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esp-to-lsm-model-split

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update model card README.md

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@@ -17,10 +17,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co/Helsinki-NLP/opus-mt-es-es) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5690
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- - Bleu: 83.5807
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- - Rouge: {'rouge1': 0.9265753592812418, 'rouge2': 0.8656694324194325, 'rougeL': 0.9238164847135437, 'rougeLsum': 0.9238003663003664}
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- - Ter Score: 10.0090
 
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  ## Model description
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@@ -50,33 +51,33 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Ter Score |
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- |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------------------------------------------------------------------------------------------------------------------------:|:---------:|
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- | 0.997 | 1.0 | 75 | 0.7578 | 74.2121 | {'rouge1': 0.8930136077372922, 'rouge2': 0.8132252290193469, 'rougeL': 0.8868313923778324, 'rougeLsum': 0.8866414102466736} | 16.3210 |
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- | 0.4353 | 2.0 | 150 | 0.5659 | 50.7443 | {'rouge1': 0.9142509364274071, 'rouge2': 0.83197113997114, 'rougeL': 0.9055773276287983, 'rougeLsum': 0.9062817797670736} | 12.8043 |
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- | 0.2602 | 3.0 | 225 | 0.5444 | 72.0122 | {'rouge1': 0.9183889862860454, 'rouge2': 0.8433486969005839, 'rougeL': 0.9132635343958876, 'rougeLsum': 0.913651539908893} | 15.9603 |
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- | 0.2316 | 4.0 | 300 | 0.5503 | 50.9502 | {'rouge1': 0.9147289323852568, 'rouge2': 0.8403040453698347, 'rougeL': 0.9084138578656601, 'rougeLsum': 0.9084760810455303} | 13.0748 |
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- | 0.1203 | 5.0 | 375 | 0.5211 | 58.7666 | {'rouge1': 0.9278827629661555, 'rouge2': 0.8655444837508406, 'rougeL': 0.922415336132431, 'rougeLsum': 0.9224576705147474} | 29.6664 |
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- | 0.1216 | 6.0 | 450 | 0.5491 | 81.6262 | {'rouge1': 0.9206053007450066, 'rouge2': 0.8534470899470898, 'rougeL': 0.9171148252618841, 'rougeLsum': 0.9168772093919156} | 11.0911 |
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- | 0.0754 | 7.0 | 525 | 0.5095 | 83.4616 | {'rouge1': 0.9305456776339132, 'rouge2': 0.8778395262145262, 'rougeL': 0.9280110015257075, 'rougeLsum': 0.9281936805025043} | 10.0090 |
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- | 0.0848 | 8.0 | 600 | 0.5538 | 81.8681 | {'rouge1': 0.9248025063172123, 'rouge2': 0.8648207579457581, 'rougeL': 0.9219360612154733, 'rougeLsum': 0.921904937654938} | 10.4599 |
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- | 0.0504 | 9.0 | 675 | 0.5390 | 80.8118 | {'rouge1': 0.9217618560633272, 'rouge2': 0.8611767121767122, 'rougeL': 0.9194047336106163, 'rougeLsum': 0.9196579346579348} | 12.3535 |
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- | 0.0367 | 10.0 | 750 | 0.5632 | 82.2896 | {'rouge1': 0.9241220549602904, 'rouge2': 0.8623059255559258, 'rougeL': 0.921636625901332, 'rougeLsum': 0.9214262796027506} | 10.8206 |
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- | 0.0386 | 11.0 | 825 | 0.5325 | 83.7819 | {'rouge1': 0.9264862667289138, 'rouge2': 0.8665701058201061, 'rougeL': 0.924734155278273, 'rougeLsum': 0.9247572857425799} | 10.2795 |
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- | 0.0377 | 12.0 | 900 | 0.5540 | 83.6969 | {'rouge1': 0.9270570480717542, 'rouge2': 0.8649807692307694, 'rougeL': 0.9248777127012422, 'rougeLsum': 0.9247459680842035} | 10.0090 |
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- | 0.0244 | 13.0 | 975 | 0.5462 | 83.4825 | {'rouge1': 0.9284353783471431, 'rouge2': 0.8673707311207314, 'rougeL': 0.9249773075508372, 'rougeLsum': 0.924672456084221} | 9.9188 |
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- | 0.0237 | 14.0 | 1050 | 0.5468 | 83.3820 | {'rouge1': 0.9267599383187618, 'rouge2': 0.8631084656084658, 'rougeL': 0.9244043657867187, 'rougeLsum': 0.9240160215601393} | 10.0992 |
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- | 0.0173 | 15.0 | 1125 | 0.5604 | 82.7936 | {'rouge1': 0.9260569985569987, 'rouge2': 0.8652394179894183, 'rougeL': 0.923313301078007, 'rougeLsum': 0.9233026695526696} | 10.1894 |
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- | 0.0193 | 16.0 | 1200 | 0.5689 | 85.1028 | {'rouge1': 0.9298936104744928, 'rouge2': 0.874325396825397, 'rougeL': 0.9280833015024192, 'rougeLsum': 0.9275536633845459} | 9.6483 |
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- | 0.0184 | 17.0 | 1275 | 0.5695 | 83.7781 | {'rouge1': 0.9266896553881849, 'rouge2': 0.8650757020757022, 'rougeL': 0.924688972247796, 'rougeLsum': 0.9245597692068284} | 10.2795 |
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- | 0.0142 | 18.0 | 1350 | 0.5655 | 83.6649 | {'rouge1': 0.925748337718926, 'rouge2': 0.8645625300625305, 'rougeL': 0.9233836055012529, 'rougeLsum': 0.9233253614577146} | 10.0090 |
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- | 0.0131 | 19.0 | 1425 | 0.5701 | 83.6843 | {'rouge1': 0.9268515199397553, 'rouge2': 0.8660478595478597, 'rougeL': 0.9242069248833956, 'rougeLsum': 0.9242629070276129} | 9.9188 |
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- | 0.0122 | 20.0 | 1500 | 0.5690 | 83.5807 | {'rouge1': 0.9265753592812418, 'rouge2': 0.8656694324194325, 'rougeL': 0.9238164847135437, 'rougeLsum': 0.9238003663003664} | 10.0090 |
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  ### Framework versions
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  - Transformers 4.26.1
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- - Pytorch 2.4.1+cu121
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  - Datasets 3.0.2
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  - Tokenizers 0.13.3
 
