decoder_layerdrop": 0.0,
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  "decoder_layers": 12,
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  "decoder_start_token_id": 2,
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+ "dropout": 0.3,
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  "early_stopping": true,
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  "encoder_attention_heads": 16,
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  "encoder_ffn_dim": 4096,
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