Instructions to use vania2911/11_6kmslsamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vania2911/11_6kmslsamples with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vania2911/11_6kmslsamples") model = AutoModelForSeq2SeqLM.from_pretrained("vania2911/11_6kmslsamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: Helsinki-NLP/opus-mt-es-es
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tags:
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- generated_from_trainer
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model-index:
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- name: 11_6kmslsamples
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results: []
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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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# 11_6kmslsamples
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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 the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4832
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- Model Preparation Time: 0.0033
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- Bleu Msl: 76.2719
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- Bleu Asl: 0
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- Ter Msl: 12.4158
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- Ter Asl: 100
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 30
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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 | Model Preparation Time | Bleu Msl | Bleu Asl | Ter Msl | Ter Asl |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|:--------:|:-------:|:-------:|
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| No log | 1.0 | 292 | 0.2395 | 0.0033 | 11.4499 | 88.9288 | 91.0976 | 5.6389 |
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| 0.4467 | 2.0 | 584 | 0.1630 | 0.0033 | 72.4836 | 90.3437 | 18.1588 | 5.1480 |
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| 0.4467 | 3.0 | 876 | 0.1537 | 0.0033 | 69.4982 | 89.2613 | 22.9135 | 5.8073 |
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| 0.0901 | 4.0 | 1168 | 0.1488 | 0.0033 | 67.2310 | 75.4270 | 22.6606 | 13.0313 |
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| 0.0901 | 5.0 | 1460 | 0.1438 | 0.0033 | 72.7268 | 91.0640 | 18.4117 | 4.4887 |
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| 0.0479 | 6.0 | 1752 | 0.1443 | 0.0033 | 74.3024 | 84.6282 | 16.6920 | 5.5828 |
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| 0.0306 | 7.0 | 2044 | 0.1468 | 0.0033 | 75.8427 | 88.1935 | 15.3768 | 6.1860 |
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| 0.0306 | 8.0 | 2336 | 0.1523 | 0.0033 | 72.8626 | 90.7097 | 18.8670 | 5.0077 |
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| 0.022 | 9.0 | 2628 | 0.1419 | 0.0033 | 71.9710 | 89.9411 | 15.8827 | 5.4566 |
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| 0.022 | 10.0 | 2920 | 0.1481 | 0.0033 | 72.5502 | 91.3971 | 18.7152 | 4.4747 |
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| 0.0217 | 11.0 | 3212 | 0.1414 | 0.0033 | 71.0317 | 91.3192 | 19.5751 | 4.8815 |
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| 0.0156 | 12.0 | 3504 | 0.1435 | 0.0033 | 76.9787 | 83.9106 | 14.3652 | 11.2498 |
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| 0.0156 | 13.0 | 3796 | 0.1513 | 0.0033 | 75.6635 | 83.1054 | 15.7815 | 11.8390 |
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| 0.01 | 14.0 | 4088 | 0.1491 | 0.0033 | 74.8931 | 91.4009 | 16.5402 | 5.0077 |
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| 0.01 | 15.0 | 4380 | 0.1502 | 0.0033 | 75.7380 | 90.2261 | 17.0460 | 5.7091 |
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| 0.0088 | 16.0 | 4672 | 0.1474 | 0.0033 | 76.2381 | 92.3039 | 16.4896 | 4.1661 |
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| 0.0088 | 17.0 | 4964 | 0.1493 | 0.0033 | 77.6706 | 92.2754 | 15.3768 | 4.4045 |
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| 0.0076 | 18.0 | 5256 | 0.1519 | 0.0033 | 73.3253 | 89.7627 | 17.9565 | 5.9195 |
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| 0.0057 | 19.0 | 5548 | 0.1473 | 0.0033 | 72.4356 | 91.0144 | 19.2210 | 4.7833 |
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| 0.0057 | 20.0 | 5840 | 0.1520 | 0.0033 | 75.0746 | 90.0677 | 17.0460 | 5.8493 |
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| 0.005 | 21.0 | 6132 | 0.1509 | 0.0033 | 75.7113 | 82.0543 | 16.1861 | 13.2277 |
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| 0.005 | 22.0 | 6424 | 0.1487 | 0.0033 | 76.6081 | 82.3037 | 15.4780 | 12.5684 |
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| 0.0041 | 23.0 | 6716 | 0.1500 | 0.0033 | 76.6765 | 81.3603 | 16.1356 | 13.7186 |
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| 0.004 | 24.0 | 7008 | 0.1497 | 0.0033 | 78.6303 | 82.3583 | 14.6181 | 12.8349 |
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| 0.004 | 25.0 | 7300 | 0.1509 | 0.0033 | 77.9463 | 82.7968 | 15.1745 | 12.1896 |
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| 0.0028 | 26.0 | 7592 | 0.1500 | 0.0033 | 77.0621 | 82.5839 | 15.3263 | 12.6806 |
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| 0.0028 | 27.0 | 7884 | 0.1517 | 0.0033 | 76.1682 | 82.2440 | 16.1356 | 13.1716 |
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| 0.0025 | 28.0 | 8176 | 0.1518 | 0.0033 | 75.5286 | 82.0875 | 16.0344 | 13.3118 |
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| 0.0025 | 29.0 | 8468 | 0.1518 | 0.0033 | 76.4287 | 82.3060 | 15.4780 | 13.0874 |
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| 0.0022 | 30.0 | 8760 | 0.1519 | 0.0033 | 76.3073 | 82.3498 | 15.5286 | 13.0453 |
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### Framework versions
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- Transformers 4.49.0
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- Pytorch 2.5.1+cu124
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- Datasets 3.3.2
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- Tokenizers 0.21.0
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generation_config.json
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{
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"bad_words_ids": [
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[
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33252
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]
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],
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"bos_token_id": 0,
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"decoder_start_token_id": 33252,
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"eos_token_id": 0,
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"forced_eos_token_id": 0,
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"max_length": 512,
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"num_beams": 4,
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"pad_token_id": 33252,
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"renormalize_logits": true,
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"transformers_version": "4.49.0"
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}
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runs/Mar08_13-03-45_5ed6bf6b2cfa/events.out.tfevents.1741439032.5ed6bf6b2cfa.19073.0
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size
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