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  This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co/Helsinki-NLP/opus-mt-es-es) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4626
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+ - Bleu: 84.9075
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+ - Ter: 9.0570
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+ - Rouge1: 0.9336
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+ - Rouge2: 0.8778
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Bleu | Ter | Rouge1 | Rouge2 |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:------:|
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+ | 1.0312 | 1.0 | 75 | 0.7455 | 44.5226 | 28.1979 | 0.8796 | 0.7984 |
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+ | 0.4186 | 2.0 | 150 | 0.5286 | 37.1434 | 14.0056 | 0.9122 | 0.8470 |
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+ | 0.2945 | 3.0 | 225 | 0.4705 | 79.5322 | 11.1111 | 0.9267 | 0.8615 |
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+ | 0.1752 | 4.0 | 300 | 0.4554 | 43.4189 | 34.3604 | 0.9248 | 0.8515 |
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+ | 0.1483 | 5.0 | 375 | 0.4950 | 79.9496 | 10.2708 | 0.9269 | 0.8584 |
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+ | 0.1049 | 6.0 | 450 | 0.5085 | 83.0106 | 9.9907 | 0.9317 | 0.8684 |
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+ | 0.0808 | 7.0 | 525 | 0.4691 | 81.5651 | 10.6443 | 0.9257 | 0.8589 |
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+ | 0.0723 | 8.0 | 600 | 0.4494 | 81.7655 | 11.6713 | 0.9226 | 0.8641 |
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+ | 0.0652 | 9.0 | 675 | 0.4359 | 84.3479 | 10.1774 | 0.9229 | 0.8663 |
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+ | 0.0469 | 10.0 | 750 | 0.4403 | 82.7091 | 10.2708 | 0.9261 | 0.8664 |
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+ | 0.043 | 11.0 | 825 | 0.4763 | 82.7838 | 11.0177 | 0.9227 | 0.8598 |
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+ | 0.0278 | 12.0 | 900 | 0.4611 | 84.5668 | 9.8039 | 0.9294 | 0.8715 |
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+ | 0.0258 | 13.0 | 975 | 0.4610 | 84.7647 | 9.3371 | 0.9317 | 0.8729 |
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+ | 0.0258 | 14.0 | 1050 | 0.4428 | 85.7637 | 8.8702 | 0.9354 | 0.8810 |
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+ | 0.0216 | 15.0 | 1125 | 0.4507 | 76.7724 | 15.4995 | 0.9292 | 0.8724 |
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+ | 0.0132 | 16.0 | 1200 | 0.4550 | 85.1505 | 9.2437 | 0.9317 | 0.8742 |
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+ | 0.0129 | 17.0 | 1275 | 0.4575 | 85.2873 | 8.8702 | 0.9345 | 0.8798 |
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+ | 0.0109 | 18.0 | 1350 | 0.4600 | 84.8355 | 9.2437 | 0.9324 | 0.8768 |
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+ | 0.0109 | 19.0 | 1425 | 0.4617 | 84.9332 | 9.1503 | 0.9336 | 0.8782 |
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+ | 0.014 | 20.0 | 1500 | 0.4626 | 84.9075 | 9.0570 | 0.9336 | 0.8778 |
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  ### Framework versions
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  - Transformers 4.26.1
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+ - Pytorch 2.5.0+cu121
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  - Datasets 3.0.2
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  - Tokenizers 0.13.3