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--- |
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library_name: transformers |
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base_model: |
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- facebook/nllb-200-3.3B |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: nllb-200-finetunning-5e-5-32batch-9310steps |
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results: [] |
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license: mit |
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datasets: |
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- Youseff1987/multilingual_translation_sft |
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- Youseff1987/multilingual_translation_sft_nllb200_tokenized |
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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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# nllb-200-finetunning-5e-5-32batch-9310steps |
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This model is a fine-tuned version of [facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9026 |
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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: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 3407 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | |
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|:-------------:|:------:|:----:|:---------------:|:----------------------:| |
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| 1.3132 | 0.0268 | 50 | 1.0922 | 0.0202 | |
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| 1.3205 | 0.0537 | 100 | 1.0550 | |
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| 1.1442 | 0.0805 | 150 | 1.0364 | |
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| 0.9155 | 0.1074 | 200 | 1.0216 | |
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| 1.2449 | 0.1342 | 250 | 1.0114 | |
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| 0.7856 | 0.1611 | 300 | 1.0021 | |
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| 1.1645 | 0.1879 | 350 | 0.9942 | |
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| 1.168 | 0.2148 | 400 | 0.9872 | |
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| 1.0991 | 0.2416 | 450 | 0.9800 | |
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| 0.9243 | 0.2684 | 500 | 0.9751 | |
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| 1.3314 | 0.2953 | 550 | 0.9689 | |
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| 1.0203 | 0.3221 | 600 | 0.9650 | |
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| 0.9849 | 0.3490 | 650 | 0.9630 | |
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| 1.1588 | 0.3758 | 700 | 0.9581 | |
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| 0.8723 | 0.4027 | 750 | 0.9560 | |
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| 0.8312 | 0.4295 | 800 | 0.9528 | |
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| 0.7363 | 0.4564 | 850 | 0.9519 | |
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| 0.9292 | 0.4832 | 900 | 0.9494 | |
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| 0.9326 | 0.5100 | 950 | 0.9449 | |
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| 0.8994 | 0.5369 | 1000 | 0.9424 | |
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| 1.0149 | 0.5637 | 1050 | 0.9401 | |
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| 0.9467 | 0.5906 | 1100 | 0.9381 | |
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| 1.0278 | 0.6174 | 1150 | 0.9375 | |
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| 0.9975 | 0.6443 | 1200 | 0.9368 | |
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| 1.0143 | 0.6711 | 1250 | 0.9342 | |
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| 1.1696 | 0.6980 | 1300 | 0.9328 | |
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| 0.8223 | 0.7248 | 1350 | 0.9316 | |
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| 0.9384 | 0.7517 | 1400 | 0.9312 | |
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| 0.9466 | 0.7785 | 1450 | 0.9296 | |
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| 0.8791 | 0.8053 | 1500 | 0.9287 | |
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| 1.1393 | 0.8322 | 1550 | 0.9287 | |
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| 0.9288 | 0.8590 | 1600 | 0.9277 | |
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| 0.9249 | 0.8859 | 1650 | 0.9260 | |
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| 1.0053 | 0.9127 | 1700 | 0.9246 | |
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| 0.7675 | 0.9396 | 1750 | 0.9235 | |
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| 1.0448 | 0.9664 | 1800 | 0.9234 | |
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| 0.8382 | 0.9933 | 1850 | 0.9214 | |
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| 0.7418 | 1.0204 | 1900 | 0.9221 | |
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| 0.8152 | 1.0472 | 1950 | 0.9234 | |
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| 0.888 | 1.0741 | 2000 | 0.9225 | |
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| 0.762 | 1.1009 | 2050 | 0.9213 | |
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| 0.8621 | 1.1278 | 2100 | 0.9210 | |
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| 0.9048 | 1.1546 | 2150 | 0.9200 | |
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| 0.7952 | 1.1815 | 2200 | 0.9192 | |
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| 0.9558 | 1.2083 | 2250 | 0.9186 | |
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| 1.0422 | 1.2352 | 2300 | 0.9190 | |
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| 0.7353 | 1.2620 | 2350 | 0.9167 | |
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| 0.924 | 1.2888 | 2400 | 0.9169 | |
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| 0.8895 | 1.3157 | 2450 | 0.9156 | |
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| 0.9581 | 1.3425 | 2500 | 0.9149 | |
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| 0.7616 | 1.3694 | 2550 | 0.9148 | |
