Instructions to use vania2911/9000samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vania2911/9000samples with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vania2911/9000samples") model = AutoModelForSeq2SeqLM.from_pretrained("vania2911/9000samples", 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: 9000samples
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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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# 9000samples
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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: 0.1413
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- Model Preparation Time: 0.0237
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- Bleu Msl: 91.1702
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- Bleu Asl: 0
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- Ter Msl: 5.4860
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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: 1e-05
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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 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 | 225 | 1.6134 | 0.0237 | 4.2664 | 37.3206 | 123.2008 | 66.8905 |
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| No log | 2.0 | 450 | 1.0476 | 0.0237 | 39.9699 | 78.0861 | 46.875 | 10.4070 |
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| 1.7524 | 3.0 | 675 | 0.6934 | 0.0237 | 56.6935 | 81.8456 | 31.1553 | 8.7285 |
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| 1.7524 | 4.0 | 900 | 0.5114 | 0.0237 | 61.6302 | 83.6277 | 26.0417 | 7.9312 |
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| 0.6148 | 5.0 | 1125 | 0.4408 | 0.0237 | 68.7337 | 85.4571 | 20.4545 | 7.3437 |
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| 0.6148 | 6.0 | 1350 | 0.3971 | 0.0237 | 68.8182 | 86.5750 | 20.0758 | 6.8821 |
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| 0.3289 | 7.0 | 1575 | 0.3596 | 0.0237 | 68.7621 | 88.0049 | 20.1705 | 6.1687 |
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| 0.3289 | 8.0 | 1800 | 0.3205 | 0.0237 | 72.2466 | 88.9222 | 17.2348 | 5.7910 |
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| 0.2228 | 9.0 | 2025 | 0.2840 | 0.0237 | 73.0662 | 89.4050 | 16.4773 | 5.5812 |
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| 0.2228 | 10.0 | 2250 | 0.2596 | 0.0237 | 74.5818 | 90.3036 | 15.9091 | 5.2035 |
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| 0.2228 | 11.0 | 2475 | 0.2465 | 0.0237 | 74.8058 | 89.9123 | 15.625 | 5.3714 |
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| 0.1439 | 12.0 | 2700 | 0.2316 | 0.0237 | 77.3348 | 89.9976 | 14.2992 | 5.2035 |
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| 0.1439 | 13.0 | 2925 | 0.2236 | 0.0237 | 75.9479 | 90.0635 | 14.2045 | 5.2035 |
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| 0.1078 | 14.0 | 3150 | 0.2204 | 0.0237 | 75.6599 | 90.4806 | 14.3939 | 5.1196 |
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| 0.1078 | 15.0 | 3375 | 0.2188 | 0.0237 | 75.8864 | 90.2107 | 14.7727 | 5.2455 |
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| 0.0862 | 16.0 | 3600 | 0.2111 | 0.0237 | 76.4512 | 90.2883 | 14.6780 | 5.2455 |
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| 0.0862 | 17.0 | 3825 | 0.2049 | 0.0237 | 76.5906 | 90.5898 | 14.3939 | 5.0776 |
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| 0.0744 | 18.0 | 4050 | 0.2047 | 0.0237 | 76.1354 | 90.1256 | 14.6780 | 5.2875 |
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| 0.0744 | 19.0 | 4275 | 0.2011 | 0.0237 | 76.4851 | 90.9593 | 14.6780 | 4.9098 |
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| 0.0648 | 20.0 | 4500 | 0.2002 | 0.0237 | 77.1588 | 91.0477 | 14.0152 | 4.8258 |
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| 0.0648 | 21.0 | 4725 | 0.2003 | 0.0237 | 77.6252 | 90.5716 | 13.8258 | 5.1196 |
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| 0.0648 | 22.0 | 4950 | 0.1980 | 0.0237 | 78.6152 | 90.9392 | 13.4470 | 4.9517 |
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| 0.0575 | 23.0 | 5175 | 0.1966 | 0.0237 | 78.5031 | 90.7533 | 13.5417 | 4.9937 |
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| 0.0575 | 24.0 | 5400 | 0.1962 | 0.0237 | 78.5453 | 90.8408 | 13.4470 | 5.0357 |
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| 0.0531 | 25.0 | 5625 | 0.1952 | 0.0237 | 78.3506 | 90.7533 | 13.3523 | 4.9937 |
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| 0.0531 | 26.0 | 5850 | 0.1938 | 0.0237 | 79.0856 | 90.9085 | 12.9735 | 4.9517 |
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| 0.0492 | 27.0 | 6075 | 0.1930 | 0.0237 | 79.0743 | 90.7829 | 12.9735 | 4.9517 |
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| 0.0492 | 28.0 | 6300 | 0.1928 | 0.0237 | 79.8281 | 90.8176 | 12.7841 | 4.9937 |
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| 0.0465 | 29.0 | 6525 | 0.1929 | 0.0237 | 79.4814 | 90.7628 | 12.8788 | 4.9937 |
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| 0.0465 | 30.0 | 6750 | 0.1929 | 0.0237 | 79.3085 | 90.7628 | 12.9735 | 4.9937 |
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### Framework versions
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- Transformers 4.46.2
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- Pytorch 2.5.1+cu121
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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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": "4.46.2"
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}
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