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| 0.77 | 1.3962 | 2600 | 0.9143 | |
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| 0.8474 | 1.4231 | 2650 | 0.9134 | |
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| 0.8242 | 1.4499 | 2700 | 0.9133 | |
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| 0.8491 | 1.4768 | 2750 | 0.9131 | |
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| 0.8286 | 1.5036 | 2800 | 0.9119 | |
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| 0.7373 | 1.5305 | 2850 | 0.9116 | |
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| 1.1709 | 1.5573 | 2900 | 0.9109 | |
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| 0.918 | 1.5841 | 2950 | 0.9100 | |
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| 0.8682 | 1.6110 | 3000 | 0.9104 | |
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| 0.7289 | 1.6378 | 3050 | 0.9098 | |
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| 0.9615 | 1.6647 | 3100 | 0.9098 | |
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| 0.9054 | 1.6915 | 3150 | 0.9101 | |
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| 0.9033 | 1.7184 | 3200 | 0.9094 | |
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| 0.8673 | 1.7452 | 3250 | 0.9095 | |
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| 1.0133 | 1.7721 | 3300 | 0.9078 | |
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| 0.8208 | 1.7989 | 3350 | 0.9075 | |
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| 0.8854 | 1.8257 | 3400 | 0.9072 | |
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| 0.81 | 1.8526 | 3450 | 0.9074 | |
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| 0.9013 | 1.8794 | 3500 | 0.9069 | |
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| 0.8539 | 1.9063 | 3550 | 0.9064 | |
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| 0.7346 | 1.9331 | 3600 | 0.9066 | |
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| 0.9698 | 1.9600 | 3650 | 0.9061 | |
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| 0.7256 | 1.9868 | 3700 | 0.9062 | |
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| 0.8813 | 2.0134 | 3750 | 0.9057 | |
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| 1.1117 | 2.0403 | 3800 | 0.9068 | |
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| 0.766 | 2.0671 | 3850 | 0.9062 | |
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| 0.8469 | 2.0940 | 3900 | 0.9066 | |
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| 0.9628 | 2.1208 | 3950 | 0.9063 | |
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| 0.9167 | 2.1476 | 4000 | 0.9062 | |
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| 0.8287 | 2.1745 | 4050 | 0.9058 | |
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| 0.866 | 2.2013 | 4100 | 0.9053 | |
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| 0.9124 | 2.2282 | 4150 | 0.9055 | |
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| 0.722 | 2.2550 | 4200 | 0.9057 | |
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| 0.956 | 2.2819 | 4250 | 0.9056 | |
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| 0.6837 | 2.3087 | 4300 | 0.9050 | |
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| 1.0191 | 2.3356 | 4350 | 0.9045 | |
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| 0.9707 | 2.3624 | 4400 | 0.9050 | |
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| 0.9852 | 2.3892 | 4450 | 0.9054 | |
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| 0.8172 | 2.4161 | 4500 | 0.9050 | |
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| 0.979 | 2.4429 | 4550 | 0.9050 | |
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| 0.9173 | 2.4698 | 4600 | 0.9042 | |
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| 0.8936 | 2.4966 | 4650 | 0.9043 | |
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| 0.6992 | 2.5235 | 4700 | 0.9045 | |
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| 0.79 | 2.5503 | 4750 | 0.9045 | |
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| 0.7661 | 2.5772 | 4800 | 0.9043 | |
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| 0.9067 | 2.6040 | 4850 | 0.9036 | |
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| 0.7251 | 2.6309 | 4900 | 0.9035 | |
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| 0.7873 | 2.6577 | 4950 | 0.9036 | |
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| 0.8441 | 2.6845 | 5000 | 0.9034 | |
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| 0.9242 | 2.7114 | 5050 | 0.9034 | |
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| 0.8931 | 2.7382 | 5100 | 0.9029 | |
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| 1.0847 | 2.7651 | 5150 | 0.9028 | |
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| 0.7797 | 2.7919 | 5200 | 0.9028 | |
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| 0.7537 | 2.8188 | 5250 | 0.9030 | |
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| 0.7131 | 2.8456 | 5300 | 0.9030 | |
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| 0.8321 | 2.8725 | 5350 | 0.9030 | |
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| 0.7554 | 2.8993 | 5400 | 0.9032 | |
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| 0.8003 | 2.9261 | 5450 | 0.9032 | |
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| 0.862 | 2.9530 | 5500 | 0.9034 | |
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| 0.9439 | 2.9798 | 5550 | 0.9031 | |
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| 0.7934 | 3.0064 | 5600 | 0.9030 | |
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| 0.7656 | 3.0333 | 5650 | 0.9030 | |
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| 1.0536 | 3.0601 | 5700 | 0.9033 | |
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| 0.7046 | 3.0870 | 5750 | 0.9032 | |
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| 0.7297 | 3.1138 | 5800 | 0.9028 | |
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| 0.7948 | 3.1407 | 5850 | 0.9028 | |
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| 0.7877 | 3.1675 | 5900 | 0.9030 | |
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| 0.8918 | 3.1944 | 5950 | 0.9028 | |
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| 0.8123 | 3.2212 | 6000 | 0.9030 | |
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| 0.7079 | 3.2480 | 6050 | 0.9029 | |
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| 0.9428 | 3.2749 | 6100 | 0.9030 | |
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| 0.7774 | 3.3017 | 6150 | 0.9030 | |
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| 0.8418 | 3.3286 | 6200 | 0.9033 | |
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| 1.0364 | 3.3554 | 6250 | 0.9032 | |
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| 0.7611 | 3.3823 | 6300 | 0.9031 | |
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| 0.8938 | 3.4091 | 6350 | 0.9030 | |
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| 0.9085 | 3.4360 | 6400 | 0.9030 | |
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| 0.8015 | 3.4628 | 6450 | 0.9030 | |
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| 0.7286 | 3.4896 | 6500 | 0.9030 | |
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| 0.7203 | 3.5165 | 6550 | 0.9030 | |
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| 0.8212 | 3.5433 | 6600 | 0.9030 | |
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| 0.7335 | 3.5702 | 6650 | 0.9028 | |
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| 0.7196 | 3.5970 | 6700 | 0.9029 | |
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| 0.6572 | 3.6239 | 6750 | 0.9030 | |
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| 0.8649 | 3.6507 | 6800 | 0.9029 | |
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| 0.805 | 3.6776 | 6850 | 0.9029 | |
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| 0.8108 | 3.7044 | 6900 | 0.9027 | |
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| 0.8756 | 3.7313 | 6950 | 0.9028 | |
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| 0.895 | 3.7581 | 7000 | 0.9026 | |
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| 0.8497 | 3.7849 | 7050 | 0.9028 | |
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| 0.9445 | 3.8118 | 7100 | 0.9026 | |
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| 0.7153 | 3.8386 | 7150 | 0.9026 | |
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| 0.7897 | 3.8655 | 7200 | 0.9026 | |
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| 0.858 | 3.8923 | 7250 | 0.9027 | |
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| 0.9963 | 3.9192 | 7300 | 0.9028 | |
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| 0.7619 | 3.9460 | 7350 | 0.9027 | |
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| 0.8844 | 3.9729 | 7400 | 0.9028 | |
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| 0.8264 | 3.9997 | 7450 | 0.9028 | |
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| 0.9657 | 4.0263 | 7500 | 0.9026 | |
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| 0.7688 | 4.0532 | 7550 | 0.9028 | |
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| 0.9613 | 4.0800 | 7600 | 0.9027 | |
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| 0.7074 | 4.1068 | 7650 | 0.9025 | |
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| 0.7589 | 4.1337 | 7700 | 0.9028 | |
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| 0.8279 | 4.1605 | 7750 | 0.9028 | |
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| 0.7417 | 4.1874 | 7800 | 0.9027 | |
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| 0.8121 | 4.2142 | 7850 | 0.9026 | |
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| 0.877 | 4.2411 | 7900 | 0.9026 | |
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| 0.7371 | 4.2679 | 7950 | 0.9027 | |
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| 0.8387 | 4.2948 | 8000 | 0.9027 | |
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| 0.8789 | 4.3216 | 8050 | 0.9028 | |
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| 1.0297 | 4.3484 | 8100 | 0.9027 | |
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| 0.7222 | 4.3753 | 8150 | 0.9028 | |
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| 0.8673 | 4.4021 | 8200 | 0.9027 | |
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| 0.7866 | 4.4290 | 8250 | 0.9027 | |
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| 0.7187 | 4.4558 | 8300 | 0.9027 | |
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| 0.8237 | 4.4827 | 8350 | 0.9027 | |
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| 0.8223 | 4.5095 | 8400 | 0.9027 | |
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| 0.8093 | 4.5364 | 8450 | 0.9027 | |
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| 0.815 | 4.5632 | 8500 | 0.9026 | |
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| 0.7278 | 4.5900 | 8550 | 0.9028 | |
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| 0.7515 | 4.6169 | 8600 | 0.9027 | |
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| 0.9041 | 4.6437 | 8650 | 0.9026 | |
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| 0.7683 | 4.6706 | 8700 | 0.9026 | |
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| 0.8538 | 4.6974 | 8750 | 0.9027 | |
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| 0.837 | 4.7243 | 8800 | 0.9027 | |
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| 0.7077 | 4.7511 | 8850 | 0.9027 | |
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| 0.8734 | 4.7780 | 8900 | 0.9027 | |
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| 0.8391 | 4.8048 | 8950 | 0.9027 | |
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| 0.7243 | 4.8316 | 9000 | 0.9028 | |
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| 0.6905 | 4.8585 | 9050 | 0.9026 | |
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| 0.8787 | 4.8853 | 9100 | 0.9026 | |
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| 0.9105 | 4.9122 | 9150 | 0.9026 | |
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| 0.9295 | 4.9390 | 9200 | 0.9025 | |
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| 1.0437 | 4.9659 | 9250 | 0.9026 | |
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| 0.9296 | 4.9927 | 9300 | 0.9026 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
